The Best Value for America’s Energy Dollar: 

A National Review of the Cost of Utility Energy 

Efficiency Programs

Maggie Molina 

March 2014

Report Number U1402

© American Council for an Energy-Efficient Economy

529 14th Street NW, Suite 600, Washington, DC 20045

Phone: (202) 507-4000  Twitter: @ACEEEDC 

Facebook.com/myACEEE  www.aceee.org
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Contents

Acknowledgments...............................................................................................................................ii

Executive Summary ........................................................................................................................ iiiii

Introduction..........................................................................................................................................1

Measuring Cost Effectiveness: Practices and Challenges ..............................................................3

Energy Efficiency Costs..................................................................................................................3

Evaluation, Measurement, and Verification (EM&V) of Energy Savings...............................5

Levelized Costs Versus First-Year Costs......................................................................................7

Cost-Effectiveness Tests .................................................................................................................8

Energy Efficiency Valuation in Integrated Resource Planning ................................................9

Methodology......................................................................................................................................10

Data Collection and Processing...................................................................................................10

Challenges and Caveats ...............................................................................................................12

Calculations and Assumptions ...................................................................................................15

Results .................................................................................................................................................17

Electricity........................................................................................................................................17

Natural Gas ....................................................................................................................................30

Discussion and Recommendations .................................................................................................34

Energy Efficiency Costs Compared to Supply-Side Options..................................................34

Consistency and Transparency in Energy Efficiency Reporting............................................37

Further Research ...........................................................................................................................39

Conclusion..........................................................................................................................................39

References...........................................................................................................................................41

Appendix A. Data Sources by State and Program Administrator..............................................43
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Acknowledgments

Thank you to ACEEE’s funders for supporting this work. Many individuals at state utility 

commissions and utilities as well as non-program administrators were invaluable in sending 

data and answering questions about the data. Thank you to colleagues at ACEEE who 

reviewed an earlier draft of this report: Jim Barrett, Annie Downs, Neal Elliott, Marty 

Kushler, Steve Nadel, Max Neubauer, and Dan York; and thank you to the following 

individuals who reviewed a draft of the report and provided comments: Megan Billingsley 

(Lawrence Berkeley National Laboratory), Joe Bryson (U.S. Environmental Protection 

Agency), Tom Eckman (Northwest Power and Conservation Council), Nick Mark 

(Centerpoint Energy), Cecily McChalicher (Northeast Energy Efficiency Partnerships,

NEEP), Jen Miller (Sierra Club), Chris Neme (Energy Futures Group), Wally Nixon 

(Arkansas Public Service Commission), and Kenji Takahashi (Synapse Energy Economics).

Finally, thank you to the following individuals who helped with the editing, production,

and communications for this report: from ACEEE, Fred Grossberg, Patrick Kiker, Eric 

Schwass, and Glee Murray, and from Resource Media, Debbie Slobe.
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Executive Summary 

After a decades-long history, U.S. energy efficiency programs have expanded rapidly in 

recent years. As program administrators face rising energy efficiency targets that require 

more comprehensive portfolios, they have an increasing concern about the impact on 

program costs. This creates the need for high-quality, comprehensive, and consistent data 

metrics on energy efficiency program costs and cost effectiveness. To this end, the American 

Council for an Energy-Efficient Economy (ACEEE) has undertaken an assessment of utilitysector energy efficiency program costs and cost effectiveness in 2009-2012.

The results of our analysis clearly demonstrate that energy efficiency programs are holding 

steady as the least-cost energy resource option that provides the best value for America’s 

energy dollar. Data from a large number of diverse jurisdictions across the nation show that 

energy efficiency has remained the lowest-cost resource even as the amount of energy 

efficiency being captured has increased significantly. At an average cost of 2.8 cents per 

kilowatt hour (kWh), electricity efficiency programs are one half to one third the cost of 

alternative new electricity resource options such as building new power plants. Natural gas 

energy efficiency programs also remain a least-cost option at an average cost of 35 cents per 

therm as compared to the national average natural gas commodity price of 49 cents per 

therm in 2013. In addition, both electricity and natural gas efficiency costs have remained 

consistent over the past decade. This consistency shows the reliability of efficiency as a longterm resource. 

METHODOLOGY

The goal of the current ACEEE analysis is to collect and aggregate recent data on energy 

efficiency program costs and cost effectiveness from jurisdictions across the United States.

Our focus is on the costs to utilities or other program administrators to run efficiency 

programs, but we also include some data on the broader costs and benefits to participants 

and to society. We do not aim to compare one state’s efficiency portfolio results to others, 

but instead to present overall results. 

We collected data for 20 states for electricity programs and 10 states for natural gas 

efficiency programs from 2009 to 2012, pulling from utilities’ and other program 

administrators’ program results. We collected the necessary data (annual program costs, net 

energy savings, and measure lifetime) to calculate the levelized utility cost of saved energy 

(CSE). By levelized we mean that upfront costs are amortized over the lifetime of a measure 

at an assumed real discount rate. The levelized CSE is the best measure for comparing

energy efficiency to other energy resource options. 

Our definition of utility energy efficiency costs includes 

 Direct program costs incurred by administrators, including incentives to participants 

and all non-incentive costs such as the direct installation of measures, program 

design and administration, marketing, education, and evaluation

 Shareholder incentives or performance fees, which reflect the rate of return utilities 

earn in some states to meet or exceed certain thresholds of energy savings levels
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We also collected some data on participant costs; however these data are much more 

sparsely reported and therefore the data set includes only seven states.

Our task of data collection and comparison was complicated by numerous challenges, 

including inconsistent reporting formats, nomenclature, and frequency; variation in energy 

savings evaluation approaches and in the accounting of demand response programs; and 

structural differences in program portfolios. We tried to make the data as consistent as 

possible in the face of these challenges. We consistently calculated the CSE based on a 5% 

real discount rate, we used net energy savings values and measure lifetimes as reported by 

the program administrator, and we used energy savings reported at the meter rather than at 

generation. We converted all data to real 2011 dollars. 

RESULTS

As shown in figure S1, the CSE for electricity energy efficiency programs ranged from 

$0.013 to $0.056 per kWh across the 20 states from 2009 to 2012.

Figure S1. Electricity energy efficiency program CSE by year. Each dot represents average costs for each state in a given year. 2011$ per 

levelized net kWh at meter. Assumes 5% real discount rate. 

We calculated four-year averages (2009-2012) for each of the 20 jurisdictions (and 10 

jurisdictions for gas programs), and display the average, median, minimum, and maximum 

for the dataset in table S1. The simple average utility CSE was $0.028 per kWh for electricity 

programs and $0.35 per therm for gas programs.
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Table S1. Summary of results for four-year averages (2009-2012) for all states in dataset

Electricity programs ($ per kWh) Natural gas programs ($ per therm)

Average $0.028 $0.35

Median $0.026 $0.37

Minimum $0.016 $0.10

Maximum $0.048 $0.59

2011$ per levelized net kWh or therm at meter. 5% real discount rate. Each state’s four-year average is a distinct 

data point. The complete data set for individual years (Figure S1) has lower minimum and higher maximum values.

We also reviewed energy savings and CSE by customer class. Among the 17 states that 

readily reported electricity savings by customer class, the average portfolio included 45% 

savings from residential customers and 55% from business (commercial and industrial) 

customers. We calculated electricity CSE values by customer class for nine states (complete 

data was not readily available for the other jurisdictions), and identified an average CSE of 

$0.037/kWh for residential portfolios and $0.027/kWh for business portfolios.

While this study focused on the utility costs to deliver energy efficiency programs, we also 

examined some results of the total resource cost (TRC) test, which involves a system-wide 

perspective.1 TRC test results from nine states show benefit-cost ratios ranging from 1.24 to 

4.0 for electricity portfolios. In other words, in these jurisdictions, each dollar invested by 

utilities and participants in energy efficiency measures yields $1.24 to $4.00 in benefits. 

Many analysts have hypothesized that program CSE will increase over time as 

administrators increase energy savings levels. An initial correlation analysis in this study 

finds only a very weak correlation between CSE values and energy savings levels. This 

analysis casts doubt on the claim that higher savings levels are associated with higher costs.

While comparisons of efficiency program costs to current levelized costs for new electricity 

resource options or natural gas commodity prices provide useful context, they do not tell 

the complete cost-effectiveness story for energy efficiency. For example, in addition to the 

avoided energy- and capacity-related costs to all customers, energy efficiency programs also 

result in utility benefits such as avoided transmission and distribution (T&D) costs, peak 

demand benefits, price mitigation effects in wholesale markets, and reduced pollution.

Program participants can also benefit from lower water and fuel usage and improved 

comfort. In addition, energy efficiency programs result in reinvestment of local dollars in 

local jobs and industries. Also, these indicators of current avoided energy costs do not 

reflect future expected avoided energy costs and future price volatility. Including higher 

levels of low-cost energy efficiency in long-term planning can hedge against volatile and/or

rising costs of supply resources.

In summary, the results of this analysis clearly demonstrate that energy efficiency programs 

are the least-cost resource option available to utilities. As shown in figure S2, electricity 

 

1 A complete and balanced TRC test should include benefits both to participants and to the system.
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efficiency programs, at a range of about 2 to 5 cents per kWh and an average of 2.8 cents per 

kWh, are about one half to one third the levelized cost of alternative new electricity resource 

options.

Figure S2. Levelized costs of electricity resource options. Source: Energy efficiency data represent the results of this analysis for 

utility program costs (range of four-year averages for 2009-2012); supply costs are from Lazard 2013.

RECOMMENDATIONS AND FURTHER RESEARCH

Given the inconsistency in efficiency program report formatting, nomenclature, and 

frequency, we recommend that utilities, regulators, and program administrators in each 

state discuss these issues, perhaps also at a regional and national level, and work toward 

adopting best reporting practices. We offer several specific recommendations to improve 

consistency and transparency in reporting. 

In this review we discuss numerous metrics that may have a direct impact on the cost of 

efficiency, e.g., the share of savings by customer class, or the types of programs offered.

Further research is needed on the relative impact of these different variables on CSE values

and on the broader set of benefits to customers. Trends in CSE over time may be another 

fruitful area of study. Correlation analyses of CSE trends over time across jurisdictions are 

difficult and may produce incomplete results because of differences among program 

portfolio structures and reporting consistency. Further research should delve into this 

question, perhaps examining individual jurisdictions or regions.
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Introduction

The energy future of the United States has entered an era of increasing change and 

uncertainty. While oil and gas production have increased in recent years, ongoing 

challenges include the environmental impacts of power generation, difficulty in siting new 

energy facilities and infrastructure as well as their high capital cost, and the continuing risk 

of fuel price volatility. In the face of these challenges and the need for economic 

development strategies, an increasing number of states have turned to energy efficiency as a 

significant component of their long-term energy resource planning.

Energy efficiency has long been demonstrated to be a low-cost and low-risk strategy. The 

American Council for an Energy-Efficient Economy (ACEEE) has conducted two reviews of 

utility-sector energy efficiency programs to document their cost effectiveness (Kushler, York

and Witte, 2004; Friedrich, Eldridge, and York 2009). Both studies found that energy 

efficiency programs are the least-cost resource option compared to supply-side energy 

options.

ACEEE’s 2004 and 2009 efficiency program cost reviews found the following:

 Examining data from seven states, the 2004 review identified a range of levelized 

cost of saved energy (CSE) from $0.023 to $0.044 per kilowatt hour (kWh), with a 

median value of $0.03 per kWh (Kushler, York, and Witte, 2004).

 The 2009 review of 14 states identified CSEs ranging from $0.016 to $0.033 per kWh, 

with an average cost of $0.025 per kWh. Six natural gas efficiency program portfolios 

covered in the report had an average CSE of $0.37 per therm (Friedrich, Eldridge,

and York 2009).

Utility-sector energy efficiency programs have a decades-long history in the U.S., but have 

expanded significantly in recent years, which means the availability of new data sets and 

the increasing visibility of efficiency.1 As states face rising energy savings targets, some 

stakeholders are concerned that energy efficiency programs’ cost of saved energy will 

increase as they ramp up to hit their targets. These recent trends and concerns suggest the 

need for an updated and expanded review of energy efficiency program costs.

From 2006 to 2011, national annual electricity efficiency program spending tripled from $1.6 

billion to about $4.8 billion (Downs et al. 2013). Although budgets for natural gas efficiency 

 

1 This report focuses exclusively on utility-sector energy efficiency programs that aim to reduce overall customer 

energy usage. We do not include demand response programs, which aim to reduce or shift energy usage only 

during times of peak demand. By “utility-sector energy efficiency programs,” we mean programs funded 

through utility rates (whether embedded in rates or as a separate tariff rider or surcharge) or through associated 

public benefits charges and administered by utilities, government agencies, or third-party organizations.
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programs have been much smaller, they have gained popularity in recent years and 

increased from $0.3 billion in 2006 to $1.1 billion in 2011.

This rapid growth stems largely from the increasing adoption of energy efficiency resource 

standards (EERS) and other regulatory mechanisms that reduce barriers to efficiency and 

encourage utilities to pursue it cost effectively.2 States have also increasingly recognized 

energy efficiency as a low-cost economic development tool that attracts new businesses,

creates jobs, and stimulates the economy. Twenty-six states now have EERS policies, and 

many others have other short-term energy efficiency planning processes.

These recent trends open up a wider set of utilities and states that collect data on efficiency 

program costs. Similarly, as efficiency programs gain traction as a true resource that 

planners can use in long-term forecasting, the need increases in step for high-quality and 

uniform data metrics on energy efficiency program costs and benefits.

Numerous utilities and statewide program administrators are now facing rising energy 

efficiency targets as part of their EERS policies, and they must hit these targets within firm

cost-effectiveness requirements. Some stakeholders are concerned that the cost of efficiency 

programs is rising and that it is becoming more difficult to realize savings as federal 

appliance and equipment standards raise the baseline. It is true that program costs increase 

in the short term as programs target the uptake of higher-cost technologies, e.g., as they 

move from CFLs to LEDs. But, continuing with this example, costs are quickly declining for 

LEDs, which can counterbalance the initial higher program costs. Similarly, utilities are 

developing new, highly cost-effective program strategies such as large customer reverse 

auctions and strategic energy management. They are also identifying market gaps such as 

multifamily buildings. Still, much uncertainty and many misconceptions remain about the 

costs and cost trends of efficiency programs.

The goal of this analysis is to collect and aggregate recent data on energy efficiency program 

costs and cost-effectiveness metrics from jurisdictions across the U.S. as a comprehensive 

source of information for stakeholders. Our primary focus is on the costs incurred by 

utilities or other program administrators to run efficiency programs, but we also include 

some data on the broader costs and benefits to participants and society. We collected data 

by reviewing utilities’ and other program administrators’ program results to calculate CSE 

values. We do not aim to compare states’ efficiency portfolio results, but instead to present 

overall results. We also would like to advance the discussion on how to improve protocols 

and consistency in the reporting of efficiency programs.

 

2 EERS policies establish specific, long-term (3+ years) energy efficiency targets that utilities or non-utility 

program administrators must meet through customer energy efficiency programs. See 

http://www.aceee.org/topics/eers for more information.
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Measuring Cost Effectiveness: Practices and Challenges

Since the 1980s, energy efficiency programs have gained traction as a true resource option 

that utilities and states can scale up and rely on within their resource plans. As a demandside resource, however, efficiency is fundamentally different from supply options such as 

power plants and wind turbines. This difference calls for a unique set of methodologies to 

quantify efficiency as a resource by measuring its energy savings and cost effectiveness.

Since the 1970s and 80s, regulators have adopted particular practices to evaluate the costs 

and energy savings from efficiency programs. States have adopted these practices with 

varying degrees of consistency, and this creates a challenge for reviewing efficiency 

program costs across states. Some regions of the country have begun to coordinate 

methodologies and reporting guidelines through efforts such as the Regional Technical 

Forum in the Pacific Northwest and the Northeast Energy Efficiency Partnership (NEEP) 

Regional Evaluation, Measurement, and Verification Forum.

3 However most states continue 

to use a diverse set of methodologies and reporting structures.

This section presents our approach to reviewing the costs, savings, and cost effectiveness of 

efficiency programs, addressing the following questions: 

1) What is typically included in the definition of energy efficiency program costs?

2) How are energy savings evaluated, measured, and verified (EM&V)?

3) How are costs expressed relative to energy savings? For example, what is the 

relationship between levelized costs, first-year costs, and measure lifetimes?

4) How are energy efficiency cost-effectiveness tests currently applied?

We discuss practices and challenges for each topic in this background section, as well as the 

approach we took to these issues in our review. The subsequent section on methodology 

goes into further detail on our approach to some of these topics.

ENERGY EFFICIENCY COSTS 

Program Costs

What types of values should be attributed to the cost of delivering energy efficiency as a 

utility resource? We include two broad categories of energy efficiency resource costs: (1) 

direct program costs and (2) shareholder incentives (also called performance incentives or 

performance fees) earned by utilities or third-party program administrators for reaching or 

exceeding certain energy savings thresholds.4 From a utility or program administrator 

perspective, the sum of efficiency program costs and performance incentives comprises the 

total cost of energy efficiency resources.

 

3 For more information, see http://rtf.nwcouncil.org/ and http://neep.org/emv-forum/.

4 For more information on performance incentives, see http://aceee.org/sector/state-policy/toolkit/utilityprograms/performance-incentives.
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The following types of costs are commonly incurred by energy efficiency program 

administrators as the direct costs to administer programs:

a. direct rebates or incentives to customers 

b. engineering or technical support

c. program administration, planning, and delivery

d. evaluation, measurement, and verification (EM&V) 

e. marketing and education5

The second general category is performance incentives, which are either utility shareholder 

incentives or performance management fees for non-utility program administrators. Both 

are typically established as a way to encourage greater levels of efficiency, and typically

they are earned only if certain thresholds of energy savings are met or exceeded. While 

utilities earn the incentives for good performance and may not perceive them as a direct cost 

of efficiency programs, ratepayers foot the bill for performance incentives, so they need to 

be accounted for in calculating the overall cost of delivering energy efficiency resources. Not 

all jurisdictions, however, adopt performance incentives: currently 28 states have them in 

place for at least one major utility (Downs et al. 2013). We have chosen to include 

performance incentives as a cost component of delivering energy efficiency resources 

because they are a direct way to encourage energy efficiency performance, and they are 

equivalent to a rate of return that utilities would earn on a supply-side investment.

6

Participant Costs

In addition to the program costs incurred by administrators, program participants may 

spend additional money to purchase or install energy efficiency upgrades. Depending on 

the type of cost-effectiveness test used (as discussed later in this section), participant costs 

may or may not be included as a component in cost-effectiveness screening. The total 

resource cost (TRC) test, for example, includes participant costs, while the utility or program 

administrator cost test (UCT/PACT) assumes the perspective of the utility planner and so 

does not take participant costs into account.

The best way to directly compare efficiency costs to supply-side options is to take the 

perspective of the UCT/PACT and focus on the cost of energy efficiency programs as a 

resource option to utility planners. Since this is how we focused our analysis, we did not 

conduct an extensive review of participant costs. Although we did collect some limited data, 

most annual program administrator reports do not include participant cost estimates and 

benefits. Participant costs are used as an input to the TRC calculations, however, and 

therefore embedded in the results of any TRC test. See the Methodology and Results 

sections for further details on participant cost estimates and TRC test results.

 

5 For all of these cost types, the nomenclature and reporting vary across jurisdictions.

6 Including this factor in comparisons with the cost of supply-side resources is only appropriate if those supply 

cost estimates include all associated utility “incentives” (e.g., rate of return).
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Decoupling and Lost Fixed Cost Recovery

Symmetrical decoupling is a way to remove the throughput incentive to utilities, which 

otherwise links utility profits to increased energy sales. While the decoupling mechanism is 

a critical component of a regulatory approach that puts efficiency on a level playing field 

with supply resources, it should not be considered a cost of delivering energy efficiency 

programs. Rather, it is used to improve regulatory certainty in ratemaking. Decoupling was 

widely adopted in the gas utility industry, for example, during the era of declining energy 

sales.

Mechanisms to directly compensate utilities for lost fixed cost are a different approach than 

decoupling.

7 These mechanisms allow utilities to recover fixed-cost revenues that are “lost” 

due to energy savings from efficiency, but they do not adjust rates downward if revenues 

are greater than authorized.

Neither decoupling nor lost fixed cost adjustments are costs of delivering efficiency services, 

because they do not increase total revenue requirements. Rather, they are rate tools 

designed to reallocate fixed costs in different ways, i.e., to recover the same fixed costs that 

would have been recovered anyway. For these reasons, neither mechanism is included in 

our analysis of efficiency costs.

It is noteworthy that these policy mechanisms are being used as a way to improve the 

business case for energy efficiency. Currently 13 states have full revenue decoupling for at 

least one major electric utility in the state, and 19 states have lost fixed cost mechanisms for 

at least one utility (Downs et al. 2013). Recent literature has explored the impact of 

decoupling on rates and found that most rate adjustments (64%) are within plus or minus 

2% of the retail energy rate, which amounts to about $2.30 for the average electric residential 

consumer (Morgan 2012).

EVALUATION, MEASUREMENT, AND VERIFICATION (EM&V) OF ENERGY SAVINGS

Dating back to the 1970s and 1980s, EM&V of energy efficiency results aims to assess the 

performance and implementation of programs, document and measure their effects, help 

program planners improve performance, and ensure that programs are cost effective. The 

State and Local Energy Efficiency Action Network (SEE Action) defines EM&V as the 

collection of approaches for determining and documenting energy and non-energy 

benefits resulting from end-use energy efficiency activities and programs. Effective 

EM&V can confirm energy savings, verify cost-effectiveness, and guide future 

energy efficiency investment decisions.

8

Various international, national, and regional groups have been working to improve 

consistency and standardization in the EM&V process. For example, the International 

 

7 These are often called lost revenue adjustment mechanisms (LRAM) or lost contribution to fixed costs (LCFC).

8 See http://www1.eere.energy.gov/seeaction/evaluation.html.
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Performance Measurement and Verification Protocol (IPMVP), published by the Energy 

Valuation Organization, defines standard terms and provides a framework for verifying 

project-specific energy efficiency savings. In the United States, the Department of Energy’s 

SEE Action network, the NEEP EM&V Forum, and the Regional Technical Forum (RTF) of 

the Northwest Power and Conservation Council all have initiatives to develop common 

standards and approaches to verify and evaluate efficiency savings.

While efforts to improve consistency in energy savings EM&V have expanded, in practice 

states still use a diverse set of methods to document savings. Not only do they use a variety 

of cost-effectiveness tests, as discussed below, but they also have various approaches to the 

energy savings calculations themselves. For example, whereas most states use deemed 

savings (i.e., predetermined engineering estimates of savings per measure, or savings 

estimates verified in past EM&V studies), some states use different methodologies to 

calculate savings after measures are installed.

9 Similarly, states have different approaches to 

achieving consistency in evaluation. Many adopt their own technical resource manual 

(TRM) as a way to specify engineering calculations for estimating savings. Others in regions 

such as the Northeast or Northwest may share resources, and still others do not have 

standard methodologies.

Net Versus Gross Savings

Another key methodological difference among states in evaluating energy savings is 

whether they estimate net or gross energy savings impacts from efficiency programs (or 

both). The definition of these terms, methodology used, and application for use also vary.

Gross energy savings impacts are “changes in energy consumption that result directly from 

program-related actions taken by participants in an energy efficiency program, regardless of 

why they participated” (NREL 2013). Net energy savings are “changes in energy use 

attributable to a particular energy efficiency program. These changes may implicitly or 

explicitly include the effects of factors such as freeridership, participant and non-participant 

spillover, and induced market effects” (NREL 2013).10 In practice, net savings calculations

typically account for freeridership, but only sometimes account for spillover and induced 

market effects. 

A recent national review by ACEEE examines and documents state practices, precedents, 

and issues regarding net and gross savings (Kushler, Nowak, and Witte 2014). The study 

finds that the majority (54%) of the 43 states that responded to the survey estimate net 

energy savings using specific values for programs, another 5 states apply a uniform net-togross (NTG) ratio, another 4 states estimate both net and gross, and the final 11 states 

estimate gross savings only. The study’s review of states and national experts makes it clear 

that both net and gross savings can serve useful purposes. For example, estimates of net 

savings help program improvement as they provide information toward minimizing 

 

9 ACEEE’s 2012 survey of EM&V practices found that 36 of the 42 states that responded used deemed savings 

values to calculate energy savings (Kushler, Nowak, and Witte 2012).

10 Freeriders are participants who would have adopted efficiency measures in the absence of the program.
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freeriders, while gross savings are more straightforward and less expensive to estimate.

Overall, there is a need and often regulatory pressure to understand the net impacts 

attributable to programs, especially as a way to calculate things like cost effectiveness and 

lost revenue adjustments in order to protect ratepayer interests. 

For our CSE calculations, we chose to use net energy savings figures, which most 

jurisdictions reported. We recognize that methodologies for calculating net savings can vary 

by state and jurisdiction, making it difficult to directly compare results. However, because 

the focus of this review is on energy efficiency as a resource for utility planners, and since 

stakeholders by and large are most interested in the net impacts of efficiency programs on 

energy usage, we decided to focus on cost effectiveness based on net savings.

Electricity Savings at Site or Generation Level

Another variation in reporting of energy savings (for electricity only) is that some entities 

report “at-site” savings, i.e., at the customer meter, whereas others report “at-generation”

savings, which add in estimated transmission and distribution (T&D) line losses that are 

avoided. The at-generation approach is an attempt to directly compare the energy savings to 

the electric generation that would otherwise be needed to offset the efficiency gains. It is 

useful in integrated resource planning (IRP) because it puts efficiency on a level playing 

field with supply-side resources.

At-generation savings are most appropriate for comparing efficiency costs to electric supply 

resources, and perhaps the appropriate framework for this analysis. However at-site savings 

are more useful for comparing efficiency gains to overall electricity sales, and they are the 

most common and longstanding approach to measuring and evaluating energy savings.

Moreover most state EERS are established as a percentage of retail sales. For these reasons, 

this analysis presents energy savings data at site or meter. In the Results section, we also 

examine the implications of using generation-level energy efficiency savings.

While this range of diversity in methodology among states makes it challenging to compare 

cost values, our review tries to make the differences across states transparent. See the 

Methodology section for more details.

LEVELIZED COSTS VERSUS FIRST-YEAR COSTS

Program managers and regulators typically use two general approaches to express the costs 

of energy efficiency portfolios relative to energy savings: (1) levelized CSE and (2) first-year 

“acquisition” costs. Since both approaches provide meaningful information to planners, we 

review them both. However, ACEEE finds that levelized costs are the best way to compare 

efficiency program costs to supply options, and therefore we place more emphasis on this 

metric. By levelized, we mean that upfront investments are annualized over the life of the 

investment assuming a real discount rate.

Energy planners commonly use levelized costs as a way to express the costs of long-term 

energy supply investments. For electricity generation technologies, for example, the 

levelized cost represents the per-kWh cost expressed in real dollars of building and 
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operating a power plant over an assumed financial life, duty cycle, and capacity factor.

11

Similarly, levelized cost is an appropriate metric for energy efficiency resources, which 

continue to save energy over several years of their effective useful lifetime. A full 

description of the cost-of-saved energy approach is included in the Methodology section.

A second approach to expressing efficiency program costs relative to their savings is to use

first-year costs, which are representative of the annual costs to administer an efficiency 

portfolio divided by the energy savings in the first year only. These are sometimes called 

energy efficiency acquisition costs, and they can be useful for program budgeting purposes.

Program administrators and regulators tend to focus on first-year costs when they are faced 

with one-year savings targets. These costs, however, do not take into account the full value 

of efficiency investments because they capture only the first-year savings, whereas the 

measures continue saving energy throughout their useful lifetime.12 (We present data on 

typical measure lifetime in the Results section.) In other words, higher first-year costs do not 

necessarily mean higher levelized CSE values. First-year costs thus misrepresent the full 

benefits of efficiency. Furthermore, supply-side investments are not typically assessed based 

on the full upfront costs. 

COST-EFFECTIVENESS TESTS

Regulators typically predicate energy efficiency programs on the fact that they are costeffective compared to their avoided costs.

13 This adds a layer of rigor to the requirements for 

energy efficiency program review, necessitating detailed analysis to evaluate how efficiency 

costs and benefits accrue to various parties with different perspectives.

Utilities and other program administrators use some combination of various costeffectiveness tests. These tests have evolved from the first California Standard Practice 

Manual in 1983, which has been periodically revised since then, most recently in 2011.

Representing various perspectives, the five standard cost-effectiveness tests are 

 

11 See http://www.eia.gov/forecasts/aeo/er/electricity_generation.cfm

12 For example, two measures can have identical levelized costs, while the first-year cost for a measure with a 

shorter lifetime (e.g., CFLs) appears lower than that of a measure with a much longer lifetime (e.g., insulation).

13 The term “avoided costs” originated with federal laws designed to encourage independent power production.

They refer to the costs that utilities would incur to produce one more unit of electricity (kWh) and/or capacity 

(kW) or one more unit of natural gas (therm). For energy efficiency cost-effectiveness evaluation, avoided costs 

refer to the energy-related and capacity-related costs that would have been incurred by utilities if the energy 

efficiency measures had not been adopted. Thus they are used as a reference point against which efficiency 

programs are compared. The methodology for calculating avoided costs can vary significantly across 

jurisdictions.
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 Utility/Program Administrator Cost Test (UCT/PACT)

 Total Resource Cost (TRC) test

 Societal Cost Test (SCT) 

 Participant Cost Test (PCT) 

 Ratepayer Impact Measure (RIM) test

Numerous resources are available on the topic of cost-effectiveness tests (e.g., National 

Action Plan for Energy Efficiency 2008; Woolf et al. 2012; Kushler, Nowak, and Witte

2012).

14 A recent national review by ACEEE found that most states use a combination of the 

tests, with the TRC being the most widely used as the primary test and the RIM rarely being 

used (Kushler, Nowak, and Witte 2012). For information on each state’s approach to costeffectiveness tests, see the ACEEE State Energy Efficiency Policy Database.

15

From a utility resource planning perspective, the UCT is the preferred approach for 

evaluating energy efficiency as a resource for utility planners, and thus is the focus of this 

report. A handful of states use the UCT as their primary test: Connecticut, Michigan, New 

Mexico, Texas, and Utah. The TRC, although most widely used as the primary test, can be

challenging to implement properly because it takes a system-based approach that requires 

all costs and benefits to be fully accounted. While costs to utilities and customers are 

relatively straightforward to count, the benefits are less straightforward, particularly for 

customers, and as a result they are often underreported (Kushler and Neme 2010).

Given the diversity of cost-effectiveness tests used across the states as well as 

methodological differences such as discount rates, the results of these tests can be difficult to 

compare across jurisdictions. While the focus of this review is on the cost of saved energy, 

we also collected the benefit/cost (B/C) ratio results of the TRC when they were available.

ENERGY EFFICIENCY VALUATION IN INTEGRATED RESOURCE PLANNING

Energy efficiency program costs are typically evaluated differently than other energy 

resources: they are evaluated against the avoided costs of supply options. In other words, 

regulators want to know how the cost of procuring additional energy efficiency compares to 

the prevailing cost of the next marginal unit of supply that would otherwise be incurred.

Efficiency resources that cost less than avoided costs are deemed cost effective.

As efficiency gains traction as a resource option, efficiency programs should be incorporated 

into integrated resource plans (IRP) and other planning tools that truly optimize efficiency 

as analogous to a supply-resource option. Although many states began to do this in the 

1980s and many continue today, efficiency is typically treated through scenarios of the 

demand curve rather than as an explicit resource option. Improved analysis of energy 

efficiency program costs and impacts in terms of procured energy (kWh) and demand (kW) 

 

14 Resources are available from the Regulatory Assistance Project (RAP), the SEE Action Network (and its 

predecessor the National Action Plan for Energy Efficiency), and ACEEE.

15 http://www.aceee.org/sector/state-policy
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savings will be crucial to the incorporation of efficiency into multi-objective resource 

planning tools. For more information, see the resources offered by the Regulatory 

Assistance Project (RAP) on best practices for the incorporation of energy efficiency into IRP 

processes (e.g., RAP 2013).

Methodology

This section describes the data collection process for this study, the challenges and caveats 

attendant on processing the data, and the various calculations used to estimate the CSE and 

first-year acquisition costs.

DATA COLLECTION AND PROCESSING

For this review, we collected data on energy efficiency program costs and energy savings 

from secondary sources including annual reports, EM&V reports, and in some cases 

individual requests to contacts at public utility commissions (PUCs), utilities, and state 

agencies. ACEEE’s 2009 review collected data from 14 states. Now, with more states 

developing comprehensive energy efficiency portfolios and reporting their results, we were 

able to collect data for 20 states for electricity programs, as shown in table 1, and 10 states 

for natural gas efficiency programs, as shown in table 2. We chose the states for two reasons: 

(1) they were included in the last study and thus were good candidates to include again,

and/or (2) they had readily available cost data in consistent reporting formats. Other states 

or utilities may have had data on energy efficiency program cost effectiveness, but if they 

did not have consistent and transparent metrics reported in a common location, they were 

not good candidates for this study. ACEEE hopes to continue conducting reviews of this 

sort and to expand the data set in the next update.

Table 1. States and program administrators covered in the review: electricity programs 

State

Program 

administrator 

covered State

Program 

administrator 

covered

1 Arizona Arizona Public 

Service Company 

(APS)

11 New Mexico Public Service of 

New Mexico

2 California IOUs 12 New York NYSERDA

3 Colorado Xcel Energy 13 Nevada NV Energy

4 Connecticut CEEF (all IOUs) 14 Oregon Energy Trust of 

Oregon

5 Hawaii Hawaii Energy 15 Pennsylvania IOUs

6 Illinois Ameren and Com-Ed 16 Rhode Island National Grid

7 Iowa IOUs 17 Texas IOUs

8 Massachusetts IOUs 18 Utah Rocky Mountain 

Power

9 Michigan All utilities 19 Vermont Efficiency Vermont
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State

Program 

administrator 

covered State

Program 

administrator 

covered

10 Minnesota Xcel Energy 20 Wisconsin Focus on Energy

IOUs are investor-owned utilities.

Table 2. States and program administrators covered in the 

review: natural gas programs 

State

Program administrator 

covered

1 California IOUs

2 Colorado Xcel Energy

3 Connecticut CEEF

4 Iowa IOUs

5 Massachusetts IOUs

6 Michigan All utilities

7 Minnesota Xcel Energy

8 Oregon Energy Trust of Oregon

9 Rhode Island National Grid

10 Wisconsin Focus on Energy

IOUs are investor-owned utilities.

We collected 10 data points for 2009 to 2012 annual program years as available. Some states 

did not yet have 2012 data available, and others only had one or two years available. Not all 

data points were available for all states. Note that in many cases we had to manipulate the 

data to permit consistent comparison among programs, e.g., by subtracting out demand 

response or renewable energy program costs. We provide some details here for each of the 

data points collected and processed, and we further discuss key challenges and caveats in 

the next section.

1. Annual total program costs by program year. We included energy efficiency program 

portfolio costs only, not renewable energy or demand response. 

2. Annual program costs by customer class (residential and business). Most states categorize 

classes by residential and business, whereas only a couple of jurisdictions disaggregate 

business customers into commercial and industrial. Low-income programs are often 

categorized separately; however we chose to include these programs in the residential

category for convenience in reporting.

3. Shareholder or performance incentives awarded annually as applicable. We collected data for 

those states with performance incentives that had been approved for the applicable program 
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year. In a couple of cases (e.g., Wisconsin), performance incentives are awarded on a 

cumulative-year basis, and so they were not yet approved and were not included in our 

estimates.

4. Annual costs by type (customer incentives, non-incentive program costs, and 

shareholder/performance incentives). Several of the jurisdictions in our review reported 

customer incentives as a distinct category, and some reported numerous other categories of 

spending such as administrative, research and development, education, and marketing. In 

these cases, we combined all non-incentive costs into one category. Other states may have 

reported program costs as distinct from administrative or EM&V; however it was unclear 

whether the definition of program costs included both customer incentives and other 

program-related costs.

5. Annual participant cost estimates. Only a handful of states explicitly and readily report 

participant contributions to energy efficiency measures, or at least report full incremental 

measure costs. It is possible to derive participant cost contributions by subtracting

incentives from full incremental measure costs.

6. Gross and net energy (kWh and therms) savings reported, both total and by customer class. We 

collected both gross and net electricity and natural gas savings from efficiency programs, 

and by customer class if available. For electricity savings, we noted whether savings were 

reported at site or at generation. We also collected some data on electricity demand (kW) 

impacts, but not comprehensively enough to report here.

7. Applicable electricity sales within jurisdiction. We collected electricity sales for the 

jurisdictions included in the state for 2010, which is the most readily accessible data point 

from the Energy Information Administration (EIA) for all jurisdictions. We collected 

electricity sales data in order to normalize savings as a percentage of sales and to compare 

this metric to CSE values.

8. Measure lifetimes by customer class. As available, we collected measure lifetimes by 

customer class and in aggregate for the entire portfolio as an input to CSE calculations.

9. Cost-effectiveness test ratios. We collected these for TRC tests and UCT/PACT, as available.

10. Weighted average cost of capital assumed in cost-effectiveness calculations. In some cases, 

program administrators reported their own utility CSE values. We collected the weighted 

average cost of capital (WACC) that was assumed for these calculations, along with 

aggregate measure lifetimes, in order to derive first-year cost assumptions. Also, we were 

interested in this metric in general to compare to our own assumptions, and so we also 

collected WACC and social discount rate assumptions for other states as available.

CHALLENGES AND CAVEATS

As previously discussed, there are a number of challenges involved in the collection and 

comparative review of national energy efficiency cost-effectiveness data. In light of these, 

the goal of this report is not to compare one state’s efficiency portfolio results to others, but 
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to present overall information and trends in energy efficiency program costs and cost

effectiveness—and also to improve the transparency of key data issues. This section 

explores several of the challenges and discusses our approach to improving consistency

within the dataset.

Variation In Reporting Formats, Frequency, And Timing

Most utilities and program administrators prepare annual reports on the impacts of 

efficiency programs, and some prepare reports more frequently, e.g., semiannually or 

quarterly. The frequency and formats are usually based on regulator requirements; however 

this varies by state. In some limited cases, annual reports present only cumulative data for 

multiple years and not distinct annual results.

The location and consistent availability of reports vary significantly by state. Some utilities 

and program administrators post reports on their own websites, others file them on 

commission websites or within commission dockets, some states have a separate website 

hosted by an advisory group, or there may be combination of these approaches. Program 

administrators who do not produce reports that are readily available in a common location 

were less likely to be included in this study.

Most program administrators are required by their regulators to calculate cost and energy 

impacts separately for electricity and natural gas programs (and they may also present 

combined results). A few states combine electricity and natural gas programs in their 

reporting. In some cases, on request, program administrators suggested a methodology for 

disaggregating program costs; in other cases we were unable to disaggregate the data and 

so these jurisdictions were not good candidates for this type of review. Similarly, renewable 

energy programs or demand response programs were sometimes combined in the overall 

reporting of cost-effectiveness metrics. In these cases we backed out these program costs 

and savings impacts to isolate the energy efficiency programs.

Variation In Reporting of Net or Gross Energy Savings, and At-Site or At-Generation Electricity 

Savings

As discussed earlier, we chose to calculate and report CSE values based on net energy 

savings. The great majority of jurisdictions in this review reported net energy savings

values. A couple of states or program administrators explicitly assume that net and gross 

savings are equivalent, i.e., that there is a 100% NTG ratio. Only a couple of states in our 

data set (Minnesota and Nevada) do not estimate net savings; in those cases we adjusted 

gross savings figures by an NTG ratio of 0.9 to estimate net savings and make them more 

comparable to net savings figures reported by other states.

16 An NTG ratio of 0.9 falls within 

the range of factors used by several states in calculating net energy efficiency savings.

As discussed earlier, some states report electricity savings at the customer or site level while 

others report savings at the generation level. At-site savings appear to be the most common 

and longstanding approach to measuring and evaluating energy savings; moreover most 

 

16 This methodology is consistent with ACEEE’s State Energy Efficiency Scorecard (Downs et al. 2013).
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state EERS are established as a percentage of retail sales. For these reasons, this review 

presents at-site energy savings data. In some cases we converted at-generation savings to atsite savings assuming the same line loss factor used by the reporting entity.

Demand Response 

Many program administrators combine their reporting of energy efficiency (EE) and 

demand response (DR) programs in their overall demand-side management reports and 

evaluations.17 While EE aims to reduce overall energy usage (kWh), DR aims to curtail 

demand (kW) only during peak hours or to shift usage from peak hours to off-peak hours.

As demand-side resources, EE and DR have documented synergies; for example, EE can 

contribute on-peak demand reductions, and DR can produce some kWh savings. However 

they remain fundamentally different resources. This review focuses exclusively on EE costs

and energy (kWh) savings. In some cases we had to subtract out DR costs from reported 

spending to focus on EE costs and benefits. 

Structural Differences in Program Portfolios

In addition to variations in reporting and evaluation methods, numerous differences in the 

structure of efficiency programs can affect efficiency costs. The type of programs offered, the 

relative share of program savings from different customer classes, and the range of eligible 

efficiency measures can all affect program cost effectiveness. These factors can also impact 

the balance of incentive versus non-incentive program costs. While this study does not tease 

out costs by program type, we do classify costs by customer class and try to identify trends.

For trends in costs at the program level, readers should review an analysis by the Lawrence 

Berkeley National Laboratory (LBNL 2014).

Differences In Program Cost Type Definitions 

As discussed earlier, energy efficiency program administrators commonly incur the 

following types of costs as the direct costs to administer programs, with the nomenclature 

and reporting often inconsistent across jurisdictions:

a. direct rebates or incentives to customers 

b. engineering or technical support

c. program administration, planning, and delivery

d. EM&V 

e. marketing and education

In particular, the definition of cost categories such as “administrative” varies by state, and 

sometimes categories are not disaggregated. Nor is this an exhaustive list of current or 

future cost types. Emerging programs such as behavioral and loan programs may require 

new cost-type definitions.

In addition, whether a program administrator earns a shareholder or performance incentive 

can increase the cost of energy efficiency resources. Also, we note that some states have an 

 

17 Demand response is also referred to as load management; however “demand response” is the more modern 

term. 
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incentive mechanism in place but they award it on a cumulative basis. If the award had not 

yet been finalized for program years 2009-2012, we did not include it in this analysis. Future 

work on CSE values could develop a methodology for estimating average annual values in 

such instances. 

CALCULATIONS AND ASSUMPTIONS

After the data collection process, we first converted all cost data by program year to 2011$ 

using GDP deflators from the Bureau of Economic Analysis (BEA).

Utility CSE 

In the 2009 review, ACEEE presented the CSE as reported by the state in many cases, while 

calculating the CSE for some states. Reported values have the limitation that input 

assumptions may not be clear, which create inconsistencies in the data set. For this update, 

to attempt a more consistent review and methodology, we instead calculate the CSE for each 

state, as shown below.

To calculate the CSE, we multiply annual energy efficiency program costs (C) by a capital 

recovery factor and then divide by the annual energy savings (D). The calculation for the

capital recovery factor, which is used as a way to levelize or spread the costs over a 

specified period of time and assumed interest rate, is shown below. For consistency, we use 

the same real discount rate (A) for all jurisdictions. We use each state’s estimated measure 

lifetime (B), program costs (C), and net energy savings (D). We discuss each of these 

elements in further detail below.

The CSE calculation is:

CSE in $/kWh = (C) x (capital recovery factor)/(D)

where:

A = Real discount rate (5%)

B = Estimated measure life in years

C = Total annual program cost in 2011$

D = Incremental net annual energy (kWh or therms) saved by energy efficiency programs

Capital recovery factor = [A*(1+A)^(B)]/[(1+A)^(B)-1]

DISCOUNT RATE The discount rate for energy efficiency cost-benefit analysis depends on the costeffectiveness test used. For the utility cost test and TRC, jurisdictions typically use the 

utility’s WACC. The SCT takes a societal perspective and should use a lower social discount 

rate to appropriately value long-term societal perspective. We collected some utility data on 

WACC rates, and found that they ranged from 7% to 8% over the 2009-2012 period. It was 

not always clear whether these values were nominal or real; however we presumed them to 

be nominal rates because they were used for annual reporting and in some cases they were
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confirmed as nominal.

18 We also collected some data on assumed social discount rates used 

for cost-effectiveness screening and found they ranged widely from about 1.2% to 6.0% 

(real).

The current practice of assuming the WACC for energy efficiency cost-effectiveness 

screening, however, has been criticized as undervaluing the reduced risk of energy 

efficiency program expenditures versus supply-side investments (Woolf et al. 2012). To 

reflect the lower financial risk of efficiency investments, some jurisdictions have adopted 

alternative discount rates for energy efficiency valuation in the UCT and TRC tests, such as 

a societal discount rate or a risk-adjusted discount rate. In Massachusetts, for example, 

regulators have acknowledged that energy efficiency resources are a low-risk investment 

and that a low-risk discount rate is most appropriate for the TRC test (Woolf et al. 2013). In 

the Northwest, the preferred approach is to use a risk-free discount rate for both supply 

resource and energy efficiency, and then to explicitly model resource risk (i.e., fuel price, 

environmental regulation, capital cost, and so forth) in the analysis of resource options 

(Northwest Power and Conservation Council 2010). This approach improves transparency 

by requiring that the type and magnitude of risk estimates for each resource are displayed.

For this analysis, we assume a real discount rate of 5% (value A in our CSE calculation) for 

the overall presentation of the results.19 This is meant to be fairly consistent with the 

weighted average utility cost of capital in real terms, and is consistent with the approach in 

the 2009 ACEEE review of energy efficiency costs. We also report the aggregate CSE values 

(for all states) in the Results section using a 3% real discount rate and 7% real discount rate 

to show the impact of this assumption on the results.

MEASURE LIFETIME The estimated measure lifetime in years (B) is based on data from the program 

administrator, if available. For some states (Colorado, Illinois, Michigan, Nevada, 

Pennsylvania, and Texas), we were unable to track down average measure lifetime 

estimates for the entire portfolio. In some cases these states did report program- or measurespecific measure lifetimes; however, due to time constraints, we were unable to go through 

all program data to develop an average portfolio-wide estimate ourselves. Instead, for these 

states we assumed an overall 11-year measure lifetime, which was the average of states that 

did provide data. Similarly, to estimate CSE values by customer class, if state-specific data 

were not available, we assumed an 8-year measure lifetime for the residential class and 12.5

years for the business class, which were the average values for states that did provide data.

COSTS AND SAVINGS Total program costs (C) and incremental net annual energy savings (D) are 

based on data collected from the program administrators, as previously discussed and 

defined. Note that we used net savings (D) as available. Some states assume that net savings 

 

18 Real discount rates do not include inflation, whereas nominal discount rates do. Assuming 1% inflation, these 

nominal WACC rates of 7-8% would range from 6% to 7% in real dollar terms. Assuming 2% inflation, they 

range from 5% to 6%.

19 In deciding whether to use a nominal or real discount rate, the key is consistency. This analysis examines 

energy efficiency program costs in real (2011$) terms, and therefore we apply a real discount rate.
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equal gross savings (i.e., a 100% NTG ratio); a couple of states do not estimate net savings, in 

which case we estimated net savings using an NTG ratio of 0.9.

First-Year Acquisition Costs

We also calculate the first-year acquisition costs ($ per kWh-net or $ per therm-net), as 

shown below:

First-year cost in $/kWh net or $ per therm net = C / D

where:

C = Total annual program cost in 2011$

D = Incremental net annual energy (kWh or therms) saved by energy efficiency programs

Energy Savings Relative to Sales

For the electricity data set, we collected data on actual electricity sales in each applicable 

jurisdiction for one year (2010). We then were able to calculate energy savings as a 

percentage of applicable energy sales in the given jurisdiction. This allowed for a direct 

comparison of energy savings thresholds to energy costs.

Results

This section presents the results of the review. Data sources for each state can be found in 

Appendix A.

ELECTRICITY

The review includes electricity energy efficiency program data for the 20 states listed in 

table 1 above.

Cost of Saved Energy

Our results are focused on the CSE values as presented in figure 1 and table 3 below. We 

emphasize again that the goal is not to compare results among states, but to present an 

overall picture of the range and typical values across many different jurisdictions, each of 

which has its own factors that bear on the costs of efficiency programs. For example, note 

that the costs in figure 1 range from $0.013/kWh to $0.056/kWh, a spread of about 

$0.042/kWh.
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Figure 1. Electricity energy efficiency program CSE by year. Each dot represents average costs for each state in a given year. 2011$ per 

levelized net kWh at meter. Assumes 5% real discount rate. 

Table 3 presents the average for each state for each year, and the state’s four-year average 

from 2009 to 2012. We were unable to calculate data for every state for each year due to 

missing data points, which means that the overall average for each year represents a 

varying number of jurisdictions. 

Table 3. CSE in $ per levelized net kWh at meter

State 2009 2010 2011 2012

4-year 

average 

(2009-

2012)

Arizona $0.016 $0.019 $0.020 $0.021 $0.019 

California $0.039 $0.041 $0.056 n/a $0.045 

Colorado $0.023 $0.029 $0.027 $0.027 $0.027 

Connecticut $0.037 $0.050 $0.045 $0.047 $0.045 

Hawaii $0.025 $0.024 $0.033 $0.040 $0.031 

Illinois n/a n/a $0.019 n/a $0.019 

Iowa $0.019 $0.018 $0.020 $0.018 $0.019 

Massachusetts $0.056 $0.048 $0.037 $0.051 $0.048 

Michigan $0.017 $0.016 $0.017 $0.018 $0.017 

Minnesota $0.021 $0.027 $0.029 $0.026 $0.026 

New Mexico $0.025 $0.024 $0.022 $0.018 $0.022 

Nevada $0.013 $0.014 $0.016 $0.020 $0.016 
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State 2009 2010 2011 2012

4-year 

average 

(2009-

2012)

New York $0.020 $0.020 $0.020 n/a $0.020 

Oregon $0.028 $0.025 $0.029 $0.026 $0.027 

Pennsylvania n/a n/a $0.017 n/a $0.017 

Rhode Island n/a $0.040 $0.044 $0.050 $0.045 

Texas $0.025 $0.026 $0.028 n/a $0.026 

Utah $0.029 $0.033 $0.024 $0.029 $0.029 

Vermont $0.043 $0.041 $0.042 $0.037 $0.041 

Wisconsin n/a n/a $0.022 $0.015 $0.019 

Average $0.027 $0.029 $0.028 $0.030 $0.028 

Median $0.025 $0.026 $0.026 $0.026 $0.026 

Minimum $0.013 $0.014 $0.016 $0.015 $0.016 

Maximum $0.056 $0.050 $0.056 $0.051 $0.048 

2011$. 5% real discount rate. N/A means that we were unable to track down sufficient data for the 

calculation. Average for each year represents a varying number of states, so they are not directly 

comparable. 

For the four-year average values in the column furthest to the right, we find an overall 

national average of $0.028/kWh, and a range of $0.016 to $0.048/kWh. As pointed out in the 

Discussion section, these typical efficiency program costs compare very favorably to the 

typical costs of new electricity generation. 

The values in table 3 vary among states due to numerous factors such as structural 

differences in program types and share of savings by customer class. For example, portfolios 

with a larger share of savings from residential programs or low-income programs tend to 

have higher overall CSE values. (We present some data by customer class for several states 

later in this section.) On the other hand, program portfolios that rely heavily on low-cost 

lighting programs, or that have lower shares of savings from low-income programs, tend to 

have lower CSE values. An analysis by LBNL provides further insight into specific CSE 

values and ranges for different types of programs (LBNL 2014).

In addition, the eligibility and attribution of non-electricity savings from programs differ by 

jurisdiction, which can affect the cost of saved electricity. For example in Massachusetts, 

electric ratepayer funds are used to support investments in oil and propane energy savings.

Because the values in table 3 are developed using total electric spending (without adjusting 

for spending on oil and propane savings) and total electric savings, the cost per unit of 

electricity savings appears higher than it would if spending were adjusted for non-electricity 

savings.
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The CSE values in figure 1 and table 3 represent costs per net electricity savings, and they 

assume a 5% real discount rate. This rate is meant to be roughly consistent with the typical 

nominal utility WACC of about 7%. We also wanted to calculate and compare the values 

under different real discount rate assumptions. Table 4 presents values for 3%, 5%, and 7% 

real discount rates.

Table 4. CSE values under various real discount rate assumptions

3% real discount rate 5% real discount rate 7% real discount rate

Average $0.025 $0.028 $0.031

Median $0.023 $0.026 $0.029

Minimum $0.014 $0.015 $0.018

Maximum $0.043 $0.048 $0.054

These values represent aggregate 4-year averages(2009-2012) for all states.

Table 4 shows that a difference of 2% in the discount rate assumption can impact the CSE 

values by about 10-12%, which is minimal compared to the wide margin between energy 

efficiency portfolios and alternative energy options. For specific programs on the margins, 

however, the assumed rate can have an impact on whether programs are deemed cost

effective.

From a utility resource planning perspective, it is important that analysts use appropriate 

discount rates for energy efficiency and supply side resources, considering their relative 

risks and other characteristics, in any levelized cost analyses. As discussed earlier, planners 

should also consider explicitly modeling resource risk in their analysis of resource options.

All these results reflect energy savings reported at the meter, which is how most states 

report energy efficiency savings. However, as discussed earlier, the more appropriate metric 

for comparing costs to supply-side resources may be savings at the generator level, which

account for T&D line losses that are avoided by efficiency. EIA estimates a national average 

line loss factor of 7%.20 If we convert the savings values at the meter level to the generator

level, the average CSE value of $0.028/kWh would decrease by 7% to $0.026/kWh. No 

matter which method is chosen, the most important thing is that the assumptions be 

transparent and that avoided T&D line losses be factored into the cost-effectiveness analysis 

in some way.

First-Year Acquisition Costs

In addition to the CSE values, in figure 2 and table 5 we present the first-year program costs

(non-amortized), which are often called acquisition costs. As noted earlier, first-year costs 

 

20 See http://www.eia.gov/tools/faqs/faq.cfm?id=105&t=3
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can be useful for program budgeting purposes, but we caution that this metric is not 

reflective of the full resource value of efficiency.

Figure 2. Electricity energy efficiency program first-year acquisition costs by year

Table 5. First-year acquisition costs 

State 2009 2010 2011 2012

4-year

average 

(2009-

2012)

Arizona $0.13 $0.16 $0.18 $0.15 $0.15

California $0.28 $0.28 $0.39 n/a $0.32

Colorado $0.20 $0.26 $0.24 $0.24 $0.24

Connecticut $0.31 $0.35 $0.30 $0.37 $0.33

Hawaii $0.17 $0.19 $0.22 $0.30 $0.22

Illinois n/a n/a $0.16 n/a $0.16

Iowa $0.15 $0.15 $0.17 $0.15 $0.16

Massachusetts $0.49 $0.42 $0.36 $0.43 $0.42

Michigan $0.14 $0.13 $0.14 $0.15 $0.14

Minnesota $0.23 $0.30 $0.31 $0.28 $0.28

New Mexico $0.18 $0.17 $0.15 $0.13 $0.16

Nevada $0.11 $0.12 $0.13 $0.17 $0.13

New York $0.21 $0.21 $0.21 n/a $0.21

Oregon $0.24 $0.21 $0.24 $0.23 $0.23

Pennsylvania n/a n/a $0.14 n/a $0.14

Rhode Island n/a $0.35 $0.37 $0.41 $0.38
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State 2009 2010 2011 2012

4-year

average 

(2009-

2012)

Texas $0.20 $0.20 $0.22 n/a $0.20

Utah $0.27 $0.28 $0.20 $0.23 $0.24

Vermont $0.35 $0.33 $0.34 $0.31 $0.33

Wisconsin n/a n/a $0.19 $0.13 $0.16

Average $0.23 $0.24 $0.23 $0.24 $0.23

Median $0.21 $0.21 $0.21 $0.23 $0.21

Minimum $0.11 $0.12 $0.13 $0.13 $0.13

Maximum $0.49 $0.42 $0.39 $0.43 $0.42

2011$ per first-year net kWh at meter. N/A means that we were unable to track down 

sufficient data for the calculation. Average for each year represents a varying number of 

states, so they are not directly comparable. Values vary among states due to numerous 

factors such as structural differences in program types and share of savings by customer 

class. 

Participant Costs

Program administrators use estimates of participants’ costs for energy efficiency measures 

as inputs to the TRC test and the SCT.21 Program administrators typically estimate 

participant costs either through deemed measure costs or for custom-based programs 

through actual reporting by customers.

Unfortunately most program administrators do not explicitly include participant cost 

estimates or participant benefits in their annual reporting. However they are implicit in their 

TRC outcomes. It might be possible to use TRC values as compared to UCT values to derive 

estimates of participant costs, but this approach has obvious caveats: cost-benefit tests do 

not make transparent all the annual values or discount rate assumptions, and they use a net 

present value basis. Further work should more fully explore participant cost and associated 

participant benefit estimates.

Several program administrators did report estimates of annual participant contributions, or 

made it possible to derive participant costs as the difference between incremental measure 

costs and incentives paid to participants. Although limited, we report these estimates along 

with program costs for the states listed in table 6 below, but we caution that there are 

significant caveats. 

 

21 Participant costs are the additional costs incurred by program participants net of any incentives paid to them 

by program administrators.
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Table 6 shows that the ratio of participant costs to program costs varies significantly by 

state. The ratio depends largely on the nature of the programs, e.g., the level of participant 

rebates versus non-financial incentives including technical assistance and marketing. 

Table 6. Combined program and participant cost estimates 

State

Program cost 

estimate 

Ratio of participant 

costs to program 

costs

Participant cost 

estimate 

Combined program and 

participant cost 

estimate 

Hawaii $0.033 149% $0.049 $0.076

Illinois $0.016 115% $0.018 $0.041

Iowa $0.019 159% $0.030 $0.049

New York $0.020 262% $0.053 $0.073

Pennsylvania $0.018 159% $0.029 $0.043

Rhode Island $0.045 25% $0.011 $0.056

Wisconsin $0.019 118% $0.022 $0.041

Average for this 

dataset $0.024 141% $0.030 $0.054 

2011$ per kWh levelized

For these seven states, the ratio of participant costs to program costs ranges from 25% to 

262%, and the simple average is 141%. In other words, for every $1 invested by the program 

administrator, participants are estimated to spend on average an additional $1.41 on 

efficiency upgrades.

The sum of program costs and participant costs on average for these states is $0.054 per 

kWh levelized. However, given the limited dataset, this figure is highly uncertain and does 

not represent a national average. It is also important to recognize that this metric is not an 

appropriate comparison to the utility cost of supply-side resources, because it captures 

participant costs which are not incurred by utilities.22

The 2009 ACEEE review similarly collected participant costs for about six program 

portfolios and found that on average participants contributed $0.83 for every $1 invested by 

program administrators. Again, these values varied significantly across jurisdictions. The 

states included in the 2009 review were different from the ones in this review (only three 

states were included in both reviews), which explains the large difference between the 2009 

results and those presented in table 6. Overall, much caution is warranted in making 

comparisons among jurisdictions about participant costs.

 

22 The inclusion of participant costs in a cost-effectiveness test also requires incorporating participant non-energy 

benefits for a systematically balanced calculation. For example, see 

http://www.nhpci.org/publications/NHPC_EE-Screening-Coalition-Position-Paper-final_20131118.pdf.
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Benefit-Cost Ratios

Next we present the benefit-cost (B/C) ratios as reported by program administrators. While 

the CSE values represent only the cost side of the cost-effectiveness equation, the B/C ratios 

represent a more complete picture of how program costs compare to program benefits. It is 

important to note that the benefits side of the equation can also vary significantly from state 

to state. Benefits include avoided energy, capacity, and T&D costs for the UCT, as well as 

participant and other system-wide non-energy benefits for the TRC test. Moreover, as noted 

earlier, implementation of the TRC test is incomplete in many states, i.e., the range of 

benefits calculated can vary significantly. 

As in our review of CSE values, our goal is not to directly compare B/C ratios, but to 

present overall trends. Although most states conduct the TRC test when evaluating energy 

efficiency cost effectiveness, the results are not always presented clearly in reports.

Therefore the TRC results presented in table 7 reflect only 9 of the 20 states. The results 

show that energy efficiency benefits in these states exceed costs by a factor of 1.24 to 4.0. In 

other words, each dollar invested by program administrators and customers in energy 

efficiency measures yields $1.24 to $4.00 in benefits to all customers.

Table 7. Benefit-cost ratios for TRC tests

State 2009 2010 2011 2012

California 1.83 1.61 1.24 n/a

Colorado 3.66 2.87 2.47 2.09

Hawaii n/a 1.40 1.60 2.60

Illinois 2.15 2.84 2.24 n/a

Iowa 2.54 2.06 2.10 2.34

Massachusetts 3.28 3.11 4.00 3.50

New Mexico 1.57 2.20 1.78 2.63

Utah 1.99 1.68 1.95 2.00

Wisconsin n/a n/a 2.84 3.26

Minimum 1.57 1.40 1.24 2.00

Maximum 3.66 3.11 4.00 3.50 

Savings by Customer Class

Figure 3 displays the results of electricity savings in several jurisdictions by customer class 

for 2009-2012. (17 states readily reported savings by customer class.

23) Jurisdictions with the 

highest share of savings from residential customers are on the left, and those with the 

highest share of savings from business customers are on the right.

 

23 Some of the other jurisdictions reported savings at the program level, but did not aggregate by customer class.
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Figure 3. Electricity savings by customer class. Program administrators often report low-income programs as a separate category; we 

include these with residential savings. Most jurisdictions report savings in aggregate for residential customers and for business 

customers. Only Utah and Oregon readily reported commercial and industrial customer categories separately.

As shown in the column furthest to the right, the average savings by customer class amount 

to 45% from residential customers and 55% from business customers. However this ratio 

varies significantly by state; for example, the share of savings from residential programs 

ranges from 60% to 26%. There are several likely reasons for this variation. For example, the 

relative size of energy savings potential by customer class itself can differ from state to state, 

or regulators may require that a specific share of savings come from specific customer 

classes.

In general it appears that jurisdictions that are newer to broad-scale energy efficiency 

portfolios (e.g., Pennsylvania, Illinois, Arizona, and New Mexico) have a higher share of 

savings from residential customers, while states with more mature portfolios (e.g., 

Minnesota, Massachusetts, Oregon, and Rhode Island) have a higher share of savings from 

business customers. New program development tends to start with a large portion of 

funding to mass residential lighting and appliance programs, and a smaller portion to 

business programs, before launching into more comprehensive programs for business 

customers.

There is no optimal mix of savings by customer class because it may vary significantly by 

jurisdiction. Also, stakeholders must consider a number of factors in addition to cost

effectiveness (e.g., equity) to ensure that all customer segments benefit from efficiency 

programs. In sum, these data show that the portion of savings by customer class can vary 

significantly by state, and this is a likely factor in the overall average CSE values. See the 

section on Costs by Customer Class for further discussion.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Residential Business Commercial Industrial
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Measure Lifetime Estimates

Energy efficiency upgrades continue saving energy over the lifetime of the measure 

installed. The estimate of measure lifetime is an important factor in calculating the cost of 

energy efficiency resources. As with many metrics, states vary in their explicit reporting of 

this figure. Table 8 presents the average electricity measure lifetimes by customer class (if 

available) for several jurisdictions, either as they were explicitly reported or as we derived 

them by dividing lifetime energy savings estimates by annual energy savings estimates. For 

these jurisdictions, the average measure lifetime for residential programs is 8 years, for 

business programs, about 13 years, and for the overall portfolio, about 11 years.

Table 8. Average electricity measure lifetimes by state and customer class

State Residential Commercial/business Industrial All sectors

Arizona 7.3 13.4 n/a 9.8

California n/a n/a n/a 9.1

Connecticut 6.5 12.8 n/a 9.6

Hawaii 6.7 12.3 n/a 9.2

Massachusetts 8 13 n/a 11.6

Minnesota n/a n/a n/a 13.8

New Mexico 8 10 n/a 8.9

Oregon 10.6 13.5 9.5 11.2

Rhode Island 9.1 12.3 n/a 11.1

Utah n/a n/a n/a 11.3

Vermont 7.7 13.1 n/a 11

Wisconsin 9 12.4 n/a 11.4

Average 8.1 12.5 9.5 10.6 

Values for each state typically represent the average over the 2009-2012 program period, although data were not 

available for all years in each state.

Costs by Customer Class

We can discern some trends from the CSE results of electricity efficiency resources by 

customer class. First-year costs are comparable for both residential and business 

(commercial and industrial) programs at about $0.22/kWh. However, because business 

energy efficiency measures tend to have longer measure lifetimes (an average of 12.5 years 

in this electricity data set) than residential measures (8.1 years), the levelized CSE is on 

average lower for business program portfolios than for residential portfolios. We calculated 

electricity CSE values by customer class for 9 states as shown in figure 4 and table 9, and 
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identified an average CSE of $0.037/kWh for residential portfolios and $0.027/kWh for 

business portfolios.24

Figure 4. Electricity CSE by state and customer class, and average

Table 9. Electricity CSE by state and customer class 

State Residential Business All Sectors

Arizona $0.026 $0.016 $0.019

Connecticut $0.062 $0.038 $0.045

Iowa $0.028 $0.016 $0.019

New Mexico $0.022 $0.022 $0.022

Oregon $0.032 $0.025 $0.027

Rhode Island $0.063 $0.037 $0.045

Hawaii $0.033 $0.028 $0.031

Wisconsin $0.032 $0.016 $0.019

Vermont $0.039 $0.044 $0.041

Average $0.037 $0.027 $0.030

$ per kWh levelized

We selected states that had readily available data for all three components of this 

calculation: savings, costs, and measure lifetime by customer class. While the average 

difference between customer class portfolio costs is about $0.01/kWh higher for residential 

programs, figure 4 demonstrates that it can vary significantly by state. A couple of states 

 

24 Note that the overall average CSE for this limited set of states is $0.030/kWh, which is slightly higher than the 

complete data set average value of $0.028/kWh.

 $-

 $0.01

 $0.02

 $0.03

 $0.04

 $0.05

 $0.06

 $0.07

Residential Business
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have cost differences of about $0.02/kWh higher for residential programs, while other states 

exhibit very negligible differences, and a few states have business programs that cost more 

than residential portfolios. Note also that the residential portfolio includes low-income 

programs, which tend to have higher CSE values and therefore (depending on the size of 

the programs) will have an impact on the overall residential CSE values.

We did not review individual program CSE values; however it is worth noting that there is 

significant variation in CSE value by program type. A report by LBNL provides information 

at the program level (LBNL 2014). These results again demonstrate the significant variation 

among jurisdictions in CSE trends by customer class. The estimates of measure lifetime 

values in particular are a large factor in determining CSE values.

Costs by Type

Figure 5 breaks down efficiency program costs by type, including customer incentives, 

performance incentives, and non-incentive program costs such as marketing, EM&V, and 

administrative costs.

Figure 5. Electricity energy efficiency program costs by type. Some states did not explicitly define costs as customer 

incentives; for example, California uses the term “direct implementation costs” and Connecticut, “direct program costs.”

However we took these to mean customer incentives.

Since definitions of cost types vary from state to state, there is significant uncertainty in 

directly comparing states. In particular, the types of costs included in the non-incentive 

program category can vary significantly. For the 8 states shown in figure 5, for instance, 

non-incentive program costs range from about 15% to 40%. One example that might help 

explain this range is mass marketing-based programs. As programs ramp up marketing and 

outreach as a way to increase participation and spur market transformation, this type of 

spending would fall into non-incentive costs. However it might have the same if not higher 
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energy savings impact as spending on direct incentives. Spending categories may need to 

shift as next-generation efficiency programs develop.

CSE Relative to Electricity Savings Thresholds

The hypothesis that programs with higher savings also have higher CSE values has been 

suggested but not readily demonstrated. This idea is especially relevant because as program 

administrators face increasing energy savings targets, they fear that program costs will rise 

as they go after higher savings. To test this hypothesis, we compare CSE values for each 

jurisdiction with relative electricity savings thresholds, i.e., savings as a percentage of 

applicable retail electricity sales. Figure 6 shows the scatter plot of these results, where each 

dot represents an individual jurisdiction for an individual year. Note that we were not able 

to present these data for all states.

Figure 6. CSE values relative to electricity savings as a percentage of sales

We calculated the Pearson correlation coefficient (r) for this data set in Excel. This 

correlation coefficient is a measure of how well two data arrays are linearly related or 

dependent. Correlation tests do not indicate a causal relationship between two variables; 

rather, they measures the strength of a linear association. The correlation coefficient may 

range from +1.0 to -1.0, where 1 is a total positive correlation, 0 is no correlation, and -1 is a 

total negative correlation. An r value of greater than 0.7 is generally regarded as strong, 

whereas an r value of less than 0.3 is generally regarded as weak. Values in between are 

considered moderate. However these general guidelines should not be regarded as strict 

rules; the strength ascribed to a particular value depends on the context and purpose of the 

calculation. Studies that use scientific data, for example, may require much higher values 

than social science data to indicate strength in correlation.

 $-

 $0.010

 $0.020

 $0.030

 $0.040

 $0.050

 $0.060

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%

CSE ($ per kWh)

Electricity Savings as % of Retail Savings 
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The r value for the data set in figure 6 is 0.27, which indicates a positive, but low or weak 

correlation between CSE and electricity savings as a percentage of sales. These findings cast 

doubt on the hypothesis that programs with higher electricity savings levels are associated 

with higher CSE values. In fact, other analysis suggests that CSE values may decrease as 

savings levels increase, due to factors such as economies of scale (Takahashi and Nichols 

2008). Our findings indicate that many robust program portfolios can exceed and are 

exceeding 1% or 1.5% savings as a percentage of sales while maintaining a cost-effective 

portfolio.

While these general findings are notable, there are many differences in the data points. For 

example, individual jurisdictions may have different program types or share of savings by 

customer class. Future work should examine trends over time for individual jurisdictions 

and within regions.

NATURAL GAS

The review includes natural gas energy efficiency program data for 10 states as shown in 

table 2 above. 

Cost of Saved Energy

Figure 7 shows CSE results by jurisdiction and year.

Figure 7. Natural gas CSE results by year. Each dot represents average costs for each state in a given year. 2011$ per levelized net therm

at site. Assumes a 5% real discount rate.

Table 10 shows CSE values by state for each year, as well as the average, median, minimum, 

and maximum values for each year across the 10 jurisdictions, and for the average of 2009-

2012. The CSE ranges from $0.15 per therm to $0.71 per therm across the time period, with a 

four-year average of $0.35 per therm.
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Table 10. Natural gas efficiency CSE results by state

State 2009 2010 2011 2012

4-year

average 

(2009-

2012)

Colorado $0.39 $0.42 $0.37 $0.29 $0.37

Connecticut $0.37 $0.42 $0.35 $0.38 $0.38

California $0.32 $0.52 $0.49 n/a $0.44

Iowa $0.32 $0.34 $0.38 $0.34 $0.34

Massachusetts $0.43 $0.58 $0.71 $0.64 $0.59

Michigan $0.26 $0.25 $0.22 n/a $0.25

Minnesota $0.15 $0.22 $0.22 $0.20 $0.20

Oregon $0.47 $0.32 $0.34 $0.36 $0.37

Rhode Island n/a $0.38 $0.42 $0.56 $0.45

Wisconsin n/a n/a $0.11 $0.09 $0.10

Average $0.34 $0.38 $0.36 $0.36 $0.35

Median $0.34 $0.38 $0.36 $0.35 $0.37

Minimum $0.15 $0.22 $0.11 $0.09 $0.10

Maximum $0.47 $0.58 $0.71 $0.64 $0.59

2011$ per therm at site. 5% real discount rate. N/A means that we were unable to track 

down sufficient data for the calculation. Average for each year represents a varying number of 

states, so they are not directly comparable. Values vary among states due to numerous 

factors such as structural differences in program types and share of savings by customer 

class.

First-Year Acquisition Costs

Figure 8 shows the results of the first-year costs by jurisdiction and year.
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Figure 8. Natural gas first-year acquisition costs by year. Each dot represents average costs for each state in a given year. 

Table 11 shows the average, median, minimum, and maximum values for each year across 

the 9 jurisdictions, and for the average of 2009-2012. The first-year acquisition cost ranges 

from $1.37 per therm to $6.97 per therm across the time period, with an overall average of 

$3.73 per therm.

Table 11. Natural gas efficiency first-year cost results

2009 2010 2011 2012

4-year

average 

(2009-

2012)

Colorado $4.26 $4.56 $4.00 $3.1 $3.98

Connecticut $4.11 $4.55 $3.92 $4.6 $4.30

California $2.20 $1.86 $1.76 n/a $1.94

Iowa $3.45 $3.64 $4.12 $3.74 $3.74

Massachusetts $4.30 $6.25 $6.97 $5.99 $5.88

Michigan $2.84 $2.75 $2.40 n/a $2.66

Minnesota $1.37 $1.99 $2.08 $2.01 $1.86

Oregon $6.37 $4.16 $3.95 $4.17 $4.66

Rhode Island n/a $4.00 $4.07 $5.69 $4.59

Wisconsin n/a n/a $1.13 $0.95 $1.04

Average $3.61 $3.75 $3.70 $4.19 $3.73

 $-

 $1.00

 $2.00

 $3.00

 $4.00

 $5.00

 $6.00

 $7.00

 $8.00

2009 2010 2011 2012 First-Year Cost (2011$ per therm- saved)
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2009 2010 2011 2012

4-year

average 

(2009-

2012)

Median $3.78 $4.00 $3.95 $4.17 $3.98

Minimum $1.37 $1.86 $1.76 $2.01 $1.86

Maximum $6.37 $6.25 $6.97 $5.99 $5.88

$ per therm. N/A means that we were unable to track down sufficient data for the calculation. 

Average for each year represents a varying number of states, so they are not directly 

comparable. Valuesvary among states due to numerous factors such as structural differences 

in program types and share of savings by customer class.

Measure Lifetimes 

We also collected gas efficiency measure lifetimes overall and by customer class, as 

presented in table 12.

Table 12. Average natural gas measure lifetimes by state and customer class

State Residential Commercial/business Industrial All sectors

California n/a n/a n/a 17.6

Connecticut 18.0 13.9 n/a 17.1

Massachusetts 13.2 12.9 n/a 13.1

Minnesota n/a n/a n/a 13.2

Oregon 23.1 18.2 14.0 19.8

Rhode Island 19.1 12.1 n/a 14.4

Vermont 18.1 17.6 n/a 18.0

Wisconsin 24.2 13.3 n/a 15.4

Average 19.3 14.7 14.0 16.1 

Average values for each state typically represent the average over the 2009-2012 program period, 

although data were not available for all years in each state. For example Massachusetts data represent 

2012 only, and Wisconsin data represent 2011-2012 average.

The average measure lifetime is about 16 years overall. Unlike electricity measures, which 

tend to have longer lifetimes for business than for residential measures, natural gas 

efficiency measure lifetimes tend to be longer for residential measures. This is likely due to 

the prevalence of equipment replacement and residential building shell measures for 

residential programs.

Due to the limited available data, we did not calculate CSE values by customer class for 

natural gas efficiency programs.
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Discussion and Recommendations

The results of this review provide a large data set that we can draw on for a discussion of 

energy efficiency program costs. First we discuss program costs in terms of cost

effectiveness and as compared to supply-side options. Second, we discuss issues related to

the consistency and transparency of energy efficiency reporting, and we make 

recommendations for improvements. Third, we discuss areas for further research that can 

build upon the findings of this review.

ENERGY EFFICIENCY COSTS COMPARED TO SUPPLY-SIDE OPTIONS

This review finds that energy efficiency programs are clearly the least-cost resource option 

compared to new energy supply resources. Here we discuss the results of our efficiency 

program cost review compared to typical costs for supply-side resource. A couple of

important caveats are worth noting. First, we do not try to conduct a new cost-benefit 

analysis here; rather, we aim to provide a high-level discussion. Energy efficiency offers 

multiple benefits to utilities and program administrators—as well as to society and to 

participants—which we do not analyze for this study. Second, this discussion compares 

efficiency program costs to indicators of current avoided energy- and capacity-related costs.

A complete cost-benefit analysis compares the costs of efficiency programs to forecast

avoided energy costs, because efficiency measures continue to provide energy savings over 

their useful lifetimes. Examining forecasted avoided energy costs would show additional 

benefits if avoided energy costs are expected to increase in future years.

Electricity

Figure 9 shows the CSE results from this analysis alongside data from Lazard, an energy 

industry analysis firm, for national averages of new electricity generation options (Lazard 

2013). On a levelized cost basis, new electricity energy efficiency programs cost about onehalf to one-third as much as new electricity generation resources.
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Figure 9. Levelized costs of electricity resource options. Source: Energy efficiency data represent the results of this analysis for utility 

program costs (range of four-year averages for 2009-2012); supply costs are from Lazard 2013.

The costs for all resources in figure 9 are presented as a range, which is indicative of the 

variability and uncertainty implicit in any energy resource option for new electricity 

generation. The results of our energy efficiency program cost review may at first seem to 

display an overly wide variation across states; however, when seen next to supply-side 

options, this variation is not unlike what we find in other resource options.

Comparing efficiency program costs to other new electricity resource options on a levelized 

cost basis provides useful context. However it does not tell the complete cost-effectiveness 

story for energy efficiency. When done properly, efficiency cost-benefit analysis should be 

more comprehensive. The utility cost test, for example, compares efficiency costs to the 

utility’s avoided energy-related costs and capacity-related costs (as well as avoided T&D

and other benefits to utilities). States use different methodologies for calculating avoided 

costs. Due to differences in methodology, economics, and market structures, avoided costs 

can vary significantly by jurisdiction, and may represent various mixes of the resources 

shown in figure 9. A complete utility cost-test analysis should consider additional benefits 

to utilities such as avoided T&D, wholesale price mitigation impacts, avoided 

environmental compliance costs, and other non-energy benefits. 
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In addition, levelized annual costs as shown in figure 9 do not reflect the added value of 

energy efficiency resources at certain periods of time during the year. For example, avoided 

energy costs can vary significantly between seasons and between peak and non-peak hours.

Energy efficiency measures that reduce demand during peak periods can result in higher 

benefits.

The TRC and societal cost-effectiveness tests also include the broader benefits that efficiency 

provides to participants and to society, which are significant and present an even more 

complete view of the benefits of efficiency. (However as discussed earlier, in practice the 

TRC test is often incomplete when it does not include full participant benefits.) Our review 

of the TRC ratios reported by several states finds that the benefits of efficiency exceed costs 

by a factor of about 1.2 to 4.0. These results further demonstrate that the benefits of 

efficiency far exceed the costs.

Natural Gas

Average natural gas commodity prices have fallen significantly in recent years, which has 

put pressure on gas program administrators to keep costs below avoided costs. Our analysis 

finds that natural gas energy efficiency programs remain a low-cost and cost-effective

resource at an average portfolio cost of $0.35/therm across 10 states. This average value is 

lower than the average citygate price of natural gas of $0.49/therm nationally in 2013 (EIA 

2014).25 However the avoided gas commodity cost does not tell the complete story of gas 

energy efficiency benefits. In addition to the commodity cost of gas, avoided costs to utilities 

can also include avoided distribution and transmission costs, peak demand benefits, 

hedging against fuel price volatility, and environmental benefits. Adding these benefits of 

efficiency savings further tilts the scale in favor of efficiency as a cost-effective resource.

In addition, natural gas avoided costs vary significantly across the country due to 

methodology and market structure differences, and they are subject to the uncertainty 

around future gas prices. For example, we collected a sample of recent (2012 and 2013) 

avoided natural gas costs, both current values and forecasts, for a handful of jurisdictions 

across the country. We identified a range of $0.37/therm to $1.019/therm for current and 

forecasted avoided gas costs. In comparison, we identified a range of natural gas efficiency 

portfolios of about $0.10 to $0.70/therm, very favorable values compared to avoided costs.

And looking at the average gas efficiency program CSE of $0.35/therm, we can see that

energy efficiency remains cost effective compared to average gas prices.

Efficiency Cost Trends

Energy efficiency program costs appear to be holding steady as the least-cost resource. The 

average utility CSE value in this review ($0.028/kWh) is only slightly higher than the 

average CSE 2009 review value ($0.025/kWh), and slightly lower than the 2004 value 

($0.030/kWh). Similarly, the average natural gas efficiency program cost in the current data 

set ($0.35/therm) is comparable to the 2009 review value of $0.37/therm. Figure 10 displays 

 

25 Per EIA, the citygate price is the “point or measuring station at which a distributing gas utility receives gas 

from a natural gas pipeline company or transmission system.” 
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37

the annual results from the 2009 review and from this analysis for average, minimum, and 

maximum CSE values. Annual results from the 2004 review were not available.

Figure 10. Utility cost of saved energy 2005-2012. Source: Data for 2005-2008 are from Friedrich et al. 2009 (designated by unfilled 

markers). Data for 2009-2012 are from this analysis. 

Some caution is warranted in drawing direct comparisons between the results of the two 

studies, since they used different data sets (i.e., the number and specific jurisdictions 

included) and slightly different methodologies. For example, the 2009 study did not review 

whether the CSE captured net or gross energy savings, and it did not include utility 

shareholder incentives, both of which we addressed in the current analysis. In addition, the 

current review calculates all CSE values, whereas the 2009 study relied on a combination of 

reported and calculated values. As discussed in the next section, there is a need for further 

analysis on CSE trends.

What is clear, however, is that the available data refute the claims that the low-hanging fruit 

has been picked and that the future availability and cost effectiveness of energy efficiency 

are in doubt. Data from a large number of diverse jurisdictions across the nation show that 

energy efficiency has consistently remained the lowest-cost resource for the past decade, 

even as the amount of captured energy efficiency has increased significantly.

CONSISTENCY AND TRANSPARENCY IN ENERGY EFFICIENCY REPORTING

Throughout this report we have discussed the challenges in energy efficiency reporting 

around the country. All states should take steps toward improving consistency and 

transparency in reporting. To this end, we recommend that utilities, regulators and program 

administrators in a given state (and perhaps also at a regional and national level) discuss 

these issues and work toward adopting best reporting practices. Guidelines are already 

available for program administrators interested in improving the transparency and 

consistency of their reporting metrics. For stakeholders interested in detailed guidelines and 
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38

templates for reporting, we suggest the NEEP Common Statewide Energy Efficiency Reporting 

Guidelines and Regional Energy Efficiency Database (REED) (NEEP 2010, 2013), and Energy 

Efficiency Program Typology and Data Metrics: Enabling Multi-State Analyses through the Use of 

Common Terminology (LBNL 2013).

Rather than offering detailed guidelines, the following section makes recommendations 

around several common issues that program administrators should address to improve

consistency and transparency.

Regularize Location and Frequency of Reporting

First and foremost, annual program reports and evaluations should be easily accessible on a 

common website. They should also follow a consistent annual schedule if possible, or 

provide public notification of schedule and availability. The website may be an individual 

program administrator’s site, a common docket established by the commission, or an 

independent advisory group website. Regulators or advisory groups should require at least 

some minimum threshold of reporting and provide sample templates that build on best 

practices such as those laid out by NEEP. In cases where there are multiple utilities or 

program administrators reporting, it makes sense and is in the interest of all stakeholders to 

have one dedicated entity to aggregate key metrics across all territories.

Improve Transparency of Energy Efficiency Metrics and Assumptions

To improve overall transparency, we recommend that program administrators and 

regulators adopt or improve on measures such as the following: 

 Report energy efficiency program portfolio spending and impacts separately from 

demand response and renewable energy impacts.

 Separate electricity and natural gas program spending and savings. For combined 

programs, develop methodologies for attributing spending and savings to gas or 

electric.

 Report estimated participant costs by customer class.

 Indicate whether electricity savings are reported at site or at generation. If at 

generation, make clear the assumption of T&D line losses so they can be converted to 

site.

 Identify whether energy savings are net or gross, and what assumptions are used.

 Provide a succinct but transparent description of the methodologies used to estimate 

gross and/or net savings, with links to more detailed information.

 If the emphasis is on cumulative (i.e., multiyear) energy savings and costeffectiveness impacts, provide incremental annual impacts to indicate trends over 

time and facilitate comparisons with other jurisdictions.

Expand Reporting and Disaggregation of Key Metrics 

More often than not, energy efficiency reporting leaves out critical metrics or assumptions 

that are necessary to calculate the cost of saved energy, or at least does not report aggregate 

values across customer classes. We recommend reporting measures such as the following:
BEST VALUE FOR AMERICA’S ENERGY DOLLAR ©ACEEE

39

 Report both net and gross energy savings values.

 Report measure lifetime estimates.

 Disaggregate all data by customer class (e.g., residential, commercial, and 

industrial). Most jurisdictions currently disaggregate business customers into 

commercial and industrial; however we recommend that programs disaggregate 

data in a way that furthers program development (e.g., commercial versus industrial 

customers, or small versus large business customers).

 Disaggregate cost data at least by the following: customer incentives, non-incentive 

program costs, and performance/shareholder incentives.

FURTHER RESEARCH

This review presents a large quantitative dataset combined with qualitative findings on 

energy efficiency cost-effectiveness metrics and reporting practices. However we offer only 

a very limited initial statistical analysis of trends. Further analysis is needed to discern 

trends in CSE values over time and the relative impact of various metrics on CSE values. As 

for trends over time, many analysts have hypothesized that the cost of saved energy for 

programs will increase as program administrators raise energy savings levels. Yet an initial 

correlation analysis in this study (for electricity programs only) finds only a weak 

correlation between CSE values and electricity savings levels and therefore casts doubt on

the broad notion that high savings are associated with high CSE values.

26 However

correlation analyses of CSE trends over time across jurisdictions are difficult and may 

provide incomplete results because of fundamental differences among program portfolio

structures and reporting consistency. Further research should delve into this question, 

perhaps examining individual jurisdictions or regions.

The relative impact of different variables on CSE values is also an important area for further 

statistical and qualitative research. In this review we present numerous metrics that may

have a direct impact on the cost of efficiency, e.g., the share of savings by customer class, or

the types of programs offered. Also of interest is the impact of avoided costs on CSE within 

a jurisdiction, which we hypothesize should be a significant indicator of CSE values. (We 

did not conduct this analysis because we did not collect avoided costs data.) If program 

administrators must pass cost-effectiveness screening up to the point that efficiency 

programs cost more than the marginal unit of energy supply (i.e., avoided costs), that would 

allow for a higher ceiling on program costs in jurisdictions with higher avoided costs.

Similarly, labor and capital costs may have a direct influence on the cost of energy efficiency 

programs.

Conclusion

This analysis finds that energy efficiency is clearly holding steady as the least-cost energy 

option that provides the best value for America’s energy dollar. At an average cost of 2.8 

cents per kWh, electricity efficiency programs are one half to one third the cost of the 

 

26 Note that this is different from the notion that total program dollar costs will increase to meet higher savings 

levels. Total program costs may increase, but the levelized CSE for the efficiency resources can hold steady.
BEST VALUE FOR AMERICA’S ENERGY DOLLAR ©ACEEE

40

alternative of building new power plants. Natural gas energy efficiency programs also 

remain a least-cost option at an average cost of 35 cents per therm, which is less than the 

average natural gas commodity price of 49 cents per therm in 2013. These data represent a 

large number of diverse jurisdictions across the nation.

The data show that energy efficiency has remained consistent as the lowest-cost resource 

over the past decade even as the amount of energy efficiency being captured has increased 

significantly. Energy efficiency also provides additional benefits beyond avoided energy 

costs, including reductions in water and fuel usage, avoided T&D costs, price mitigation 

effects in wholesale markets, and non-energy benefits to society such as reduced pollution 

and job creation. As utility and state planners face increasing uncertainty and rising supply 

costs in their long-term planning (including fuel-price volatility and the need to address the 

environmental impacts of power generation), they should look to energy efficiency as a 

reliable and consistent “first fuel” in their loading order of energy options. 

The need increases for high-quality and consistent data across the country as efficiency 

gains even wider adoption and traction as the least-cost energy resource option. In this 

analysis we found that jurisdictions collect and report a wealth of data and information on 

efficiency programs. However we also found that the collection and comparison of energy 

efficiency cost data across the nation face numerous challenges, including variation in 

reporting formats, nomenclature, and frequency. All states should take steps toward 

improving consistency and transparency in reporting. Finally, further work should explore 

the relative impact of different variables on CSE values, as well as trends over time for 

individual jurisdictions. 
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References

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C. Cui. 2013. The 2013 State Energy Efficiency Scorecard. Washington, DC: American 

Council for an Energy-Efficient Economy. http://aceee.org/research-report/e13k.

EIA (U.S. Energy Information Administration). 2014. “Natural Gas Prices.” Accessed March 

2014. http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_nus_a.htm)

Friedrich, K., M. Eldridge, and D. York. 2009. Saving Energy Cost-Effectively: A National 

Review. Washington, DC: ACEEE.

Kushler, M. and C. Neme. 2010. “Is it Time to Ditch the TRC? Examining Concerns with 

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Study on Energy Efficiency in Buildings. Washington, DC: ACEEE.

Kushler, M., S. Nowak, and P. Witte. 2012. A National Survey of State Policies and Practices for 

the Evaluation of Ratepayer-Funded Energy Efficiency Programs. Washington, DC: ACEEE.

———. 2014. Examining the Net Savings Issue: A National Survey of State Policies and Practices in 

the Evaluation of Ratepayer-Funded Energy Efficiency Programs. Washington, DC: ACEEE.

Kushler, M., D. York, and P. Witte. 2004. Five Years In: An Examination of the First Half-Decade 

of Public Benefits Energy Efficiency Policies. Washington, DC: ACEEE.

Lazard. 2013. "Levelized Cost of Energy Analysis: Version 7.0." New York: Lazard.

LBNL (Lawrence Berkeley National Laboratory). 2013. Energy Efficiency Program Typology 

and Data Metrics: Enabling Multi-State Analyses through the Use of Common Terminology.

Berkeley: LBNL.

———. 2014. The Program Administrator Cost of Saved Energy for Utility Customer-Funded Energy 

Efficiency Programs in the United States. Berkeley: LBNL.

Morgan, P. 2012. A Decade of Decoupling for US Energy Utilities: Rate Impacts, Designs, and 

Observations. Revised February 2013. Accessed March 2014. 

http://switchboard.nrdc.org/blogs/rcavanagh/Decoupling%20report%20Final%20Feb

%202013%20-%20pdf%20%282%29.pdf

National Action Plan for Energy Efficiency. 2008. Understanding Cost-Effectiveness of Energy 

Efficiency Programs: Best Practices, Technical Methods, and Emerging Issues for Policy-Makers. 

Prepared by Energy and Environmental Economics, Inc. and Regulatory Assistance 

Project. http://www.epa.gov/cleanenergy/documents/suca/cost-effectiveness.pdf

NEEP (Northeast Energy Efficiency Partnership). 2010. Common Statewide Energy Efficiency 

Reporting Guidelines. Version 1.0. Lexington, MA: NEEP. 

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———. 2013. Regional Energy Efficiency Database (REED).Accessed October 2013. 

http://www.neep-reed.org/Disclaimer.aspx?Source=http://www.neepreed.org/default.aspx

Northwest Power and Conservation Council. 2010. Sixth Northwest Conservation and Electric 

Power Plan. Appendix N. Accessed March 2014. 

http://www.nwcouncil.org/media/6332/SixthPowerPlan_Appendix_N.pdf

NREL (National Renewable Energy Laboratory). 2013. The Uniform Methods Project: Methods 

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http://www.raponline.org/document/download/id/6368

Takahashi, K. and D. Nichols. 2008. “The Sustainability and Costs of Increasing Efficiency 

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Appendix A. Data Sources by State and Program Administrator

State and administrator Data sources

Arizona: Arizona Public 

Service Company (APS) 

DSM semiannual reports with annual data for 2009, 2010, 2011, and 

2012. Published in March each year.

California: Southern 

California Gas; Southern 

California Edison; Pacific 

Gas & Electric; San Diego 

Gas & Electric

Utility annual reports available for 2009, 2010, and 2011 from 

http://eega.cpuc.ca.gov/. Published in May.

Colorado: Xcel Energy Xcel Energy Colorado Demand-Side Management (DSM) Annual Status 

Reports. Published in April.

http://www.xcelenergy.com/About_Us/Rates_&_Regulations/Regulato

ry_Filings/CO_DSM

Connecticut: Connecticut 

Energy Efficiency Fund

CEEF Annual Legislative Reports for 2009, 2010, 2011, and 2012.

Published from February to April. 

http://www.ctenergyinfo.com/about/eeboard/annualreports

Hawaii: Hawaii Energy Hawaii Energy PY12 Annual Report, October 1, 2013. also PY11, 

PY10, and PY9 reports. http://www.hawaiienergy.com/informationreports

Illinois: Ameren Illinois 

Utilities; ComEd, and DCEO

Ameren: ActOnEnergy Energy Efficiency and Demand Response 

Results (PY 1). Portfolio Cost-Effectiveness Evaluation (PY 3).

ComEd: Evaluation Reports Prepared by Summit Blue/Navigant for PY 

1 (2009), PY 2 (2010), and PY 3 (2011) (savings data and TRC ratios).

ComEd PY3 annual report to ICC (cost data for 2011).

Some reports available at http://ilsag.org/.

Iowa: MidAmerican Energy 

and Alliant 

Energy/Intrastate Power & 

Light

Energy Efficiency Annual reports for 2009, 2010, 2011, and 2012.

Published in May. 

http://www.state.ia.us/government/com/util/energy/energy_efficienc

y/ee_plans_reports.html

Massachusetts: All 

investor-owned utilities

2011 data: Northeast Energy Efficiency Partnership’s Regional Energy 

Efficiency Database (REED).

2012, 2010, and 2009 data: individual utility reports available from 

www.ma-eeac.org

2011 and 2012 TRC results: “Statewide Electric Results Master 

Summary” spreadsheets. www.ma-eeac.org

Michigan: All utilities Michigan PSC. 2012. Report on the Implementation of P.A. 295 Utility 

Energy Optimization Programs. November 30, 2012. Data for 2009, 

2010, and 2011.

Michigan PSC. 2013 Report on the Implementation of P.A. 295 Utility 

Energy Optimization Programs. November 26, 2013.

Minnesota: Xcel Energy Xcel Energy annual Status Reports available from 

https://www.edockets.state.mn.us/EFiling/home.jsp for 2009, 2010, 

2011, and 2012 program years
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State and administrator Data sources

New Mexico: Public 

Service Company of New 

Mexico (PNM) and 

Southwestern Public 

Service Company (SPS)

PNM: PNM Energy Efficiency Program Annual Report (available for 

2009, 2010, 2011, and 2012), published annually from March-June.

SPS: SPS 2009 Energy Efficiency and Load Management Annual 

Report (August 1, 2010). Only 2009 report was readily available.

Note: New Mexico’s TRC results reported only for PNM.

Nevada: NV Energy 

(merger of Nevada Power 

Company (NPC) & Sierra 

Pacific Power Company 

(SPPC); but separate EE 

reporting still)

NPC: DSM Status Update Reports for 2009, 2010, 2011, and 2012

SPPC: DSM Status Update Reports for 2009, 2010, 2011, and 2012

New York: NYSERDA Cumulative data for 2006-2011: NYSERDA, New York's System 

Benefits Charge Programs Evaluation and Status Report: Year Ending 

December 31, 2011 Report to the Public Service Commission (March 

2012) (Revised April 2012)

Oregon: Energy Trust of 

Oregon

ETO Annual Reports for 2009, 2010, 2011, and 2012

http://energytrust.org/About/policy-and-reports/Reports.aspx

Pennsylvania: Electric 

Distribution Companies 

(EDCs)

Act 129 Market Potential Study, with impacts for PY 1 (June 2009-May 

2010) and PY 2 (June 2010–May 2011) 

http://www.puc.pa.gov/electric/pdf/Act129/Act129-

PA_Market_Potential_Study051012.pdf

Rhode Island: National 

Grid Electric and Natural 

Gas

National Grid Year-End Reports for 2010, 2011, and 2012.

Also for 2011 data: Northeast Energy Efficiency Partnership Regional 

Energy Efficiency Database (REED).

Texas: Electric IOUs Frontier Associates; Energy Efficiency Accomplishments of Texas 

Investor-Owned Utilities. Annual reports for 2009, 2010, and 2011

http://www.texasefficiency.com/index.php/publications/reports

Utah: Rocky Mountain 

Power (PacifiCorp)

PacifiCorp, Annual Energy Efficiency and Peak Reduction Report: Utah.

Annual reports for 2009, 2010, 2011, and 2012

Vermont: Efficiency 

Vermont (Vermont Energy 

Investment Corporation)

Annual Savings Claim Reports 

http://www.efficiencyvermont.com/about_us/information_reports/ann

ual_reports.aspx

Wisconsin: Focus on 

Energy

Focus on Energy evaluation reports for 2011 and 2012: Calendar Year 

2011 Evaluation Report (October 31, 2012), Calendar Year 2012 

Evaluation Report (April 30, 2013)