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Chapter 5: Demand-side Energy Efficiency (EE) 

Introduction 

This chapter provides information on demand-side energy efficiency (EE) as an 

abatement measure for reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric 

generating units (EGUs). Specifically, this chapter addresses EE as a component of both the 

“best system of emission reduction” (BSER) and state goals, and the inclusion of EE within the 

impacts assessment. Support is provided in this chapter for the discussion of the EE abatement 

measure throughout the preamble (most extensively in these sections: Building Blocks for 

Setting State Goals and Considerations, 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 projecting 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 Assessment 
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– Approach 

– Inputs 

– Calculations 

– Results 

 Appendices 

 References 

Background 

As discussed in the State Plan Considerations TSD (Appendix: “Survey of Existing State 

Policies and Programs that Reduce Power Sector CO2 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 savings goals, reduce the 

environmental impacts (including carbon dioxide (CO2) 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 currently in place are considered by states to be 

cost-effective strategies for contributing to these policy objectives1. 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 

billioni. In 2012, the cumulative impacts of these programs represented a 3.7% reduction in 

national electricity demandii. And, EE spending is projected to continue to grow at a substantial 

rate. A recent study by Lawrence Berkeley National Laboratory (LBNL) projects EE program 

spending to reach $8.1 billion to $12.2 billion (“Medium 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 the 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 technologies and practices, and then describes the market 

                                                            

1

 See below for discussion of cost-effectiveness and related cost tests used by states to evaluate EE programs. 2 Specifically, the LBNL study states: “By virtue of limiting the 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, and discuss in more detail the key strategy of 

employing EE programs. 

EE Technologies and Practices 

Energy efficiency is using less energy to provide the same or greater level of service. 

Demand-side energy efficiency refers to an extensive array of technologies, practices and 

measures that are applied throughout all sectors of the economy to reduce energy demand while 

providing the same, and sometimes better, level and quality of service. Utilities’ employ a large 

array of strategies in implementing energy efficiency programs, these include financial 

incentives such as rebates and loans, technical services such as audits and retrofits, and 

educational campaigns about the benefits of energy 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 undertaking home energy audits leading to customized whole home 

retrofits. Opportunities for cost-effective EE in commercial buildings include optimization of 

heating, ventilation, and air conditioning (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 instrumentation and controls systems. 

The opportunity presented for economic investment in EE is dynamic, growing over time 

as technologies and practices advance, as populations grow, and as investment occurs in the 

construction of new homes, buildings, and industrial facilities. As new 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, states are finding renewed opportunities as they 

develop more sophisticated and effective strategies, evolving from a focus on individual enduses and products to whole-building and systems-based strategies that account for the 

interactions between the many energy end-uses in buildings and industryiv. As will be discussed, 

the experience in the U.S. has been that on balance, a persistent and large potential for 
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achievable and cost-effective EE has remained even as the impact of past and ongoing efforts 

have accumulated. 

Barriers to EE Investment 

Despite the persistent and large potential for electricity 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 market failures that lead to inefficiencies in 

energy use are well recognized by analysts and practitioners, 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 CO2, SO2, and NOx that cause human health and environmental damages. 

Energy prices that do not correctly reflect these 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 energy efficient products or practices 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 within 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 efficient products or practices. 

 Under-provision of research and development (R&D). Because of the public good nature of 

knowledge, technology innovation invested by one firm likely spills over to other firms. As a 

result, firms involved in technology development may be less willing to invest in R&D, 

leading to sub-optimal levels of EE investments from a social perspective.vii 
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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 start-up costs and the existence of patents may 

create barriers to entry in markets and result in oligopolistic 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 psychology have identified potential 

behavioral phenomena that lead to consumers to deviate from the standard theory of welfare 

maximizing in consumption and other decisions, including energy efficiency investments. 

Behavioral economics posits possible explanations, including bounded rationality, heuristic 

decision-making, and non-standard 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 discussed above explain why individuals do not always make 

energy efficiency investments that are seemingly in their own best interest to reduce their total 

expenditure, given prevailing electricity prices. 

In addition to market failures and behavioral impediments, other factors, such as hidden 

costs, risk and uncertainty experienced by both consumers and suppliers of energy efficient 

products, and heterogeneity among consumers, producers and markets, also influence EE 

investment decisions3

. 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 phenomenon, also termed ‘energy 

paradox,’ or ‘energy efficiency gap,’ has been studied extensively 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 Characteristics 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. 
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and future energy prices, and the irreversibility of the investment. 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 consumer preferences for energy 

efficiency. In the presence of risk and uncertainties, 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 products at a disadvantage relative to less-efficient products with 

lower upfront costs. 

 Capital market barriers. Consumers sometimes face higher interest rates to finance EE 

investments compared to other investments. Lenders can be reluctant to invest in EE loan 

portfolios in part because energy efficiency loans may lack standardization 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 adoption of socially beneficial energy efficiency 

opportunities. Examples of effective EE policies and programs include public funding of R&D, 

information programs (such as energy labeling, the voluntary ENERGY STAR Program, and 

consumer education), rebates for high-efficiency products, product energy performance 

standards, financing and loan programs, and technical assistance. 

EE Policies4

Objectives and Role in Reducing CO2 Emissions from the Power Sector 

EE policies are implemented by states to meet a number of closely related policy goalsix

including: 

- Reducing costs to electricity customers 

- Providing a significant resource for meeting power system capacity needs 

- Meeting energy savings goals 

- Stimulating local economic development and new jobs 

                                                            

4

 Existing state EE policies are described extensively in the State Plan Considerations TSD. 
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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-effective strategies for 

contributing to each of these policy objectivesx. While each of these objectives, and others, 

contribute to the motivation of state policymakers to pursue EE policies, reducing energy costs 

over the long term is the leading objective in pursuing these policies. In addition, EE policies are 

central to meeting state objectives for reducing CO2 emissions from the power sector. As noted 

in the State Plan Considerations TSD, EE policies are a leading tool for achieving CO2

reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in 

ten states5 with statutory requirements for greenhouse gas reductions. 

Economy-wide studies of climate mitigation scenarios confirm that energy efficiency plays a 

critical role in reducing the costs and enhancing the flexibility of meeting long-term climate 

stabilization targetsxi. Analysis by the International Energy Agency (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 

economyxii. 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 efficiency in various economic sectors would slow 

the increases of GHG emissions in the short 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 goalsxiii,xiv. 

Several economic studies (including 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 studies 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.  
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addition, EE policies are recognized to be an appropriate 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 opportunities for reducing GHG emissions, energy 

efficiency is recognized to provide other co-benefits, including air quality and public health 

benefits, waste reduction from energy generation, energy security, energy system reliability, 

community economic and social development, and consumer amenities.xvii Energy efficiency 

investments and policies are also found to spur productivity growth, technology learning and 

innovation.xviiixix Recently, more attention has been paid 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 programsxx. These co-benefits have not been fully 

accounted for in the EPA analysis. 

Policy Types 

EE policies come in many forms. The most prominent and impactful EE policies in most 

states include those that drive development and funding of EE programs8, and building energy 

codes. Other policies that are leading to significant impacts 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 the relative impact (achieved or 

potential) of the different policy types is challenging, particularly to do so comprehensively, 

across all states, and at the national level. EE programs are the only state EE approach that has 

comprehensive and detailed reporting of impacts, costs, and other characteristics from all 50 

states9

. This information is generally based upon measurement and verification studies submitted 

annually, most commonly to state utility commissions, 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 breadth and quality of information collected through Form 

EIA-861 has improved over time, however, outside entities (e.g., ACEEE) have found 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. 
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government agencies). EE program data reported to EIA includes incremental and cumulative 

energy and peak demand savings, program costs broken down by component, and composition 

by end-use sector (residential, 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 comprehensive dataset reported by states of the 

achieved impacts of strategies other than those that lead to investment in EE programs. However, 

state and regional-level information does exist. 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 energy 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 codes accounting for the remaining savingsxxi. 

Another representation of the relative opportunity provided by different state EE 

strategies is presented by evaluations 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 ACEEExxii and Georgia 

Techxxiii studies, respectively. These studies indicate that the substantial majority of potential 

savings from state EE efforts are available through EE programs, and that state and local 

building energy codes can make a significant additional contribution. Massachusetts provides a 

state example of the impacts of EE programs relative to other state EE policies. The 

Massachusetts Global Warming Solutions Act 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 contribution to meeting the 25 percent target with 

utility sponsored EE programs and state building energy codes accounting for 76% and 17%, 

respectively, of those policiesxxiv. In their 2013 progress report, Massachusetts indicates that they 

are generally on track for meeting or exceeding these projections.xxv

 
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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 verification (M&V), penalties for non-compliance, and 

implementation status) in the State Plan Considerations TSD. Because EE programs have 

provided the majority of state EE-policy electricity savings to-date and offer the majority of 

potential savings going forward, we next summarize 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 sectors of electricity end-users. Figure 5-1xxvi 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 detailed 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 participant costs, investment life, scale, M&V 

approach, etc.10

 

                                                            

10 See following sections for discussion of these factors. 
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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 ownership types (investor-owned, municipals, and cooperatives), nonprofit and for-profit third-parties (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 savingsxxvii) are overseen by state utility 

commissions, which review and approve program plans, projected impacts, and associated 

budgets; and establish annual reporting and M&V requirements. 

 
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Policy Drivers 

EE programs result from a number of different policy approaches or “drivers.”11 These 

include energy efficiency resource standards (EERS) (26 states)xxviii, system benefit charges (14 

states), integrated resource planning (IRP) requirements (34 states), demand-side management 

plan or multi-year energy efficiency budget (28 states), 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 their clear goals and proven 

success as a policy toolxxx. These policy drivers lead to the evaluation, planning, and adoption of 

EE programs and associated budgets, which 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 surcharges, typically ranging from $1 to $4 per 

megawatt-hourxxxi. More recently adopted funding sources include proceeds from the auction of 

allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid 

into the forward capacity market operated by the New England Independent System Operator 

(NE-ISO). Ratepayer-funding accounts 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 historically 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, and 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 with EE incorporated as an 

eligible resource in a renewable portfolio standard. The numbers for the other policy approaches are from LBNL 

(see endnote). 
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of existing EE programs and trends, and the electricity savings potential achievable through 

expanded 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 rapidly in recent years driven by recent policy 

innovations and increasing evidence of the effectiveness of these new strategies. Table 5-2 

presents levels of EE program funding in the U.S. since 2006xxxii. 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 have helped to drive these rapid increases in EE program 

funding include EERS, electricity savings goals, and “all cost-effective energy efficiency” 

requirements. The adoption of EERS, in particular, increased through this period and clearly has 

been the primary driving force behind the increasing success of and investment in EE programs. 

Table 5-3 shows the number of states adopting EERS by yearxxxiii. 

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 2 
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2007 Colorado, Connecticut, Illinois, Minnesota, North Carolina 5 

2008 Maryland, Michigan, New Mexico, 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 information (including costs and impacts) are 

annual EE program reports required by utility commissions, or cooperative or municipal utility 

boards of directors. These reports are based on M&V studies of individual EE programs within 

the program portfolio. The Energy Information Administration (EIA) has been 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 programs 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 data, supplemented it with additional sources 

including surveys of utility commissions and program administrators, and published their own 

annual reports that capture EE program impactsxxxivxxxv. 

The EPA has relied on the EIA dataset for identifying historic impacts of EE programs by 

state. EIA data is reported by program administrator (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 utilities). 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 savings13 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. 
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of retail sales with individual state values ranging from 0.00% to 2.19%. Cumulative electricity 

savings14 (representing the remaining impacts of programs from all prior years) reported at the 

national level for 2012 represent 3.74% of retail sales with 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, taking into account the lifetimes of installed measures. 
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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% 
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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 Laboratory (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 gas utility customers in the United States under three scenarios 

(high, medium, and low cases)xxxvi. The scenarios represent “a range 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 standard, clean energy standard, or carbon policy” and 

specifies that such policy changes could “result in customer-funded energy efficiency program 

spending and savings that exceed the values in our High Case.” 

The study concludes that efficiency programs are “poised for dramatic growth over the 

course of the next 10 to 15 years” with the most significant increases occurring in regions with 

lower levels of program spending, historically, including the Midwest and 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 the high scenario, spending more than doubles 

from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensurately, to 

0.8% and 1.1% of sales under the medium and high scenarios, respectively. The study results 

indicate that under the high scenario 20 states would be achieving 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.
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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 common tool for informing the development of EE 

program plans and budgets, as well as supporting the development of electricity savings targets, 

required savings levels under an EERS, or “all cost-effective” EE requirement. In conducting 

these studies, states and utilities have developed a standardized methodology that is often 

described as a “bottom-up, engineering-based” approachxxxvii. EE potential studies are conducted 

at various geographic scopes (national, regional, state, and utility service territory level) and at 

different degrees of aggregation (e.g., economy-wide, sectoral, and program), and can be broadly 

grouped into a few types: technical, 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 potential” 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). 
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Some estimate economic potential by evaluating technology upfront cost, operating costs 

that considers energy prices, product lifetime and discount rate, compared to a 

conventional alternative or the supply-side energy resources. Others incorporate 

consideration of consumer preferences in addition to consumers’ out-of-pocket 

expenditure when evaluating the economic potential. Both technical and economic 

potential estimates assume immediate implementation of efficiency measures without 

regard to technology adoption process or real-life program implementation. In addition, 

these estimates do not always reflect market failures or barriers that impede energy 

efficiency and often fail to capture transaction costs (e.g., administration, 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 amount of energy savings that can realistically be achieved, 

taking into account factors such as technology adoption process, market failures or 

barriers that inhibit technology adoption, transaction costs, consumer 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 potential that can be realized given 

specific program funding levels and designs. Program potential studies can consider 

scenarios ranging from a single program to a full portfolio of programs17. 

As mentioned, the EE industry standard for potential studies is the bottom-up, 

engineering evaluation of energy efficiency potential of individual end-use technologies and 

measuresxxxviii. Bottom-up analyses all employ a similar methodology but can vary significantly 

in key assumptions (e.g., breadth of sectors and end-uses considered, study period, discount rate, 

pattern of technology penetration, whether economically justified early replacement of 

technologies is allowed for, and whether continued improvement in efficiency of technology is 

provided for). As a result, estimated efficiency potential can vary significantly among studies18. 

                                                            

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 economic 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 consumer
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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, state, and utility levels. This section reviews recent studies and 

presents a summary of findings. We first address 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 study periods, we present 

average annual achievable potential by dividing cumulative percentage savings in the last year of 

the study by the duration (in years) of the study period. This is a common method of 

normalization for energy efficiency 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 potential, 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% per year in economic potential, and 1.5% per year in achievable 

potential. To supplement these studies with more recent data, the EPA has conducted a metaanalysis of twelve studies conducted between 2010 and 2014 at the utility, state or regional level 

(see Appendix 5-1). The EPA review indicates an average annual achievable potential of 1.5% 

per year across the reviewed studies. See Appendix 5-2 (Summary of Recent (2010-2014) 

Electric Energy Efficiency Potential Studies) for complete results from the EPA research. 

 

                                                                                                                                                                                               

technology adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect 

estimated potential. 
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Table 5-6 presents a summary of these 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) evaluated four major energy efficiency 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 other 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 electricity savings potential in the 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 potential. On a normalized basis, the EPRI 2009 study 

provides an achievable annualized potential range of 0.2-0.4% per year (realistically achievable 

and maximum achievable potential, respectively) through 2030 at the national level. 

Two more recent studies also provide national 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 are summarized in Table 5-7. 
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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 studies 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 budgets 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 similar scale opportunity across all regions. For 

instance, EPRI (2014) shows achievable 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 industrial sectors in every region. The ACEEE (2014) study also 

shows consistently large potential across all states 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 policy objectives including reduction of customer 

electricity bills, lower costs of meeting electricity supply needs, energy reduction, environment 

and health benefits, and local economic development benefitsxliii. Most states evaluate their EE 

policy options through the application of cost tests, weighing the projected benefits with the 
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costs of the energy efficiency technologies and practicesxliv xlv. Each state determines their own 

policies for the specific costs and benefits to include 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 expenditures (e.g., a whole home retrofit) while the 

economic benefits (e.g., electricity bill savings) accrue 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 categories depending upon the 

economic perspective of interest (e.g., utility, ratepayer, program participant, society), and 

consider the results from each one, usually with an identified primary test typexlvi. Policies that 

are selected are those that are found to be cost-effective, with benefits greater than costs, as 

determined by the utility applying methods defined by their state 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 installation costs; benefits include incentive payments, 

bill savings, and applicable tax credits or incentives. 

(2) Utility/program administrator cost test from the perspective of utility, government 

agency or third-party implementing the program. Costs may include program incentive, 

installation, and overhead costs; benefits may include avoided energy and capacity costs - 

including generation, transmission and 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 well 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 utility 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 capacity costs, and additional resource savings. 

(5) Societal cost test from the social perspective. In addition to benefits considered in total 

resource cost test, may also include non-monetized benefits such as environmental and health 

benefits. 
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While many states consider more than one cost test in evaluating EE programs, the most 

commonly used (29 states) primary test is the total resources cost test. This test is considered to 

be the best measure of the interests of all utility customers. The utility 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 climate change policy suggests that energy 

efficiency presents a large potential in reducing greenhouse gas emissions and plays a critical 

role in offsetting the costs and enhancing the flexibility to achieve long-term GHG reduction 

targetsxlvii. Consistently, evaluations of the economic potential for carbon dioxide reductions 

from the United States’ power sector identify demand-side energy efficiency as the lowest cost 

strategy (typically, as noted above, with positive net present value) as well as the strategy having 

the greatest reduction potentialxlviii. 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 positive net present value if “persistent barriers to 

market efficiency” could be addressed. 

Costs of Saved Energy 

A common metric for comparing alternative electricity resource options within utility 

resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of 

saved energy (LCSE)l. LCSE EE is often compared favorably with LCOE of alternative new 

generation sources such as fossil-fueled or nuclear power plants, or renewable energy resources 

like wind or solar-power generation. 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 states between 2009 and 2012, and estimated LCSE 

for electricity energy efficiency programs in the range of 1.3-5.6 cents/kWh, with a mean value 

of 2.8 cents/kWh.li Earlier reviews of utility 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 measure lives, and other assumptions affecting the calculation of 

LCSE were not consistent or not reported in all studies. 
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cost-effectiveness analysis results in nine states and suggested that reported utility LCSE ranged 

between 2.3-4.4 cents/kWh, 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-down modeling). Some recent analyses suggest 

estimated LCSE in a similar range as in the energy efficiency analysis literature. 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 savings potential of EE investments using stateand region-specific price elasticity, Paul et al. (2011) estimated that electricity savings were 

available at an average utility LCSE of 2.5-3.5 cents/kWh.lv In an econometric analysis of utility 

rate-payer funded demand-side management and energy efficiency programs between 1992 and 

2006, Arimura et al. (2009) found that the estimated 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 considerations related to LCSE estimation are also 

discussed in the literature, including the issue of “free riders” in EE programslvii, and the 

accuracy of utility reported costs and energy savingslviii. Energy efficiency practitioners also 

recognize the need to consider “free rider” and “spillover” effects in program evaluation. A 

slight majority of states adjust for free ridership in energy savings estimates, 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 reductions in utilization of affected EGUs. 

Moreover, this measure has been adopted by nearly every state and most utilities across the 

country, typically through multiple policy approaches. Increased use of, and impacts from, state 

energy efficiency policies is a leading industry 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 energy efficiency as an abatement measure for 

reducing carbon dioxide (CO2) emissions from fossil fuel-fired electric generating units (EGUs). 

In the next section, we address the setting of state-specific goals for electricity savings levels 

resulting from state demand-side energy efficiency efforts. In the final section, the integration of 
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these goals into the impacts assessment is presented 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 CO2 reductions at affected EGUs that could be achieved 

through implementation of demand-side energy efficiency 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 technologies and practices, and projects the annual 

impacts of the scenario for each state. It does not 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 CO2 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 has already been demonstrated or 

required by policies (e.g., energy efficiency 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 are reported by utility or other program 

administrator, by state, and compiled nationally, using standardized elements and 

definitions, and 

 EE program savings are projected and evaluated under requirements established and 

overseen by state utility commissions, and by municipal and cooperative utility boards of 

directors. 
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While the approach is derived from information about energy efficiency programs 

overseen by state utility commissions, other state energy efficiency policies are available to 

realize a state’s goals20 such as building energy codes, appliance standards, and building energy 

benchmarking requirements. All policies included in a state plan will need to meet established 

requirements or guidance for EM&V21. 

The following steps were taken to establish 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 year level of performance 

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

performance 

 Step 6: Determine average portfolio measure life and distribution 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 resource standards and is readily calculated from EIA Form 861 data 

for each state. Incremental annual savings are also more directly estimated and evaluated than 

are cumulative savings22. For the best practices scenario, we aggregated the most recent year of 

EIA Form 861 data to the state level to establish each state’s current level of performance. These 

results were presented previously 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. 
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Step 2: Determine Best Practices Level of Performance 

As discussed previously, achievable demand-side 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 regional characteristics (e.g., avoided 

power system costs, economic growth, sectoral mix, climate, and level of past energy efficiency 

efforts) affect estimates of achievable potential, ongoing improvements in energy-efficient 

technologies and practices, economic growth, population increases, and continually improving 

strategies for program delivery have resulted in persistent and substantial levels of achievable 

potential regardless of specific regional characteristics. 

A direct indicator of the achievable incremental levels of energy savings performance is 

provided by past performance at the state and utility levels, and by requirements states have put 

in place for levels of savings to be achieved by 2020. As discussed, these requirements are 

typically in the form of energy efficiency resource standards or similar savings goals that are 

applied to utilities in the state23. 

Table 5-8 summarizes incremental savings levels as a percentage of retail sales from EIA 

Form 861 (2012) datalx, aggregated to the state level, and categorized into four ranges of savings 

levels (> 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved 

the highest level of performance (> 1.5%) and an additional eight states achieved the second 

highest level of performance (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. 
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Table 5-9 summarizes incremental savings levels required by state policy on or before 

2020 and categorized into the same four rangeslxi. Eleven states are required to achieve the 

highest level of performance (> 1.5%) and an additional five states are required to achieve the 

next highest level of performance (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 level of performance for Option 124, the EPA has chosen 1.5% 

incremental savings as a percentage of retail sales. This level was achieved by three states (AZ, 

ME, and VT) in 2012 and an additional nine states (CO, IL, IN, MA, MN, NY, OH, RI, and 

WA), accounting for overlap, are expected to achieve this level by 2020. Thus, twelve states 

have either achieved or are required to achieve this level of performance by 2020. 

For Option 225, the EPA has chosen 1.0% incremental savings as a percentage of retail 

sales as the best practices level of performance for this alternate approach. 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 the 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 scenario, the EPA has used 2017, the year following 

the required state plan submittal26, 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 descriptions of the schedule for state plan submittals. 
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Step 4: Determine Start Year Level of Performance 

For construction of the best practices scenario, the EPA has set each state’s level of 

performance (incremental savings) in the start year (2017) to its current level of performance 

(aggregated to the state-level from 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 benefit a state in meeting its state EE goals for 

the 2020-2029 interim compliance period27. 

Step 5: Determine Pace at Which States Improve from Start Year to Best Practices Level of 

Performance 

To determine a trajectory of incremental savings levels from the 2017 level to the best 

practices level, the EPA considered past performance of individual program administrators28 as 

well as requirements of existing state energy efficiency 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. The moderate group (47 entities) was defined as 

programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high 

group (26 entities) was defined as programs that achieved greater than 1.5% maximum 

incremental savings levels. We then calculated the rate at which each entity had increased 

savings over time and calculated average values for each sub-group. For the moderate group, the 

average rate of improvement of incremental annual savings rate was 0.30% per year. For the 

high group, the average rate of improvement of incremental annual savings rate was 0.38% per 

year. See Appendix 5-3 for supporting data and analysis. 

 The EPA also considered requirements of existing 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 states 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 collect data from third-party program administrators. 
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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 Measure Life and Distribution 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 represent the total impacts of all EE 

measures put in place in that year and all prior years, due to EE program activities. The 

cumulative savings account for the continuing impacts of energy efficiency measures that remain 

in place for a period of time (the “measure life”) before being replaced. For example, the 

purchase of a high-efficiency refrigerator may lead to savings for twelve years, before being 

replaced with a new model. To estimate cumulative impacts of a series of years of incremental 

savings, the industry uses the concept of an average measure life for the entire portfolio of EE 

programs. Rather than use a single, average measure 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 years (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 levelslxii. 

 In 2014, ACEEE updated their 2004 and 2009 national 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. 

 
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TABLE 5-10 

Average Electricity Measure lifetimes by state and customer class 

Sector 

Residential Commercial/Business Industrial All Sectors 

Average 8.1 12.5 9.5 10.6 

Other studies have found slightly higher values for average measure life for EE portfolios, 

ranging from 10 to 13 yearslxiii. 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 lives across an EE portfolio, consistent with an 

average measure life of ten years, we have assumed an even distribution from one year in length 

to two times the average measure life (twenty years) in length. Our approach is generally 

supported by the substantial range in measure lives reviewed and summarized in a 2014 study by 

LBNL which shows an interquartile range from five to 25 years across twelve program 

categories (e.g., low income, residential new construction, commercial/industrial custom, etc.). 

Our approach represents a first-order approximation of the distribution 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 measure 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 conservative one, leading to the same quantity of 

total energy savings, but with a greater portion of the savings occurring in later years than occurs 

with the more common, and simpler, approach. This results in lower cumulative impacts in 

earlier years and correspondingly lower state goals through 2030. 

 

Step 7: Determine Sustainability of Best Practices Level of Performance 

For construction of the best practices scenario, once a state achieves the best practices 

level of performance, the EPA has kept the level of performance constant through 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 the best practices level of 
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performance, this reflects an ability to sustain the target level for thirteen years (2017 through 

2030). 

 Limited empirical data suggests the reasonableness of this approach; however, 

comprehensive data, across all regions and states, does not exist because these levels of 

performance have not been achieved and sustained nationwide previously. The Northwest Power 

Conservation Council (NPCC) provides one such example. NPCC has been conducting the most 

consistent and long-running series of evaluations of achievable cost-effective potential in the 

country, updated every five years, as part of their five-state29 regional energy resource planslxiv. 

These analyses have become more detailed, reliable, and purposeful over time. Since 1998, 

NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity 

savings from energy efficiency programs have increased rapidly, more than quadrupling between 

1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and 

exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our 

research shows that when programs invest in higher levels of efficiency, this helps drive 

measurement improvements and technical innovation, resulting in larger and more reliable 

conservation supply estimates.”lxv Table 5-11 summarizes the NPCC’s achievable potential 

estimates and evaluated savings since 1998lxvi. 

TABLE 5-11 

NPCC Achievable EE Potential and Achieved Incremental Savings (1998-2010) 

Year 

1998 2005 2010 

Achievable Potential over 20 Years 

(GWh) 

13,447 24,651 51,684 

Achieved Incremental Savings from 

EE Programs (GWh) 

547 1,184 2,248 

                                                            

29 NPCC’s resource plans cover Idaho, Oregon, and Washington in their entirety, and western regions of Montana 

and Wyoming. 
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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 with 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 practices scenarios for Options 1 

and 2. The pace of improvements, average measure life, and distribution of measure lives are 

each conservative and, therefore, contribute lower state goals than would otherwise result. 

Similarly, the best practices level of performance, being based solely on results from and 

requirements of EE programs, is less stringent than a level would be that accounted for potential 

impacts of other state EE policies such as building energy codes, building 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 year 
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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 for determining cumulative savings levels 

(cumulative savings as a percentage of baseline sales) for each state, 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-side EE component is used as one of several inputs to the 

calculation of interim and final state emission rate goals. 

Calculating the net cumulative savings as a percent of electricity sales 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 savings 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 example) for 2017 through 2025 for Option 1. We 

truncate the results at 2025 for simplicity, but full results are presented for all states in the 

section. 
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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 corresponding to the state. For South Carolina 

the corresponding region is SERC and the average annual growth rate from 2012 to 2040 

is 1.10% per year. The resulting values are summarized 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 incremental 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 increased by the pace of improvement of 0.2% per 

year (for Option 1) until the goal level of 1.50% (for Option 1) is reached and then held 

constant. The resulting values are summarized 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 EE
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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 net 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 (first-year) savings based upon an average measure 

life of 10 years and a linear decline in first-year savings over twenty years. As an 

example, Figure 2 illustrates the decline in first-year savings from EE measures installed 

in 2017. The resulting values for expiring EE savings are summarized in Table 5-16 for 

South Carolina. 
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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. 

 
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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. The 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. 
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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% 
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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 are represented as cumulative savings as a 

percentage of retail sales by year for each option. 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. 
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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%
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 44 

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%
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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 Rates 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 average annual savings rate represented in the 

EE best practices scenario are below the range of values found in recent utility, state, and 

regional studies (1.2% to 1.5% per year) as summarized in Table 5-6, and 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 associated state-specific EE goals. 

Impacts Assessment 

Approach 

In the Goal Computation TSD, state-specific 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 as 
DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014 

 46 

requirements within the power sector modeling for 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 modeling for the illustrative 

compliance scenarios. In other words, the degree to which EE is employed as an abatement 

resource is not determined endogenously within the power sector modeling based upon 

optimization of costs but, rather, “hard wired” into the illustrative compliance scenarios. This 

approach 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 implementation, specifically, the current levels of EE 

performance and the pace at which states can feasibly improve their levels of performance over 

time. 

The EE goals represented in the illustrative compliance scenarios lead to substantial 

reductions in power system costs due to the reductions in specified electricity demand. Since EE 

is not represented endogenously as an abatement measure within the power sector modeling, the 

costs associated with the EE 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 addressed in the following sections. 

Inputs 

The following steps were taken to establish the inputs for development of the EE cost 

estimates for each state. 

 Step 1: Determine state-specific 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, state-specific annual incremental savings (MWh) and 

yearly distribution of associated continuing savings (MWh) in future years are used as inputs to 

the cost estimation calculations. 
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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.) incurred by a utility or other administrator of EE 

programs in a given year that lead to EE measures (technologies and practices) put in place in 

that year and resulting in reductions 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, ACEEE conducted a national 

review of data on EE program costs from program annual reports, evaluation reports, and 

information compiled from contacts at program administers in 14 states. 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 net30 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 administrators 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 comprehensive study of EE program costs. The LBNL analysis reviewed 

program-level data from over 100 program administrators in 31 states. 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 their study and the lack of 

net savings information for many programs, LBNL focused on gross31, 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 deflating 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 conservative, 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” refers to electricity savings before any accounting for effects of free-ridership or spillover. 
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Step 3: Determine the Ratio of Program to Participant Costs

 As noted above, while program costs are readily known and consistently reported by the 

program administrator, participant costs require significant effort to estimate, and are less 

consistently estimated and reported. The ratio between program and participant costs will vary 

significantly from one program to the next within a utility’s portfolio. 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 once program costs are determined. To derive the 

ratio, the EPA reviewed 2012 EE annual reports from program administrators in 22 states 

identified as leaders in EE programs32 based upon their magnitude of savings or their savings as 

a percentage of retail sales. Complete information on full portfolio participant costs were 

available for nine of the 22 states. Across the nine 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 this review. Based on this review, the EPA has taken a slightly 

conservative approach33 and used a ratio of 1:1 between program and participant costs. We use 

this ratio to derive participant and total costs based upon program costs. Starting from program 

CSE of $275/MWh and applying the 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 represented in the state EE goals are substantial and 

represent a scenario that has not previously been achieved and sustained at a national level in the 

U.S. Thus, even though the EPA has taken a conservative approach (i.e., leading to higher 

estimates of costs) to the development 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 their historic levels of 

incremental savings to the best practices level of 1.5% of retails sales). 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 ACEEE 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. 
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achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE 

programs, and learning and expertise gained over time from the continued implementation of 

programs, are two factors that would lower costs as programs scale up and expand to realize 

higher levels of performance. 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 limited empirical data does provide support for 

significant economies of scale and/or cost reductions over time as learning and expertise are 

gainedlxvii. “Supply curves” of EE as an energy resource, as well as EE as a measure represented 

within a GHG abatement curve, provide support for escalating costs as higher levels of savings 

are realizedlxviii. In a recent analysis, Lawrence Berkeley National Laboratory (LBNL) adopted 

an approach that generically represented both effects discussed abovelxix. LBNL changed EE 

costs (first-year program costs) as a function of EE savings levels, decreasing costs at savings 

levels up to 0.5%, leaving costs constant at the base level at savings 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 ratelxx. 

In consideration of the above discussion, the EPA has chosen to escalate EE costs over 

three steps as a function of incremental savings (as a percentage of electricity sales) at the state 

level. Until a state reaches a 0.5% savings level, their costs are set at the base level; for savings 

levels between 0.5% and 1.0%, state costs are escalated to 120% of the base level; and for 

savings levels over 1.0%, state costs are escalated to 140% of the base 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 analysis including first-year 

costs of saved energy, ratio of program to participant 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. 
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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 estimating the costs associated with the statespecific EE goals discussed above. The results of these calculations are then used within the RIA 

and preamble. Specifically, three values are calculated (annual first-year costs, levelized cost of 

saved energy (LCSE), and annualized costs); for each, program and participant components are 

then calculated using the 1:1 ratio (i.e., 50% of total for each) derived above. Specific results 

from prior sections on state goal setting and impacts 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 example) for 2017 through 2025 for Option 1. The 

results are truncated at 2025 for simplicity, but full national results (through 2030) are 

presented below. 
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Step 1: Calculate Annual First-Year Costs

Annual total first-year costs are calculated by multiplying annual total incremental 

savings (MWh) (from Table 5-15) by the first-year total CSE (from Table 5-23 with 

escalation based upon results from Table 5-14). Program and participant 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-Year 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$) 

75.3 145.3 200.7 256.3 363.9 428.5 481.2 480.8 480.9 
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 53 

Step 2: Calculate Levelized Cost of Saved Energy

Levelized costs of saved energy (LCSE) are based on levelization 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 Manuallxxi. The net present 

value of all savings from a single year’s EE activities (i.e., over the entire distribution of 

program lifetimes) is calculated using the real discount rate. The levelized cost of saved 

energy is then calculated by dividing the annual first-year costs (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 multiplying the LCSE for each year by the 

remaining savings in each year through the full distribution of measure lifetimes. For 
DRAFT DELIBERATIVE – DO NOT QUOTE OR CITE April 10, 2014 

 54 

each year in the analysis, the annualized costs resulting from all current and past 

investments are summed to calculate the total 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. 
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 55 

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 Savings 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 96.0 151.4 229.1 317.6 413.2 502.7 586.2 

 
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 56 

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 summarizes national LCSE for Option 1 

for the same years. Each of the three tables includes values for 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 90 

 
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 57 

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 and 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 costs. Historic data is from Table 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% 

 
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 58 

Appendices 

Appendix 5-1: Summary of Recent (2010-2014) Electric 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: State Goal Setting and Impacts Assessment 

 
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 59 

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 (2010-2014) for states, utilities, and other agencies 

across the U.S. Study periods ranged from five to twenty-one years in length. As Table 1 shows, 

across the eleven studies that reported 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% 
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 60 

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 River 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 

 61 

Delaware: Optimal Energy, Inc. 2013. Delaware Economic Energy Efficiency Potential. 

Prepared for the Delaware Department of Natural Resources and Environmental Control, May 

24. 

Illinois: ICF International. 2013. ComEd Energy Efficiency Potential Study Report, 2013-2018. 

August 20. 

Michigan: GDS Associates, Inc. 2013. Michigan Electric and Natural Gas Energy Efficiency 

Potential Study – Final Report. Prepared for the Michigan Public Service Commission, 

November 1. 

New Jersey: EnerNOC Utility Solutions Consulting. 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 Efficiency Potential Study for the State of 

New Mexico, Volume 2: Electric Energy 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 Potential Report. March. 

Pacific Northwest states (combined): Barbose, Galen, and Alan Sanstad, Charles Goldman, 

Stuart McMenamin, Andy Sukenik. 2014. Incorporating Energy Efficiency into Western 

Interconnection Transmission Planning. Draft report, 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. 
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 62 

Tennessee: Global Energy Partners. 2011. Tennessee Valley Authority Potential Study, Final 

Report, Volume 1: Executive Summary. Report Number 1360, December 21.
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 63 

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 two different perspectives: 1) historical data reflecting achieved 

savings of EE programs and 2) requirements of existing state energy efficiency resource 

standards (EERS). For the historical perspective, we reviewed data from the Energy Information 

Administration’s Form EIA-861 on EE program electricity savings (supplemented as needed 

with program administrator reports) and identified the pace at which entities reaching higher 

savings levels have historically increased energy savings over time.34 Specifically, we reviewed 

the historical savings data in the following two groups of energy efficiency program 

administrators. 

1. Top saver 1% - a group with 47 entities that achieved a maximum first-year savings level 

of 0.8% to 1.5%. 

2. Top saver 2% - a group with 26 entities that achieved a maximum first-year savings level 

of 1.5% to 3.0%.35

For the existing state requirements perspective, we reviewed energy savings ramp-up 

schedules established under EERS for states that provide clear ramp-up schedules. According to 

ACEEE’s 2013 State Energy Efficiency Scorecardlxxii, 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 collect third-party administrator data in 2011. The third-party 

entities included in our analysis are Efficiency Vermont, Energy 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 Electric Company (now part of National Grid). 35 In addition to these maximum first-year savings thresholds, 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 provided to evaluate reasonable ramp-up schedules to gain an incremental 1% firstyear savings; and (c) savings data series are continuous between the years for the minimum and maximum savings 

levels.
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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 savings by 0.30% per year on average from their 

minimum to their maximum first-year savings levels (with a range of 0.10% per year to 

0.63% per year).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% per 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%” groups39

 

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 Scorecard, Appendix B, November 2013, 37 This is a simple average estimate of the annual average increase in first-year savings from each entity in this 

group. 

38 This is the simple average estimate of the annual average increase in first-year savings 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. 
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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 electricity savings ramp-up based on existing state 

EERS policies are: 

 The states with EERS policies which exhibit savings ramp-up schedules are requiring 

increases in first-year energy savings at a pace that ranges from 0.11% (Colorado and 

Oregon) to 0.40% (Rhode Island) as shown in Table 2. 

 The first-year savings pace of increase averages 0.21% per year across the 10 states. This 

savings level translates to about 4.7 years to achieve an incremental 1% first-year savings 

increase. 

Table 2. First-Year Energy Savings Ramp-up Review of State EERS Policies40

State 

Minimum 

Target 

Maximum 

Target 

Climb 

Time 

(years) 

Annual 

Average % 

Increase 

Years to 

Achieve 1% 

Min Year Max Year Increase 

 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. 
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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 Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/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 No. 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_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

 
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 67 

Appendix 5-3 

Review of the Ratio of Program 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 administrators in 22 states. Our research findings 

are as follows: 

 A 1:1 ratio between program and participant 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 information (participant costs across entire portfolio 

of EE programs) to inform the analysis. 

 Participant cost data from ten program administrators in nine states indicate that the 

weighted average and simple average participant costs were 47 percent of total costs. 

Participant Cost Analysis 

We first identified states having high incremental electricity savings rates or high 

absolute savings levels based upon 2013 ACEEE State Energy Efficiency Scorecard.41 These 

states represent a large portion of total EE savings in the U.S. We identified 22 states meeting 

these criteria and collected publicly available EE program reports for major program 

administrators within each state. From these program reports we identified 10 program 

administrators across nine states where we were able to identify both program administrator and 

participant costs across their full portfolio of EE programs. The table below provides the 2012 

portfolio-level program administrator and participant 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 the top 15 states in the 2013 

ACEEE State Energy Efficiency Scorecard in terms of incremental savings as a percentage of retail sales or absolute 

annual energy savings in terms of total annual MWh savings. 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. 
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implementation costs, and the participant costs represent 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 costs across all nine states are presented as a 

percentage of total program costs. The weighted average cost shares were based on each 

program’s spending by administrator and participants. 

Table 1 

2012 Participant and Program Cost Information from Reported Entities 

In our analysis, the weighted average program 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 total costs. Participant cost results range from 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%
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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, leading to slightly higher total costs than the precise average values 

would provide. 

References 

MidAmerican Energy Company, "Energy Efficiency Plan Docket No. EEP-08-2, 2012 Annual 

Report to the Iowa Utilities Board," May 1, 2013, available at: 

http://www.state.ia.us/government/com/util/energy/energy_efficiency/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 No. 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_efficiency_vermont/annual_reports/EfficiencyVermont-Annual-Report-2012.pdf 

Burlington Electric Department, "2012 Energy Efficiency Annual Report," 2012, available at: 

https://www.burlingtonelectric.com/ELBO/assets/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.com/information-reports 

Xcel Energy, "2012 Status Report & Associated Compliance Filings Minnesota Electric and 

Natural Gas Conservation Improvement Program Docket No. E,G002/CIP-09-198, " April 1, 

2013, available at 

http://www.xcelenergy.com/staticfiles/xe/Regulatory/Regulatory%20PDFs/MN-DSM-CIP-2012-

Status-Report.pdf 
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National Grid, "2012 Energy Efficiency Year-End Report," May 31, 2013, available at 

http://www.nationalgridus.com/non_html/eer/ri/4295-YearEnd%20Rept%20(PUC%205-31-

13).pdf 

Southern California Edison, "2013 Energy Efficiency Annual Report," June 2013, available at 

http://eega.cpuc.ca.gov/Documents.aspx http://eega.cpuc.ca.gov/Documents.aspx 

 
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Appendix 5-4 

Comprehensive Results: State Goal 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, First-year Costs, and Annualized Costs 

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

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

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

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

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

o Levelized Cost of Saved Energy, First-year Costs, and Annualized Costs 
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 72 

References

                                                            

i

 American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy Efficiency 

Scorecard. Available at http://www.aceee.org/state-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-Funded 

Energy 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-Efficient 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 (2013). “Bridging the Energy Efficiency Gap: insights 

for policy from economic theory and empirical analysis,” Resources for the Future DP 13-02. 

vi For example, see DeCanio, S. 1998. The efficiency paradox: 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. Stavins. 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 Action 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 Action 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. 2014. 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. 2014. 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 Technologies 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 Provision 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 Program Impacts’ and Environmental Compliance Costs,” Report prepared by RAP 

and Synapse Energy Economics. 

xviii Boyd, GA and JX Pang (2000). “Estimating the linkage between energy efficiency and productivity,” Energy 

Policy, 28: 289-296; Worrell, E. (2011). “Productivity benefits of industrial energy 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 costs. Proceeding of National Academy of Sciences. [forthcoming] 
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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 Program Impacts’ and Environmental Compliance 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 (ACEEE). April 2004. Select State-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-Engineering 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-gas-and-climate-change/climate-changeadaptation/mass-clean-energy-and-climate-plan.html. xxv Commonwealth of Massachusetts. Global Warming Solutions 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. Schiller, 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 Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxix Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy 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/research-report/u112. xxxi American Council for an Energy-Efficient Economy (ACEEE). November 2013. The 2013 State Energy 

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

Efficiency Scorecard. Available at http://www.aceee.org/state-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 Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard. xxxvi Barbose, G. L., C.A. Goldman, I. M. Hoffman, M. A. Billingsley. 2013. The Future of Utility Customer-Funded 

Energy 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. Recent estimates of energy efficiency potential in the USA. Energy Efficiency. xl Eldridge et. al. 2008. State-Level Energy Efficiency Analysis: Goals, Methods, and Lessons Learned. 2008 

ACEEE Summer Study on Energy Efficiency in Buildings. Available at http:// 
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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 (ACEEE). April 2014. Select State-Level Energy Efficiency 

Policy Opportunities 2016-2035. [forthcoming] 

xliii U.S. EPA and U.S. DOE. July 2006. National Action 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. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and 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 Assistance Project, “Energy Efficiency Cost-Effectiveness 

Screening. Available at http://ww.raponline.org/document/download/id/6149. xlvi U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and 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 and climate policy strategies. 

Climatic Change, January 2014; Kyle P., L. Clarke, S. Smith et al. 2011. The 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 Efficiency as a Low-Cost Resource for Achieving Carbon 

Emissions Reductions: a Resource of the National Action Plan for Energy Efficiency. 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/latest_thinking/reducing_us_greenhouse_gas_emissions. l

 U.S. EPA and U.S. DOE. November 2007. Guide for Resource Planning with Energy Efficiency: a Resource of the 

National Action Plan for Energy Efficiency. Available at 

http://www.epa.gov/cleanenergy/documents/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/research-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. Retrospective 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-Effectiveness of Electricity Energy 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 Negawatt Really Cost? Evidence from Utility Conservation 

Programs. The Energy Journal 13 (4):41-74. 

lix U.S. EPA and U.S. DOE. November 2008. Understanding Cost-Effectiveness of Energy Efficiency Programs 

(Best Practices, Technical Methods, and 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 Energy Efficiency Resource 

Standard (EERS) Activity Policy Brief. Available at www.aceee.org/files/pdf/policy-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 Cost 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 Cost of Saved Energy for Utility CustomerFunded energy Efficiency Programs. http://emp.lbl.gov/sites/all/files/cost-of-saved-energy-for-ee-programs.pdf. 

lxiv Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest Conservation and 

Electric Power Plan,” Council Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxv Gordon, Fred, Lakin Garth, Tom Eckman, and Charles Grist, “Beyond Supply 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 Document 2010-09. Available at www.nwcouncil.org/energy/powerplan/6/plan/. lxvii Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 2008. The Sustainability and Costs of 

Increasing Efficiency Impacts: Evidence from Experience to Date. ACEEE Summer Study on Energy Efficiency in 

Buildings. http://www.aceee.org/files/proceedings/2008/data/papers/8_434.pdf. lxviii For example: Northwest Power & Conservation Council (NPCC). February 1, 2010. “Sixth Northwest 

Conservation and Electric Power Plan,” Council Document 2010-09. Available at 

www.nwcouncil.org/energy/powerplan/6/plan/; and McKinsey & Company. December 2007. Reducing U.S. 

Greenhouse Gas Emissions: How Much at What Cost? Available at 

http://www.mckinsey.com/client_service/sustainability/latest_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-Funded 

Energy 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 Report 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 Economy (ACEEE). November 2013. The 2013 State Energy 

Efficiency Scorecard. Available at http://www.aceee.org/state-policy/scorecard.