The Program Administrator 

Cost of Saved Energy for Utility 

Customer-Funded Energy 

Efficiency Programs

Megan A. Billingsley, Ian M. Hoffman, Elizabeth Stuart, 

Steven R. Schiller, Charles A. Goldman, Kristina LaCommare

Environmental Energy Technologies Division

March 2014

The work described in this report was funded by the National Electricity Delivery 

Division of the U.S. Department of Energy’s Office of Electricity Delivery and 

Energy Reliability under Lawrence Berkeley National Laboratory Contract No. 

DE-AC02-05CH11231. 

ERNEST ORLANDO LAWRENCE

BERKELEY NATIONAL LABORATORY

LBNL-6595E
The Program Administrator Cost of Energy Saved for Utility 

Customer-Funded Energy Efficiency Programs

Prepared for the

U.S. Department of Energy 

National Electricity Delivery Division of the Office of Electricity Delivery and Energy 

Reliability

Principal Authors 

Megan A. Billingsley, Ian M. Hoffman, Elizabeth Stuart, Steven R. Schiller, Charles A. 

Goldman, Kristina LaCommare

Ernest Orlando Lawrence Berkeley National Laboratory

1 Cyclotron Road, MS 90R4000 

Berkeley CA 94720-8136 

March 2014 

The work described in this report was funded by the National Electricity Delivery Division of the U.S. 

Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley 

National Laboratory Contract No. DE-AC02-05CH11231. 

i 

LBNL-6595E
Disclaimer

This document was prepared as an account of work sponsored by the United States Government. 

While this document is believed to contain correct information, neither the United States 

Government nor any agency thereof, nor The Regents of the University of California, nor any of 

their employees, makes any warranty, express or implied, or assumes any legal responsibility for 

the accuracy, completeness, or usefulness of any information, apparatus, product, or process 

disclosed, or represents that its use would not infringe privately owned rights. Reference herein 

to any specific commercial product, process, or service by its trade name, trademark, 

manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, 

recommendation, or favoring by the United States Government or any agency thereof, or The 

Regents of the University of California. The views and opinions of authors expressed herein do 

not necessarily state or reflect those of the United States Government or any agency thereof, or 

The Regents of the University of California. 

Ernest Orlando Lawrence Berkeley National Laboratory is an equal opportunity employer. 

ii
Acknowledgements

The work described in this report was funded by the National Electricity Delivery Division of the 

U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under 

Lawrence Berkeley National Laboratory Contract No. DE-AC02-05CH11231. 

The authors would like to first and foremost thank Larry Mansueti and Cyndy Wilson of the U.S. 

Department of Energy for their support of this work. We would also like to thank Caitlin 

Callaghan, Judy Greenwood, George Edgar, Scott Johnstone, Barbara Alexander, Maggie 

Molina, Tim Woolf, Rich Sedano, Howard Geller, Nick Hall, M. Sami Khawaja, Stan Price, Lara 

Ettenson, David Goldstein, Peter Miller, Cecily McChalicher, Jim Lazar, Natalie Mims, and

Peter Narog for providing comments on a draft of this report. We also thank Anthony Ma for 

assistance with graphic design and Dana Robson for copyediting. Any remaining omissions and 

errors are, of course, the responsibility of the authors. 

iii
Table of Contents 

Acknowledgements ........................................................................................................................ iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ................................................................................................................................ vi

Acronyms and Abbreviations ...................................................................................................... viii

Executive Summary ....................................................................................................................... ix

Results ........................................................................................................................................ xi

Observations and Recommendations on Reporting ................................................................. xvi

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

1.1 Assessing Energy Efficiency as a Resource ........................................................................1

1.2 Objectives and Scope ...........................................................................................................2

1.3 Report Organization .............................................................................................................3

2. Approach .....................................................................................................................................4

2.1 Data Summary .....................................................................................................................4

2.2 Program Typology and Standardized Definitions ................................................................7

2.3 Challenges in Consistent and Standardized Reporting of Program Data ..........................11

2.4 Calculating and Using the Cost of Saved Energy ..............................................................12

2.4.1 Levelized Cost of Saved Energy ................................................................................14

2.5 Treatment and Adjustments for Missing Data ...................................................................15

2.5.1 Program Average Measure Lifetime ..........................................................................16

2.5.2 Cost Data for Combined-Fuel Programs....................................................................18

2.5.3 End-Use versus Source and Busbar Energy Savings .................................................18

3. Results—Utility Customer-Funded Programs: Costs and Savings ...........................................19

3.1 Energy Efficiency Program Administrator Expenditures and Savings..............................19

3.1.1 Electric Programs .......................................................................................................19

3.1.2 Gas Program Expenditures and Savings ....................................................................25

3.2 Observations on the Cost of Saved Energy........................................................................27

3.2.1 National Observations ................................................................................................27

3.2.2 Sector and Program Level Observations for Electricity Efficiency Programs ..........30

3.2.3 Regional Observations in Electricity Efficiency Programs .......................................35

3.2.4 Sensitivity Analysis: Impact of Measure Lifetime ....................................................41

3.2.5 Program Administrator and Participant Cost Analysis: The Total Resource Cost of 

Saved Energy .........................................................................................................................42

4. Testing Influences on the Costs of Saved Energy .....................................................................44

4.1 Hypotheses .........................................................................................................................45

4.2 Approach ............................................................................................................................46

4.3 Preliminary Results: Analysis of Factors that May Influence the Cost of Saved Energy .47

iv
4.3.1 Program Administrator Experience ...........................................................................47

4.3.2 Scale of Program ........................................................................................................48

4.3.3 Labor Costs ................................................................................................................50

4.4 Analytical Challenges ........................................................................................................51

5. Discussion of Key Findings and Recommendations .................................................................52

5.1 Key Findings ......................................................................................................................52

5.2 Discussion: Program Data Collection and Reporting ........................................................54

6. References .................................................................................................................................58

v 
List of Tables

Table ES-1. The program administrator cost of saved energy (CSE) for electricity efficiency 

programs for 2009-2011 data in the LBNL DSM Program Impacts Database 

(2012$/kWh) .................................................................................................................... xi

Table 2-1. Summary of energy efficiency program data in LBNL DSM Program Impacts 

Database ............................................................................................................................ 5

Table 2-2. Abridged definitions for selected program cost and savings data ............................... 10

Table 2-3. Program administrator cost of saved energy metrics: definitions and potential uses.. 13

Table 3-1. Program administrator expenditures for 2009–2011 electricity efficiency programs . 20

Table 3-2. U.S. Census Regions and states in the LBNL DSM Program Impacts Database ....... 23

Table 3-3. Program administrator expenditures for 2009-2011 natural gas efficiency programs 26

Table 3-4. The program administrator CSE for electricity efficiency programs by sector: national 

savings-weighted averages .............................................................................................. 28

Table 3-5. The program administrator CSE for gas efficiency programs by sector: national 

savings-weighted averages ($/therm) .............................................................................. 29

Table 4-1. Factors that may influence the cost of saved energy ................................................... 45

List of Figures

Figure ES-1. CSE for electricity efficiency programs by sector for 2009-2011 data in the LBNL 

DSM Program Impacts Database .................................................................................... xii

Figure ES-2. National levelized CSE for C&I sector simplified program categories ................. xiii

Figure ES-3. National levelized CSE for residential sector simplified program categories ........ xiv

Figure ES-4. Levelized savings-weighted average CSE for electricity efficiency programs that 

include program administrator costs vs. total resource costs for select program categories

 ......................................................................................................................................... xv

Figure 2-1. LBNL DSM Program Impacts Database coverage as compared to national efficiency 

spending reported by Consortium for Energy Efficiency (CEE) ...................................... 6

Figure 2-2. Selected program types in the LBNL program typology ............................................. 8

Figure 2-3. Range of reported program average measure lifetime values for select detailed 

program categories .......................................................................................................... 17

Figure 3-1. Program administrator expenditures, first year and lifetime gross savings for 2009–

2011 electricity efficiency programs ............................................................................... 20

Figure 3-2. Program administrator expenditures and lifetime gross savings by simplified program 

category for 2009–2011 residential electricity efficiency programs ............................... 21

Figure 3-3. Lifetime gross electricity savings for 2009-2011 residential consumer product rebate 

programs .......................................................................................................................... 22

Figure 3-4. Program administrator expenditures and gross lifetime savings for 2009-2011 

commercial and industrial electricity efficiency programs ............................................. 23

Figure 3-5. Program administrator expenditures by region for 2009-2011 electricity efficiency 

programs .......................................................................................................................... 24

Figure 3-6. Program administrator lifetime savings by region for 2009-2011 electricity efficiency

 ......................................................................................................................................... 25

vi 
Figure 3-7. Program administrator expenditures, first- year and lifetime gross savings for 2009–

2011 natural gas efficiency programs ............................................................................. 26

Figure 3-8. National savings-weighted average CSE for electricity efficiency programs by sector

 ......................................................................................................................................... 28

Figure 3-9. CSE for natural gas efficiency programs by sector .................................................... 30

Figure 3-10. National levelized CSE for electricity efficiency programs by sector ..................... 31

Figure 3-11. National levelized CSE for commercial and industrial sector simplified program 

categories ......................................................................................................................... 32

Figure 3-12. National levelized CSE for residential sector simplified program categories ......... 33

Figure 3-13. National levelized CSE for residential whole home detailed program category ..... 34

Figure 3-14. National levelized CSE for residential consumer product rebate detailed program 

categories ......................................................................................................................... 35

Figure 3-15. Levelized CSE for electricity efficiency programs by region .................................. 36

Figure 3-16. CSE values by state for electricity efficiency programs (excluding low-income 

programs) ......................................................................................................................... 37

Figure 3-17. Percent of regional lifetime savings by climate zone and levelized CSE for 

electricity efficiency programs ........................................................................................ 38

Figure 3-18. Levelized CSE for residential new construction programs compared to residential 

building energy codes adopted by states in each region ................................................. 39

Figure 3-19. Regional levelized CSEs for commercial new construction programs compared to 

commercial building energy codes adopted by states in each region .............................. 40

Figure 3-20. Impact of different program average measure lifetime assumptions on the levelized 

CSE for electricity efficiency programs .......................................................................... 42

Figure 3-21. Levelized savings-weighted average CSE for electricity efficiency programs that 

include program administrator costs vs. total resource costs for select program categories

 ......................................................................................................................................... 43

Figure 4-1. First-year portfolio-level CSE and program administrator experience, as measured by 

years of program spending above a minimal level. ......................................................... 48

Figure 4-2. First-year CSE for appliance recycling programs and the reported number of 

recycling program participants ........................................................................................ 49

Figure 4-3. First-year portfolio-level CSE values and state average wages for construction

industry employees ($/hour) ............................................................................................ 50

Figure 5-1. Components of annual energy efficiency program reporting .................................... 56

 

vii 
Acronyms and Abbreviations

ACEEE American Council for and Energy-efficient Economy

C&I commercial and industrial (private sector)

CCE Cost of conserved energy

CEE Consortium for Energy Efficiency 

CSE Cost of saved energy 

DOE U.S. Department of Energy 

DSM Demand-Side Management

EIA Energy Information Administration

EERS Energy Efficiency Resource Standards

HVAC heating, ventilation, air conditioning 

LCOE Levelized cost of energy

MUSH Municipal and state governments, universities and colleges, K-12 schools, and 

healthcare markets

WACC Weighted average cost of capital

viii
Executive Summary

End-use energy efficiency is increasingly being relied upon as a resource for meeting electricity 

and natural gas utility system needs within the United States. There is a direct connection 

between the maturation of energy efficiency as a resource and the need for consistent, highquality data and reporting of efficiency 

program costs and impacts. To support this 

effort, LBNL initiated the Cost of Saved 

Energy Project (CSE Project) and created a 

Demand-Side Management (DSM) Program 

Impacts Database to provide a resource for 

policy makers, regulators, and the efficiency 

industry as a whole. 

This study is the first technical report of the 

LBNL CSE Project and provides an overview 

of the project scope, approach, and initial 

findings, including: 

• Providing a proof of concept that the 

program-level cost and savings data can 

be collected, organized, and analyzed in 

a systematic fashion; 

• Presenting initial program, sector, and 

portfolio level results for the program 

administrator CSE for a recent time 

period (2009-2011); and

• Encouraging state and regional entities to establish common reporting definitions and 

formats that would make the collection and comparison of CSE data more reliable. 

The LBNL DSM Program Impacts Database includes the program results reported to state 

regulators by more than 100 program administrators in 31 states, primarily for the years 2009–

2011. In total, we have compiled cost and energy savings data on more than 1,700 programs over 

one or more program-years for a total of more than 4,000 program-years’ worth of data, 

providing a rich dataset for analyses. We use the information to report costs-per-unit of 

electricity and natural gas savings for utility customer-funded, end-use energy efficiency 

programs. The program administrator CSE values are presented at national, state, and regional 

levels by market sector (e.g., commercial, industrial, residential) and by program type (e.g., 

residential whole home programs, commercial new construction, commercial/industrial custom 

rebate programs). 

In this report, the focus is on gross energy savings and the costs borne by the program 

administrator—including administration, payments to implementation contractors, marketing, 

incentives to program participants (end users) and both midstream and upstream trade allies, and 

Cost of Saved Energy (CSE) 

vs. Cost Effectiveness

The program administrator’s cost of saved 

energy is a useful metric for comparing the 

relative costs of efficiency programs and for 

comparing an energy efficiency option to other 

demand and supply choices for serving energy 

needs. The CSE is comparable to the levelized 

cost of energy (LCOE), which represents the perkilowatt hour cost (in real dollars) of building and 

operating a generating plant over an assumed 

financial life and duty cycle.

The cost of saved energy is not a direct test of 

cost effectiveness, however, and is not a benefitcost analysis, like the Program Administrator’s 

Cost Test or Utility Cost Test, because it does not 

purport to capture the monetized value of 

efficiency to utility customers and shareholders. 

ix
evaluation costs.1 We collected data on net savings and costs incurred by program participants. 

However, there were insufficient data on participant cost contributions, and uncertainty and 

variability in the ways in which net savings were reported and defined across states (and program 

administrators). As a result, they were not used extensively in this report. It is also important to 

note that savings metrics reported by program administrators draw heavily from estimated 

values.2

Key Definitions

Program administrator costs include 

administrative, education, marketing and 

outreach, and evaluation, measurement and 

verification (EM&V) costs as well as financial 

incentives paid to customers or contractors. The 

CSE values exclude participant costs, and program

administrator performance incentives, and, thus,

do not represent the total resource cost unless 

indicated otherwise. 

Program savings are based on claimed gross 

savings reported by the program administrator 

unless indicated otherwise. For program 

administrators that only reported net savings 

values, we calculated gross savings values using 

net-to-gross ratios if those were available from the 

program administrator. 

Savings values are also based on savings at the 

end-use site and not at the power plant or natural 

gas pumping station and thus do not account for 

transmission and distribution losses. 

Lifetime energy savings, when not reported by the 

program administrator, were calculated per the 

protocol described in Chapter 2.

Cost of First-Year Energy Savings (First-Year CSE): 

The cost of acquiring a single year of annualized 

incremental energy savings through actions taken 

through a program/sector/portfolio. The cost of 

efficiency as a function of first-year energy savings 

may be useful for program design or budgeting to 

meet incremental annual savings targets. 

Levelized Cost of Lifetime Energy Savings 

(Levelized CSE): The cost of acquiring energy 

savings that accrue over the economic lifetime of 

the actions taken through a program/sector/

portfolio, amortized over that lifetime and 

discounted back to the year in which the costs are 

paid and the actions are taken. 

1 Researchers who have estimated the cost of saved energy for efficiency programs have typically focused on the 

program administrator’s costs because data on participant costs are often not available (Friedrich et al. 2009). Gross 

savings are those associated with the program participants’ efficiency actions, irrespective of the cause of those 

actions. Net savings is defined as the total change in energy use that is attributable to a program (for both program 

participants and non-participants). 2 Savings metrics rely heavily on estimated values because “....energy and demand savings as well as non-energy 

benefits resulting from efficiency actions cannot be directly measured. Instead, savings and benefits are based on 

counterfactual assumptions. Using counterfactual assumptions implies that savings are estimated to varying degrees 

of accuracy by comparing the situation (e.g., energy consumption) after a program is implemented (the reporting 

period) to what is assumed to have been the situation in the absence of the program (the “counterfactual” scenario, 

known as the baseline). For energy impacts, the baseline and reporting period energy use are compared, while 

controlling (making adjustments) for factors unrelated to energy efficiency actions, such as weather or building 

occupancy. These adjustments are a major part of the evaluation process; how they are determined can vary from 

one program type to another and from one evaluation approach to another. “ (SEE Action Network 2012)

x 

 
Results

The CSE values presented in this study are retrospective and may not necessarily reflect future 

CSE for specific programs, particularly given updated appliance and lighting standards. The CSE 

values are presented as either (a) the savings-weighted average values; (b) as an inter-quartile 

range with median3 values across the sample of programs; or (c) both. 

Table ES-1 provides an overall indication of national, savings-weighted average program

administrator CSE values by sector using two indicators (e.g., levelized CSE 6% real discount 

rate and first-year CSE).4 Figure ES-1 indicates the savings-weighted averages, medians and 

inter-quartile ranges for levelized CSE values using a 6% discount rate.

Table ES-1. The program administrator CSE for electricity efficiency programs for 2009-2011 data 

in the LBNL DSM Program Impacts Database (2012$/kWh) 

Sector Levelized CSE

($/kwh; 6% discount rate)

First-Year CSE

($/kwh)

Commercial & Industrial (C&I) $ 0.021 $ 0.188

Residential $ 0.018 $ 0.116

Low Income $ 0.070 $ 0.569

Cross Sectoral/Other $ 0.017 $ 0.120

National CSE $ 0.021 $ 0.162

Values in this table are based on the 2009-2011 data in the LBNL DSM Program Impacts Database. CSE values are for program

administrator costs and based on gross savings.

3 The inter-quartile range is the middle 50 percent of the range of program CSE values. The median is the numerical 

value separating the upper half of a data sample from the lower half. 

4 We calculated a levelized CSE using two discount rates that are rough proxies for different perspectives on energy 

efficiency investments: a 6% real discount rate that can reflect the utility weighted average cost of capital (WACC) 

and a 3% real discount rate that can be a proxy for a societal perspective.

xi

 
Figure ES-1. CSE for electricity efficiency programs by sector for 2009-2011 data in the LBNL 

DSM Program Impacts Database 

Our key national and regional findings are:5

• The U.S. average levelized CSE was slightly more than two cents per kilowatt-hour 

when gross savings and spending is aggregated at the national level and the CSE is 

weighted by savings.

• Residential electricity efficiency programs had the lowest average levelized CSE at 

$0.018/kWh. Lighting rebate programs accounted for at least 44% of total residential 

lifetime savings with a savings-weighted average levelized CSE of $0.007/kWh. The 

residential CSE, when the lighting programs were removed, was $0.028/kWh. Lowincome programs have an average levelized CSE at $0.070/kWh. 

• Commercial, industrial and agricultural (C&I) programs had an average levelized 

CSE of $0.021/kWh. 

• Not surprisingly, the levelized CSE varies widely, both among and within program 

types. We find that the median value is typically higher than the savings-weighted 

average for nearly all types of programs. One possible explanation is that our sample 

includes a number of very large programs and for any given program type, larger 

efficiency programs have lower CSE than smaller programs because administrative 

costs are spread over more projects (e.g., economies of scale).

• In reviewing regional results, efficiency programs in the Midwest had the lowest 

average levelized CSE ($0.014/kWh), while programs in northeast states had a higher 

5 Key findings in this section use savings-weighted average CSE values that include program administrator costs (in 

2012$) and reported gross savings, which are levelized using a 6% real discount rate.

xii

 
average CSE value ($0.033/kWh). Programs in western states are at $0.023/kWh and 

for the southern states included in the database, the comparable program CSE was 

$0.028/kWh. 

• Natural gas efficiency programs had a national, program administrator savingsweighted average CSE of $0.38 per therm, with significant differences between the 

C&I and residential sectors (average values of $0.17 vs. $0.56 per therm, 

respectively).

• The cost of saved energy may vary across program administrator portfolios for 

reasons that have little to do with programmatic efficiency. In some jurisdictions, a 

policy mandate of acquiring all reasonably available cost-effective energy efficiency 

can lead to a focus on more comprehensive programs which will tend to have a higher 

CSE because they are serving more diverse constituencies and technologies. In other 

jurisdictions, the focus may be on acquiring the cheapest savings possible. 

Program-level results

We also examined the cost of saved energy by program type for both residential and C&I 

programs (see Chapter 3). Figure ES-2 shows an example for the C&I programs, including 

savings-weighted average (pale green bar) CSE values, the inter-quartile ranges (blue line) and 

median (red dotted line) CSE values. The median value and inter-quartile ranges for CSE are 

based on calculations for each individual program and gives equal weighting to programs 

irrespective of their relative size in terms of either savings or costs. 

Figure ES-2. National levelized CSE for C&I sector simplified program categories

xiii
The simplified C&I programs have median values for program administrator CSE that range 

from $0.01/kWh to $0.05/kWh. It is worth noting that the savings-weighted average CSE values 

for custom and prescriptive rebate program categories are $0.018/kWh and $0.015/kWh, 

respectively. Since these two program categories account for almost 70% of C&I sector savings, 

they tend to drive the overall CSE results for the C&I sector (less than $0.02/kWh). 

For the residential programs, several program categories have a relatively tight range of program 

CSE values (see Figure ES-3). For example, Consumer Product Rebate programs have an interquartile range of $0.01/kWh to $0.04/kWh and a low savings-weighted average (~$0.01/kWh). 

However, the residential prescriptive ($0.03/kWh to $0.11/kWh), new construction ($0.03/kWh 

to $0.11/kWh) and whole-home upgrade ($0.03/kWh to $0.21/kWh) program types have 

significantly larger ranges. There are several possible reasons for the range of CSE values in 

each of these program categories. The prescriptive simplified program category includes detailed 

program types that implement a wide variety of measures (e.g., HVAC, insulation, windows, 

pool pumps) as well as some generic “prescriptive” programs6

 that often include measures also 

found in the consumer product rebate category. This broad measure mix—and the variation in 

costs and measure lifetimes associated with those measures—are possible drivers for the wide 

range of CSE values for the prescriptive category.

Figure ES-3. National levelized CSE for residential sector simplified program categories

6 Some programs include all their rebated measures under the same program title and it is not possible to determine 

where the majority of the savings is coming from. In these cases, the programs were categorized as “Residential 

Prescriptive.”

xiv

 
For the Whole-Home Upgrade program category, the broad range of program designs and 

delivery mechanisms (this category includes audit, direct install, and retrofit/upgrade programs) 

may help explain the relatively wide range of CSE values. Overall, most C&I program categories 

have a relatively smaller inter-quartile range of CSE values compared to residential program 

categories.

Total resource cost of saved energy

Although we focus on program administrator costs in this report, it is important to note that these 

metrics do not reflect a total cost perspective since program administrators infrequently report 

participant costs. We were able to collect participant cost data from a handful of program 

administrators. However, given small sample size and uncertainty in how participant costs were 

derived, it is difficult to confidently assess the “all-in” or total resource cost of efficiency or 

analyze potential influences on the total cost of the efficiency resource. For these reasons, in 

Figure ES-4, we compare the program administrator’s levelized CSE vs. a total resource 

levelized CSE for illustrative purposes only. We calculate this total resource CSE for the 

simplified program categories where both program administrator and participant costs are 

available for more than 18 program years.7

Figure ES-4. Levelized savings-weighted average CSE for electricity efficiency programs that 

include program administrator costs vs. total resource costs for select program categories8

7 The “n” of 18 was selected because there was a natural break in the data and there were a meaningful number of 

programs from which to calculate average values. 

8 This chart includes a very small sample of programs from 11 states; thus, results may not reflect current practices 

in many jurisdictions. 

xv

 
For this small sample of programs, we found that the levelized total resource CSE values are 

typically double the program administrator CSE with the exception of the Residential Whole 

Home Upgrade program category (which has a savings-weighted total resource CSE that is about 

25-30% higher than the program administrator CSE). Further data collection and analyses could 

better characterize the way in which the ratio of program administrator costs to participant costs 

varies as a function of sector, measure types, and market maturity; and how incentives and direct 

support might be optimized to pay no more than is necessary to meet a state’s efficiency policy 

objectives.

Observations and Recommendations on Reporting 

In calculating the CSE, we utilized information on program administrator costs, annual energy 

savings, estimated lifetime of measures installed in a program, and an assumed discount rate. 

However, with respect to current program reporting practices, we observed several challenges to 

the collection of this data for the purposes of calculating the CSE: 

• Inconsistencies in the quality and quantity of the costs and savings data led LBNL to 

develop and attempt to apply consistent data definitions in reviewing and entering 

program data: 

o Program administrators in different states did not define savings metrics (e.g., 

varying definitions of net savings) and program costs consistently; and 

o Market sectors and program types were not characterized in a consistent fashion 

among program administrators. 

• Many program administrators did not provide the basic data needed to calculate CSE 

values at the program level (i.e., program administrator costs, lifetime savings, or 

program-average measure lifetimes), which can introduce uncertainties into the 

calculation of CSE values (as we developed and utilized methods to impute missing 

values in some cases). 

As a practical matter, the quality and quantity of program data reported by program 

administrators is an important factor in assessing energy efficiency as a resource in the utility 

sector. Additional rigor, completeness, standard terms, and consensus on at least essential 

elements of reporting could pay significant dividends for program administrators and increase 

confidence in energy efficiency savings among policymakers and other stakeholders—

particularly in situations where efficiency is treated as a resource in utility procurement 

decisions, ISO/RTO forward capacity markets, or as an environmental compliance or mitigation 

option by state or federal environmental agencies. 

Of the 45 states currently running utility-customer funded efficiency programs (Barbose et al. 

2013), only 31 states provided reporting with sufficient transparency to complete a programlevel CSE analysis, and almost all of the 31 states’ data required some interpretation for purposes 

of regional or national comparison. With more consistent and comprehensive reporting of 

program results, additional insights can quite possibly be obtained on trends in the costs of 

energy efficiency as a resource as program administrators scale up efforts, what saving energy 

costs among an array of strategies, and what and how cost efficiencies might be achieved. 

xvi 
Therefore, we urge state regulators and program administrators to consider annually reporting 

certain essential data fields at a portfolio level and more comprehensive reporting of programlevel data in order to facilitate the comparison of efficiency program results at state, regional, 

and national levels. A diagram illustrating this reporting hierarchy approach can be found in 

Chapter 5, Figure 5-1. 

As part of the LBNL CSE Project, we intend to continue collecting energy efficiency program 

data and analyzing and reporting the CSE for efficiency actions funded by utility customers. We 

also plan to: 

• Work with state, regional, and national stakeholders to encourage the collection of

program cost and impact data using a common terminology and program typology as

defined in this report and a companion policy brief (Hoffman et al. 2013). This is

important for organizing program data into appropriate and consistent categories so

that programmatic energy efficiency, as a regional and national resource, can be

reliably assessed.

• Annually compile data reported by program administrators and state agencies from

across the United States.

• Conduct additional analyses to help increase understanding of factors that influence

EE program impacts, costs and the cost of saved energy.

xvii 
1. Introduction 

Demand side management (DSM), and end-use energy efficiency specifically, is increasingly 

being relied upon as a resource for meeting electricity and natural gas system needs within the 

United States, often because efficiency is quite cost-effective compared to other resource 

options. For example, 15 states have enacted long-term, binding energy savings targets, often 

called Energy Efficiency Resource Standards (EERS), and another five states have mandates that 

program administrators must acquire “all cost-effective energy efficiency.”9 In 2011, U.S. energy 

efficiency program administrators that manage utility customer-funded efficiency programs 

spent about $5.4 billion on electric and gas energy efficiency programs (CEE 2013), with 

spending projected to possibly more than double by 2025 (Barbose et al. 2013). 

Electric and natural gas energy efficiency in the United States is pursued through a diverse mix 

of policies and programmatic efforts, which support and supplement private investments by 

individuals and businesses. These efforts include federal and state minimum efficiency standards 

for electric and gas end-use products; state building energy codes; a national efficiency labeling 

program (ENERGY STAR®); tax credits; and a broad array of largely incentive-based programs 

for consumers, funded primarily by electric and natural gas utility customers (Dixon et al. 2010) 

(Barbose et al. 2013).10

These utility customer-funded efficiency programs are overseen by state regulators and 

administered by more than 100 different entities (e.g., utilities, state energy agencies, non-profit 

and for-profit third parties) and are the focus of this study. Policymakers, regulators, program

administrators and implementers rely on information about lifetime costs and savings of these 

customer-funded efficiency programs to assess efficiency’s potential, to design and implement 

programs in a cost-effective manner or to improve program cost effectiveness. Given the 

expected growth in efficiency funding and the importance of understanding the cost of saved 

energy (CSE), we initiated this LBNL Cost of Saved Energy Project (CSE Project) to provide a 

resource for policy makers, regulators and the efficiency industry as a whole. 

1.1 Assessing Energy Efficiency as a Resource

The cost and cost effectiveness of utility-customer funded end-use efficiency programs depend

on perspective. From the perspective of a participant in a program, their cost is the cost of an 

efficiency project net of any incentives or support that might be provided by a program 

administrator. From the program administrator’s perspective, it is the cost of planning, designing, 

and implementing a program and providing incentives to market allies and end users to take 

actions that result in energy savings; costs incurred by participants are not considered as part of 

the program administrator’s costs. The total resource or societal cost perspective takes into 

9 States with an EERS as of the date of this report are: AZ, CA, CO, HI, IL, IN, MD, MI, MN, MO, NM, NY, OH, 

PA, and TX. Six states have a mandate to achieve all cost-effective savings: CA, CT, MA, RI, VT, and WA. 10 For additional energy efficiency market background, please see: The Future of Utility Customer-Funded Energy 

Efficiency Programs in the United States: Projected Spending and Savings to 2025. 

http://emp.lbl.gov/publications/future-utility-customer-funded-energy-efficiency-programs-united-states-projectedspend

1 

 
account the costs paid by both the program administrator and the participant to implement the 

efficiency action.

Numerous researchers have estimated the CSE for efficiency programs funded by utility

customers (see Appendix A for a description of past and current efforts). These researchers have 

typically focused on the program administrator perspective (i.e., the program administrator

CSE), for two primary reasons. First, in some cases, participant costs are often not collected or 

reported by program administrators in annual reports (see Chapter 2). Second, when comparing 

efficiency with supply side resources, some consider that the proper metric is the money paid to 

obtain the resource by the program administrator as supply-side resources do not consider, or 

have, participant costs. For this report, primarily because of the first reason, we present program

administrator CSE data and analyses.

Another consideration for assessing efficiency as a resource is whether CSE values are based on 

net or gross savings. Net savings are those attributed to a program (for both program participants 

and non-participants). Gross savings are those associated with the program participants’ 

efficiency actions, irrespective of the cause of those actions. There is debate about the proper use 

of net and gross savings in CSE calculations (SEE Action 2012); however, since there is neither 

sufficient nor consistent data available on net savings, we present CSE values based on gross 

savings in this study. 

1.2 Objectives and Scope 

This CSE Project presents and analyzes the costs of acquiring energy savings for different 

efficiency program types and in different market sectors across the United States. Our objectives 

are to provide insight into the costs associated with saving a unit of energy and the potential 

factors that influence those costs. To this end, we hope our work will: 

• Benefit policy makers, system planners and other stakeholders by providing 

continually improving CSE indicators that enable projections of future spending and 

savings. 

• Enable more cost-effective efficiency programs by:

o Benchmarking and comparing program implementation approaches across 

different markets (e.g., industrial, commercial, small commercial), delivery 

mechanisms (e.g., direct install versus do it yourself), and design approaches (e.g., 

prescriptive versus custom rebates);

o Analyzing contextual factors that affect CSE, such as types of programs, 

measures, program administrator experience, changes in building energy codes 

and standards, labor costs, climate, state-level policies, and the scale of efficiency 

investments.

This study is the first technical report of the LBNL CSE Project and provides an overview of 

project scope, approach and initial findings, including: 

• Providing a proof of concept that the program-level cost and savings data can be 

collected, organized and analyzed in a systematic fashion; 

2 
• Presenting initial program, sector and portfolio level results for the cost of saved 

energy for a recent time period (2009-2011); and

• Encouraging state and regional entities to establish common reporting definitions and 

formats that would make the collection and comparison of CSE data more reliable. 

Specifically, this report includes and discusses elements of our approach, including the 

following: 

• Developing the data collection, documentation, and analyses procedures LBNL used 

to calculate the CSE (Chapter 2);

• Defining program categories as well as cost and savings definitions that allow for 

consistent, standardized entry of program administrator data into a CSE database

(Chapter 2); 

• Developing a database of program-level data on energy efficiency program impacts 

and costs from states with significant utility customer-funded energy efficiency 

programs (Chapter 2); 

• Presenting the range of regional-, state-, sector-, and portfolio-level energy-efficiency

program administrator CSE and program-level CSE for a defined set of over 60 

program categories (Chapter 3); 

• Exploring potential relationships between the program administrator costs of saved 

energy for specific types of programs and climate zones and adopted building energy 

codes (Chapter 3); 

• Conduct a preliminary statistical analysis that explores factors that may be associated 

with and influence the cost of saved energy at the portfolio or program level and set 

the stage for future analyses that will assess additional hypotheses and a broader, 

more refined range of factors (Chapter 4); and

• Present recommendations for future data collection and analyses (Chapter 5). 

1.3 Report Organization 

The remainder of this report is organized as follows. Chapter 2 provides an overview of approach 

used to collect data in the LBNL DSM Program Impacts Database and the challenges associated 

with collecting, organizing and analyzing the data in a consistent fashion. In Chapter 3, we 

present descriptive statistics on efficiency program costs and savings followed by presentation of 

CSE statistics at a national, sector, regional, and state level and for certain program types and in 

relation to climate zones and building code status. In Chapter 4, we discuss our efforts to define 

and statistically test some factors that may influence the CSE. Chapter 5 presents a discussion of 

the key findings and recommendations for regulators and program administrators to consider 

with respect to CSE-related data collection and reporting. 

The appendices contain documentation on topics covered in the chapters, including tables of 

CSE metrics by region, sectors, and program types in Appendix E.

3 
2. Approach

The state-by-state evolution of utility customer-funded energy efficiency programs has fostered 

diversity in these programs’ oversight, design, administration and evaluation. Thus, not 

surprisingly, information provided to state regulators by program administrators on the impacts 

and costs of efficiency programs is diverse with respect to the level of specificity and detail 

required as well as terms and definitions used to describe the costs and impacts of individual 

programs. In this chapter, we summarize our assembled program data, discuss our approach to 

compiling, organizing and analyzing the data in a manner that addresses the diversity in 

reporting practices yet allows for consistent reporting on the cost of saved energy across the 

country and on the basis of region, market sector, and type of program. This approach included 

developing an energy efficiency program typology and adopting standard definitions for program 

characteristics, cost and savings data. We also discuss several major challenges associated with 

collecting and analyzing program cost and impact data and calculating CSE values given data 

quality issues. 

2.1 Data Summary

The data for this study were drawn from annual reports, mostly for the years 2009–2011, which 

were prepared by program administrators of efficiency programs funded by the customers of 

U.S. investor-owned utilities in 31 states. Our energy efficiency program data set comprises 

expenditure, energy savings and program participation data (where available) reported by 107 

program administrators, for a total of 4,184 program records (see Table 2-1).

We relied primarily on annual DSM or efficiency reports filed by program administrators with 

state regulatory agencies because they both typically include data for a portfolio of programs and 

are publicly available from state regulatory commission filings.11 In some cases, when data were 

not found or were ambiguous in annual reports, we consulted other reports (e.g., other 

performance metrics reports filed by investor-owned utilities in California) or solicited additional 

information directly from the program administrator or regulatory staff. Where required data 

were not provided in a program administrator’s filed annual report, but provided in third-party 

program evaluation reports that were included as attachments to the program administrator 

annual reports, we used data from both to populate what we are calling the LBNL DSM Program 

Impacts Database (database).12,13 

11 The states included in this analysis were selected based on the availability and transparency of program cost and 

savings data at the individual program level as identified by LBNL researchers in a recent review of customerfunded energy-efficiency programs (Barbose et al. 2013). To the extent that reports were accessible, we collected 

data for all investor-owned utilities (IOUs) in the target states. Many program administrators had not yet released 

2012 program year results during the data collection period for this study; thus our analysis focuses on the 2009-

2011 period. We did not include program data from publicly-owned electric utilities and rural electric cooperatives 

because these utilities often do not report program level data that is publicly available. Future efforts may include 

data collected from public utilities. 12 We did not rely on individual impact evaluation studies of efficiency programs because the data of interest to this 

project are usually reported in relatively easily accessible summary form and per program in the annual reports filed 

with regulators. Moreover, evaluations of individual programs are not always publicly available nor do they always 

include program or portfolio-related costs. 13 Appendix C describes data that was collected for this research effort, the database configuration, and the data 

quality assurance/quality control process and procedures.

4 

 
Table 2-1. Summary of energy efficiency program data in LBNL DSM Program Impacts 

Database14

State First Year of 

Data

Last Year of 

Data

Total # of 

Years

Number of 

Program 

Administrators*

Number of 

Program 

Records

AZ 2010 2011 2 3 65

CA 2010 2012 3 4 1210

CO 2009 2011 3 1 110

CT 2009 2011 3 4 60

FL 2011 2011 1 5 88

HI 2009 2011 3 1 21

IA 2009 2011 3 3 171

ID 2010 2011 2 1 40

IL 2008 2011 4 2 85

IN 2009 2012 4 5 244

MA 2009 2011 3 11 403

MD 2010 2011 2 4 126

ME 2009 2011 3 2 22

MI 2009 2011 3 2 81

MN 2009 2011 3 2 141

MT 2011 2011 1 1 19

NC 2009 2011 3 2 37

NH 2009 2011 3 4 90

NJ 2009 2011 3 1 40

NM 2010 2011 2 4 101

NV 2009 2011 3 3 209

NY 2009 2011 3 11 111

OH 2009 2011 3 7 170

OR 2009 2011 3 2 16

PA 2009 2010 2 6 143

RI 2010 2011 2 2 36

TX 2010 2011 2 10 202

14 “Number of Program Records” includes programs that produced energy savings (e.g., residential or commercial 

rebate programs), programs for which the program administrator did not claim savings (e.g., education and outreach 

programs or pilot programs), and, in some cases, sector- or portfolio-wide activities (e.g., marketing or internal 

program evaluation activities).

5 

 
State First Year of 

Data

Last Year of 

Data

Total # of 

Years

Number of 

Program 

Administrators*

Number of 

Program 

Records

UT 2009 2011 3 1 41

VT 2009 2011 3 1 18

WA 2010 2011 2 1 42

WI 2009 2011 3 1 42

 Totals 107 4184

* In some cases, program administrators who run both gas and electric programs are counted twice for the purposes of 

separating the reported effects of each program.

Figure 2-1. LBNL DSM Program Impacts Database coverage as compared to national efficiency 

spending reported by Consortium for Energy Efficiency (CEE)15

15 CEE Annual Industry Reports can be found here: http://www.cee1.org/annual-industry-reports

6 

 
The efficiency program data that were compiled by LBNL staff into the database represent a 

significant share of all efficiency programs funded by utility customers in the United States. The 

database contains programs with total program administrator expenditures of about $7.6 billion

(see light and dark blue shading in Figure 2-1). Programs in the LBNL database represent about 

25% ($1.1 billion) of 2009 national program expenditures by gas and electric utilities and about 

50% of program expenditures in 2010 and 2011 ($2.9B in 2010 and $3.2B in 2011), compared to 

national efficiency spending as reported by the Consortium for Energy Efficiency (CEE) (see 

Figure 2-1).16 

2.2 Program Typology and Standardized Definitions 

We developed program categories in order to characterize and analyze similar types of efficiency 

program types, as defined by market sector and technology, action, delivery approach, or other 

common themes. Examples of program categories include commercial prescriptive HVAC 

programs, low-income programs, and residential whole home direct-install programs. Some 

program categories are relatively well defined and include a narrow set of technologies (e.g., 

high-efficiency windows or pool pumps), while other categories are cross-cutting, may span a 

wide variety of activities (e.g., statewide marketing, take-home energy efficiency kits), and/or 

target several market sectors (e.g., in-school education programs, lighting technology market 

transformation programs). 

The typology grouped and classified energy efficiency programs into three tiers: (1) sector; (2) 

simplified program categories; and (3) detailed program categories. Figure 2-2 provides a partial 

snapshot of this three-tiered program typology approach: seven sectors (including one for 

demand response programs, which are not addressed in this report), 31 simplified efficiency 

program categories (27 for efficiency programs) and 66 detailed categories (62 for efficiency).17

LBNL has prepared a policy brief that describes the typology in more detail as well as the 

standardized definitions (Hoffman et al 2013). Appendix B also includes the complete typology 

and set of definitions. 

We determined that a three-tiered hierarchy was appropriate because it allowed for flexibility in 

grouping programs for comparison (e.g., single-measure versus comprehensive whole-building 

programs or by technology such as lighting vs. HVAC programs) and provides options for 

different levels of analysis. Moreover, in some cases, the detailed program category tier 

narrowed the range of installed measures for a program type, thus reducing the uncertainty in 

derivation of measure savings and lifetime savings across measures installed in that program. For 

example, we defined three detailed program categories that fall under the simplified program 

16 However, as noted below and in Chapter 3, some of the data were not utilized for the data presentations, CSE 

metrics and analyses due to missing data. For example, the programs indicated as Combined Fuel in this figure were 

not included in the cost of saved energy analyses, because the costs borne by electricity and gas utility customers 

could not be determined for this subset of programs. Without the useable data, the database still contains about 45-

50% of the national spending estimate.

17 The relatively large number of simplified and detailed categories was necessary to capture the wide range of 

common program offerings throughout the country. We also included some program types in the detailed typology 

because they have regional significance (e.g., pool pump programs in the Southwest, data center programs in New 

York, Washington and California), or the program types appear to be emergent (e.g., financing programs, residential 

behavior-based efficiency programs). 

7 

 
category of “Whole Home Upgrades”: Whole Home Audit Programs; Whole Home DirectInstall Programs; and Whole Home Retrofit Programs.18 

Figure 2-2. Selected program types in the LBNL program typology 

Note: Not all sectors and simplified and detailed program categories are shown

We have relatively high confidence in the categorization of most programs. However, there are 

some programs where we were either not able to obtain much information about the measures 

offered under that program or where there was a wide array of measures offered under a single 

umbrella program. In both situations, programs were generally categorized under “prescriptive” 

or “other” categories. The mix of programs and measures in these two types of categories are 

likely to be less consistent than in other program categories.

The data fields and specification for the database and program categories were developed 

through an iterative process which included review of program administrator annual reports and 

review of several other sources that contain typologies and/or definitions, including the State and 

Local Energy Efficiency Action Network (SEE Action 2012), the Consortium for Energy 

Efficiency (CEE 2012), the Regional EM&V Forum of the Northeast Energy Efficiency 

18 We found that program names were not always indicative of the appropriate program category. Thus, in many 

cases, we reviewed program information as part of the process of classifying programs into program category. We 

defined a specific set of guidelines for classifying programs by type. For example, when the program name was 

ambiguous (e.g., EnergySaver) or when the program description indicated savings could fall into more than one 

detailed or simplified category (e.g., a single program that offered both prescriptive and custom rebates), we looked 

at the measure-level savings reported for that program (if available) and categorized the program according to the 

reported measure mix. 

8 

 
Partnerships (NEEP 2011), and the NEEP Regional Energy Efficiency Database (REED 2013). 

We shared a draft of our categories and definitions and had several discussions with 

representatives from CEE, NEEP and the American Council for an Energy-Efficient Economy 

(ACEEE); and made revisions based on their input. For the demand-response program 

categories, we relied on program categories defined by the Federal Energy Regulatory 

Commission (FERC) for its national surveys (FERC 2012), although demand-response program 

data are not included in this study. 

We also defined program cost and energy savings (impacts) data fields as part of our effort to 

classify and report program information in a consistent fashion across program administrators 

and states.19 

• Program Administrator Costs: The primary cost data used in this report are the 

program administrator costs which include: (1) program administration planning and 

delivery; (2) engineering or technical support; (3) services provided by 

implementation contractors; (4) marketing, education and outreach; (5) direct rebates 

or financial incentives to program participants; and (6) evaluation, measurement and 

verification costs (see Table 2-1).20 Program administrator costs exclude participant 

costs and performance incentives for program administrators (e.g., utility shareholder 

incentives).21 For each program we collected from one to four years of data.22 We 

made inflation adjustments to the program cost data provided by program 

administrators so that all cost data are reported in 2012$.23 We chose to use 2012 as 

our base year because 2012 is the most recent year for which an annual implicit price 

deflator for GDP is available from the U.S. Bureau of Economic Analysis. We would 

have preferred to also report CSE values based on participant, as well as program 

administrator, costs; however, we found that few program administrators reported 

participant costs in their annual reports (see Appendix C). 

• Program Savings: The State and Local Energy Efficiency Action Network’s Energy 

Efficiency Program Impact Evaluation Guide (SEE Action 2012) was the primary 

source used to describe and define the program energy savings indicators in a 

consistent fashion.24 The SEE Action Guide was particularly important for providing 

19 Program cost and savings definitions tend to be consistent within a state, even if there are multiple program 

administrators.

20 Some program administrators did not include program-level costs for activities such as marketing/outreach, 

education, and evaluation, but instead accounted for those expenditures at the sector or portfolio level.

21 We did not report program administrator performance incentives because actual awards of performance incentives 

are not often included in annual reports filed by program administrators, and are frequently awarded at a 

significantly later date. 

22 Some program administrators included prior years’ data in their reports in addition to the 2009–2011 period. 23 Costs can be presented in nominal (or current) or real (or constant) dollar terms. Nominal values are economic 

units measured in terms of purchasing power of the date in question. Real dollar values are economic units measured 

in terms of constant purchasing power. A real value is not affected by general price inflation and can be estimated by 

deflating nominal values with a general price index, such as the implicit deflator for gross domestic product or the 

Consumer Price Index. From OMB Circular A-94 Guidelines And Discount Rates For Benefit-Cost Analysis of 

Federal Programs. We used the GDP implicit price deflator published regularly by the U.S. Bureau of Economic 

Analysis.

24 The SEE Action Guide describes common terminology, structures, and approaches used for determining savings 

from energy efficiency programs guide. The definitions in the SEE Action Guide incorporated input from program 

9 

 
data definitions for net and gross energy savings and lifetime energy savings, which 

for this report are assumed to take place at the end-use site where the efficiency 

actions were implemented.

Table 2-2 provides abridged definitions for key program data in the Database (see Appendix B 

for the complete glossary of energy efficiency program data fields).

Table 2-2. Abridged definitions for selected program cost and savings data

Term Definition

Program Administrator 

Costs

Program administrator costs include the costs of designing programs and 

portfolios; directing, managing and paying implementation contractors; 

marketing, education and outreach (ME&O); program and portfolio 

evaluations; and incentives to both program participants (or end users) and 

to both mid-stream and upstream allies in the market (e.g., financing and 

services such as installations or free audits).

Program Average 

Measure Lifetime 

Weighted average economic lifetime (years) of all measures installed in a 

program year in a specified program.

Annual Gross Savings Gross annual incremental savings (kWh or therm) as reported by the 

program administrator using their own staff or evaluation firm, after the 

subject energy efficiency activities have been completed. Gross savings are 

the change in energy consumption resulting from program-related actions 

taken by program participants regardless of why they participated. Note that 

these are annualized “full-year” savings, regardless of when measures were 

installed during the program year. Per the SEE Action reference (SEE Action 

2012) these may be Claimed or Evaluated Savings.

Lifetime Gross Savings The expected gross savings (GWh or therm) over the lifetime of the 

measures installed under the subject program. For our analysis, where 

available, we relied on lifetime savings reported by the program 

administrator.

The detailed program categories and data definitions described in this section have been adapted 

by CEE for its own 2013 annual surveys of the efficiency program industry.25 We hope that 

other entities will consider using them as well and to support that objective, as part of the CSE 

Project, LBNL plans to gather feedback from stakeholders via an annual or biennial process to 

modify, add or subtract program categories as program offerings change or to address potentially 

needed clarifications in the definitions and categories. 

administrators, state regulators, and other stakeholders from a number of states and regions and included a review 

and synthesis of definitions used in a broad set of energy efficiency glossaries. 

25 As part of its 2013 annual “State of the Industry” survey, CEE is collecting program-level energy efficiency and 

demand response program data from program administrators using the LBNL program categories described in this 

report as well as the definitions from the SEE Action guide.

10 

 
2.3 Challenges in Consistent and Standardized Reporting of Program Data 

When data are compiled from multiple states and program administrators, terminology 

differences can potentially make it difficult to conduct comparative analysis across states or 

program administrators. This was a primary rationale underlying our effort to develop a program 

typology and standardized definitions so that we could conduct a comparative analysis of energy 

efficiency program impacts and costs. However, even with the typology and definitions, there are 

two key data challenges. 

First, we assume that all expenditure, savings and participation data reported by a program 

administrator are accurate. Given our time and resources, this is a reasonable starting 

assumption; however, it should be noted that the range of effort placed into documenting impacts 

by program administrators varies significantly among states (SEE Action 2012). 

Second, in reviewing information on efficiency programs funded by U.S. utility customers, we 

found that program data are often not defined and reported consistently among states. 

Specifically, we identified three key concerns in compiling and analyzing program information 

on a regional or national basis, some of which are addressed by the common typology and 

standardized definitions: 

1. Energy savings and program costs are not defined consistently. The most common 

discrepancies can be found in the definitions of net energy savings. Examples of other 

program data where differences are found across states include:

• The term “annual energy savings” typically is understood as shorthand for annualized 

incremental energy savings, but some entities—including resource planners—apply a 

different meaning that includes savings resulting from prior years’ activities. 

• The definition of measure lifetime, how a program’s average measure lifetime is 

determined, and the estimated measure lifetime values for the same measures or 

program types varies among states. 

• Some program administrators report end-use site savings and others report savings at 

the power plant bus bar (for electricity efficiency programs). 

• Most program administrators do not count their own performance incentives among 

program costs, although some do. The definitions of other cost categories (e.g., 

marketing costs, general consumer education, and evaluation) also vary among states.

2. Program data are not reported consistently across states. For example, some states 

report just gross or net energy savings; others report both. Similarly, many efficiency 

annual reports only include first-year savings and not lifetime savings.26 With respect to 

cost data, program administrators often classify costs differently among administration, 

marketing and outreach, incentives and participant costs. Some program administrators 

26 We found that only about a quarter of the program reports that were reviewed included information on measure 

lifetimes or lifetime savings, although this information is required to assess program cost effectiveness. See below, 

in the section on adjustments for missing data, for discussion of how measure lifetime variation creates uncertainty 

in the calculation of CSE.

11 

 
also report certain costs (e.g., marketing, evaluation) at the portfolio or sector level, while 

others account for those costs at the program level. 

3. Programs and sectors are not characterized in a standardized fashion. Programs 

targeting specific building types or consumers can be included under different sectors 

from state to state (e.g., multi-family residential structures are sometimes categorized as 

commercial programs). Moreover, the types of activities and measures that are included 

under the same program title (e.g., custom vs. combination custom/prescriptive 

programs) also vary.

We suggest that readers consider these above issues when utilizing the information in this report 

for their own uses and understanding of the cost of saved energy. 

2.4 Calculating and Using the Cost of Saved Energy 

The program administrator’s CSE is a useful metric for comparing the relative costs of efficiency 

programs and for comparing an energy efficiency option to other demand and supply choices for 

serving electricity and natural gas needs27. However, the cost of saved energy is not a test of cost 

effectiveness (e.g., one of the screening tests used by program administrators) because: (1) it 

does not capture the full benefits to utility customers and shareholders (e.g., avoided generation 

capacity, avoided transmission and distribution investments, avoided environmental compliance 

costs); (2) benefits are not monetized but reflected simply in energy units of kilowatt hours or 

therms, the cost of which will vary by utility; and (3) energy is saved at the end use, not the 

power plant.28 

In this report, we use gross energy savings (rather than net savings) in the CSE calculations 

primarily because of data availability and comparability reasons: (1) more administrators 

reported gross savings than net; and (2) net savings are defined relatively inconsistently, as 

compared to gross savings, among program administrators and states.

We also report savings at the end-user level (and not at the busbar or power plant source), 

because this is what most program administrators report. It is important to note that savings from 

electricity efficiency programs reported at the busbar would be higher than at the end-use level 

because we are accounting for distribution and transmission losses (losses also occur in the 

natural gas network as well).29 

27 According to the Energy Information Administration, “levelized cost is often cited as a convenient summary 

measure of the overall competiveness of different generating technologies. It represents the per-kilowatt hour cost 

(in real dollars) of building and operating a generating plant over an assumed financial life and duty cycle. Key 

inputs... include overnight capital costs, fuel costs, fixed and variable operations and maintenance (O&M) costs, 

financing costs, and an assumed utilization rate for each plant type. 

http://www.eia.gov/forecasts/aeo/electricity_generation.cfm

28 The equation also is inverted, with costs in the numerator and benefits (in energy units) in the denominator—the 

reverse of the benefit/cost ratios that are a key determinant of cost effectiveness.

29 This is an important consideration if the CSE values were to be compared with costs of electricity generation 

resources, which typically are indicated as busbar values.

12 

 
We calculate the cost of saved energy (CSE) metrics in three ways: (1) a cost of lifetime saved 

energy; (2) a levelized cost of energy savings using two discount rates (3% and 6% real); and (3) 

a cost of first-year energy savings. See Table 2-3 for definitions of these CSE metrics and their 

common uses. 

Table 2-3. Program administrator cost of saved energy metrics: definitions and potential uses

Program 

Administrator 

Cost Metric

Shortened 

Term What is Measured Potential Uses

Cost of Lifetime 

Energy Savings

Lifetime CSE The cost of acquiring energy 

savings that accrues over 

the economic lifetime of the 

actions taken through a 

program/sector/portfolio. 

Calculated by dividing 

program administrators’ 

costs by the gross savings.

• Used by program 

administrators for designing 

programs and portfolios, 

e.g., for depth of savings and 

cost effectiveness

• Used by planners and other 

stakeholders to project 

efficiency as a resource, 

develop load forecasts, etc.

Levelized Cost of 

Energy Savings

Levelized CSE The cost of acquiring energy 

savings that accrue over the 

economic lifetime of the 

actions taken through a 

program/sector/portfolio, 

amortized over that lifetime 

and discounted back to the 

year in which the costs are 

paid and the actions are 

taken

• Same uses as lifetime 

savings

• Useful to program 

administrators, regulators 

and other stakeholders who 

want to compare particular 

demand-side options with 

other demand, and supplyside, resources

Cost of First-Year 

Energy Savings

First-Year CSE The cost of acquiring a 

single year of annualized 

incremental energy savings 

through actions taken 

through a 

program/sector/portfolio. 

Calculated by dividing the 

program administrators’ 

costs by the first year 

incremental savings.

• Useful for program 

administrators in program 

design

The cost of saved energy can be useful to various stakeholders. For example, state regulators can 

use both first-year and lifetime CSE values as quick metrics for assessing whether a program or 

portfolio looks like a reasonable expenditure of utility customer funds. A program administrator 

that is considering offering a comprehensive residential energy upgrade program may want to 

compare that program’s estimated per-unit cost performance against average costs and the range 

of costs for similar programs. Based on the comparison, the program administrator may want to 

13 
look at the design of comparable programs for potential cost efficiencies. Regulators and 

resource planners can use the levelized CSE in the initial screening analysis of various supplyand demand-side resources. Resource planners also can use the lifetime CSE to convert approved 

budgets for demand-side management plans into energy savings estimates that then can be used 

in scenario or sensitivity analysis of future load forecasts. 

Finally, based on the limited participant cost data reported by program administrators, we 

calculate a total resource CSE for illustrative purposes in Chapter 3. This calculation presents the 

net total costs, including both program and participant costs, for the efficiency resource. A 

levelized total resource CSE might also be useful to program administrators, regulators and other 

stakeholders who want to compare particular demand-side options with other demand and 

supply-side resources. 

2.4.1 Levelized Cost of Saved Energy

The lifetime cost of energy savings metric is a simple, straight-forward calculation although it 

ignores changes in the value of money between an initial investment and future energy savings. 

Meier (1982) included the time value of money (discount rate) to calculate the “cost of 

conserved energy” (CCE) or what we are calling the “levelized cost of saved energy”. Meier 

found that inclusion of the discount rate raises the CCE because of discounting future benefits, 

yet provides a basis for comparing the CCE for measures that have different lifetimes and can be 

compared to retail rates and levelized costs of supply-side resources.30 A similar accounting 

framework, the levelized cost of energy (LCOE), often is applied to assessing the economic 

competitiveness of diverse generation sources (U.S. Energy Information Administration 2013). 

We calculated a levelized CSE using two discount rates31 that are rough proxies for different 

perspectives on energy efficiency investments: a 6% real discount rate that can reflect the utility 

weighted average cost of capital (WACC) at present and a 3% real discount rate that can be a 

proxy for a societal perspective. The levelized CSE calculation is as follows: 

Levelized CSE (in $/unit energy, e. g. , kWh,therm, Btu) 

= (C x (Capital Recovery Factor))/(D) 

Capital Recovery Factor = [A ∗ (1 + A)^B]/[(1 + A)^B − 1] 

Where:

A = Discount rate

30 See Appendix A for further discussion of the history of efficiency CSE analyses 31 Discount Rate: An interest rate applied to a stream of future costs and/or monetized benefits to convert those 

values to a common period, typically the current or near-term year, to measure and reflect the time value of money. 

It is used in benefit-cost analysis to determine the economic merits of proceeding with a proposed project, and in 

cost-effectiveness analysis to compare the value of projects. The discount rate for any analysis is either a nominal or 

a real discount rate. A nominal discount rate is used in analytic situations when the values are in then-current or 

nominal dollars (reflecting anticipated inflation rates). A real discount rate is used when the future values are in 

constant dollars and can be approximated by subtracting expected inflation from a nominal discount rate (SEE 

Action Network 2012). 

14 

 
B = Estimated program measure life in years

C = Total program cost in 2012$ 

D =Annual kWh saved that year by the energy efficiency program

This formula is the classic definition of a compound interest calculation used to calculate 

equivalent annual net disbursements. 

The discount rate can have a significant impact on the calculated CSE. For example, for a 

program with an average measure lifetime of 20 years, a discount rate of 6% will indicate a 

levelized CSE that is about 30% higher than the same program if a discount rate of 3% were 

used. See Appendix D for further discussion of the factors considered in choosing these two 

illustrative interest rates. 

2.5 Treatment and Adjustments for Missing Data

In calculating CSE for efficiency programs, we encountered several data completeness issues 

that needed to be resolved: 

• Many programs’ data included neither program measure lifetime nor gross lifetime 

savings. This information is necessary to calculate lifetime and levelized CSE; 

• Some combined gas and electric program administrators reported separate savings for 

their electric and gas programs but did not separate their electric and gas program 

costs; and, 

• Most program administrators reported end-use energy efficiency savings while others 

reported savings at the source of the electricity (generation or busbar savings). 

Natural gas savings are usually considered the same at the end-use site and at points 

along the gas distribution, although there is the potential for per unit losses from the 

natural gas source to the end user. 

In addition, for the few program administrators that reported only net savings, we calculated 

gross savings by dividing reported net savings by a net-to-gross ratio32 when this ratio was 

provided in related references for the subject programs.33 Furthermore, some program reports 

provided no cost data and others provided no savings data; these programs were excluded from 

the CSE analysis. These adjustments resulted in program data from 100 program administrators 

in the database being utilized in calculating CSE values in this study.34

32 The net-to-gross ratio is the net program impact (energy savings) divided by the gross program impact. 33 In Massachusetts and New York, program administrators reported net savings and did not provide net-to-gross 

ratios in their annual efficiency reports. In these cases, we applied net-to-gross ratios reported in the 2011 REED 

database and applied the program level ratios to the previous two years included in this analysis (2009-2010). New 

Hampshire program administrators reported net lifetime savings for 2009-2010. We were not able to generate a 

gross lifetime or annual incremental savings values needed to calculate the CSE and therefore those years were 

dropped from the analysis.

34 Data from 100 of the 107 program administrators whose data are in the LBNL DSM Program Impacts Database 

are included in this Chapter. The seven program administrators that were excluded represent about eight percent of 

the total costs for programs in the Database. Three program administrators are excluded because their combined gas 

and electric program costs could not be separated out by fuel type, three program administrators were excluded 

because they did not report expenditures at the program level, and one program administrator was excluded because 

it reported net savings in a manner that did not allow determination of gross savings. Two years of program data 

15 

 
2.5.1 Program Average Measure Lifetime

The CSE calculation takes into account the costs incurred to implement the measures, which in 

the database all occur during the program year,35 and the savings that occur over the lifetime of 

the implemented measures. However, program administrators reported lifetime savings for only 

about 44% of the programs years in the collected annual reports (see Appendix C).36 Another 

way to calculate the lifetime savings is to multiply the first-year savings by the program average 

measure lifetime (program lifetime)37, which we interpret as the lifetimes of the various 

measures installed through a program weighted by their respective savings. 

However, even fewer program administrators reported any form of a program lifetime—about 

26% of electric and 30% of gas programs for the 2009–2011 period (see Appendix C). For the 

programs that did report a lifetime value, program average measure lifetimes varied widely 

within many of the detailed program categories.38 For example, the median program lifetime for 

residential new construction programs is 18 years, with a program life of 14 and 25 years at the 

25th and 75th percentile for programs in the database. Figure 2-3 shows the range, inter-quartile 

range, and median program lifetime values reported for a selected sample of detailed program 

categories. 

Given the limited availability of lifetime savings and program lifetime values, we developed the 

following set of decision rules, or protocol, for defining lifetime savings for each program in the 

database: 

1. When available, use the program lifetime savings reported for the program by the 

program administrator; 

2. When program administrator did not report program lifetime savings, but did report 

program average lifetime value, we multiplied this value by the reported first-year 

savings to calculate the program’s lifetime savings;39

from three other program administrators were not used in the CSE analysis because these program administrators 

reported net savings in a manner that did not allow determination of gross savings; however, the third year of data 

for those three program administrators was used. 

35 Some project installations may be completed after the end of the program year but are accrued to the program year 

in which the project was initiated (e.g., customer has signed up, equipment installation has been scheduled, 

equipment installation has begun but not been completed). Some energy efficiency actions also may require 

ongoing, incremental operations and maintenance expenditures (compared to the baseline equipment), which are not 

considered in this study, which is consistent with most energy efficiency program assessments. 36 There are more than 4,000 program years in the database, where we count each program in each year of 

implementation separately. 

37 Measure lifetime, also called effective useful life (EUL), is based on the lifetime of equipment installed or 

measures implemented and measure persistence (as opposed to savings persistence). In many energy efficiency 

programs, the estimated EUL takes into account both the expected remaining life of the measure being replaced and 

the expected changes in operational baselines over time (Mass Save 2011, SEE Action 2012).

38 A number of factors may contribute to the variation in reported measure lifetimes including the unique mix of 

measures implemented for a program (particularly for programs that contain a wide range of longer- and shorterlived measures) and different assumptions and/or methodologies used to determine measure lifetime used by 

program administrators.

39 Some program administrators document the average measure lifetime for programs that installed a mix of 

measures. The most common approach used by program administrators is to weight the program average measure 

lifetime by respective measure savings. We applied this approach for all of the reported program measure lifetimes. 

16 

 
3. For programs where we did not have lifetime savings or measure lifetime data, we 

calculated a program average measure lifetime for similar programs in the database and 

used that imputed value along with the program’s first-year savings to calculate program 

lifetime savings.40

For program categories that contained a broad unspecified mix of activities or too few data 

points to calculate a national program average measure lifetime values, we reviewed technical 

reference manual lifetime values for specific measures to generate a “national program average 

measure lifetime” value for that program.41 Given the wide variation in reported measure 

lifetimes, our method of calculating a national program average measure lifetime and applying it 

to programs for which that data are not available introduces uncertainty into the final CSE 

calculation, particularly for program categories that contain mixes of measures with wideranging measure lifetimes. In Chapter 3, we include results of a sensitivity analysis that 

illustrates the impact of varying measure lifetime assumptions on CSE calculations. 

Figure 2-3. Range of reported program average measure lifetime values for select detailed program 

categories

The authors’ experience indicates that the way in which measure lifetimes are defined, determined and reported are 

not consistent among program administrators. 

40We calculated a national program average measure lifetime as follows: divide reported lifetime savings by firstyear savings values for each program in the database that reported this information in order to generate a national 

(un-weighted) program average measure lifetime by program type. 41 See Table C-3 in Appendix C for the national program average measure lifetime values calculated for each of the 

detailed program categories. 

17 

 
2.5.2 Cost Data for Combined-Fuel Programs

Some program administrators of combined-fuel programs reported separate electric and gas 

savings values but did not report separate costs for electric and gas programs or measures. For 

those program administrators where we could not reliably calculate the per-kWh and per-therm 

CSE from the reported data, we obtained additional information that enabled us to calculate 

reasonable estimates of the disaggregated electric and gas expenditures for the following 

combined fuel utility cases:

• The California combined-fuel utilities did not provide separate electric and gas cost 

data. However, one of the utilities provided program-level data on the net monetized 

benefits of the programs, allocated by fuel. We were then able to estimate that 

utility’s combined electric and gas program costs by fuel (electricity and natural gas) 

based on the program’s share of savings allocated to each fuel. 

• A New England combined-fuel utility that had not reported separate gas and electric 

cost data later provided estimates of the ratio of gas and electric costs which were 

applied to that utility’s data. 

Other program data from program administrators for which we could not disaggregate electric 

and gas program costs were included in the overview of program spending and savings presented 

at the beginning of Chapter 2, but excluded from the dataset used to calculate CSE.42 

2.5.3 End-Use versus Source and Busbar Energy Savings

Most state program administrators reported end-use energy efficiency savings; however, there 

were a few program administrators that reported both end-use and busbar, and a handful that 

only reported busbar savings. For the purposes of this report, we followed the following decision 

rules:

• Where program administrators reported both end-use and busbar savings, we used 

end-use savings;

• Where program administrators are not clear, or do not explicitly state that the savings 

is end-use, we treat the savings values as end-use savings;

• Where program administrators only reported a busbar savings value, we identified a 

line loss estimate and calculated that end-use savings.43

42 Wisconsin’s single statewide program administrator was included in the program spending and savings overview 

but excluded from the CSE results because the program administrator did not provide disaggregated electric and gas 

program expenditures data.

43 For a discussion on line losses, please see: http://www.raponline.org/ document/download/id/4537 

18 

 
3. Results—Utility Customer-Funded Programs: Costs and Savings

In this chapter, we first present a national overview of electric and gas energy end-use efficiency 

program administrator expenditures and savings, including summaries by market sector and 

region for the programs in the LBNL DSM 

Program Impacts Database (database). We then 

present ranges of program administrator cost of 

saved energy (CSE) values, mostly for 

electricity efficiency programs (as they 

represent about 80% of program expenditures), 

on a national, regional, and state basis. Some 

CSE values are presented at the sector and 

program level as well. We also include 

sensitivity analyses on the impact of assumed 

measure lifetimes on the CSE (one of the data 

issues raised in Chapter 2). Finally, we present 

CSE results for those programs where program

administrators reported program administrator 

costs and participant costs (what some refer to 

as the total resource cost).

The results presented in this chapter represent 

a significant portion of the efficiency programs 

funded by customers of U.S. investor-owned 

utilities during 2009, 2010, and 2011. 

However, when using the information, the 

reader should recognize that they are not necessarily a representative sample, particularly for 

some regions of the country where annual reporting is not prevalent. 

3.1 Energy Efficiency Program Administrator Expenditures and Savings 

3.1.1 Electric Programs

Program administrator expenditures for identifiable electricity efficiency programs44 in the 

database, for the years 2009–2011, totaled just under $5.3 billion (in 2012$) with 

commercial/industrial programs (C&I) programs representing about 60% of expenditures and 

residential programs comprising about 30% of the expenditures (see Table 3-1). 

In terms of how electricity savings vary by sector for the programs in the database, the answer 

depends on whether first year or lifetime savings are considered (see Figure 3-1). The savings 

accruing from C&I sector programs accounted for 53% of the aggregate first-year savings and 

62% of the aggregate lifetime savings. Residential programs’ share of first-year savings was 

higher than their share of expenditures; residential programs made up 29% of expenditures but 

garnered 40% of first-year savings and 31% of lifetime savings. On the other hand, low-income

programs represent 6% of the total expenditures and 2% of first-year and lifetime savings. 

44 Eighty-eight program administrators reported electric program data.

Attributes of Information 

Reported in this Chapter

Costs refer to program administrator costs only; 

the CSE values exclude participant costs unless 

specifically indicated otherwise. 

Savings are based on gross savings reported by 

the program administrator unless specifically 

indicated otherwise. For program administrators 

that only reported net savings values, we 

calculated gross savings values using net-to-gross 

ratios. Savings values are also based on savings at 

the end-use site and not at the power plant or 

natural gas pumping station and thus do not 

account for transmission and distribution losses. 

See Chapter 2 for more detailed explanation.

Lifetime energy savings, when not reported by 

the program administrator (which was the case 

for about 50% of the programs), were calculated 

per the protocol described in Chapter 2.

19 

 
Table 3-1. Program administrator expenditures for 2009–2011 electricity efficiency programs

Market Sector Share of Total Program 

Administrator Expenditures

Total Program Administrator 

Expenditures (million 2012$)

C&I 61% $3,214

Residential 29% $1,515

Low Income 6% $332

Cross Sector/Other 4% $213

TOTAL 100% $5,274

We also examined residential expenditure and savings data by simplified program type and 

found that consumer product rebate programs,

45 prescriptive rebate programs46 and whole home 

programs47 were the top three contributors to expenditures and lifetime electricity savings in the 

LBNL DSM Program Impacts Database. Combined, these three programs represented 84% of 

total expenditures and 90% of the lifetime savings for residential programs in our database (see 

Figure 3-2).

Figure 3-1. Program administrator expenditures, first year and lifetime gross savings for 2009–

2011 electricity efficiency programs 

45 Programs that encourage use of more efficiency products such as appliances, electronics, lighting products, etc. 46 Programs that provide pre-defined incentives for installation of cost efficient products such as insulation, 

windows, water heaters, etc.

47 Programs that offer direct install services, audits or incentives for comprehensive packages of efficient products.

20 

 
Figure 3-2. Program administrator expenditures and lifetime gross savings by simplified program 

category for 2009–2011 residential electricity efficiency programs

Other observations from the database’s residential electricity program data, as shown in Figure 

3-2, are:

• Consumer Product Rebates accounted for about 29% of total residential program 

expenditures, but over half of the lifetime savings; 

• Residential Prescriptive programs accounted for similar percentages of expenditures 

and lifetime savings, both 26%; 

• Whole Home Upgrade programs represented about 29% of aggregated expenditures 

and12% of the lifetime electricity savings; 

• New Construction programs accounted for 5% of residential program expenditures 

and 6% of the sector’s lifetime savings, 

• Multifamily programs accounted for 5% of expenditures and 3% of lifetime savings, 

and

• Behavior and Education programs make up 3% of expenditures but less than 1% of 

lifetime savings.

To illustrate the power of a program-level database, we analyzed the four detailed program types 

that are included in the residential Consumer Product Rebate program category that covers 52% 

of the residential lifetime electricity savings (see Figure 3-3). This analysis indicated that lighting 

rebate programs accounted for over 80% of all gross electricity savings attributed to the 

consumer product rebates in the program administrator program reports we compiled. This 

means that lighting rebates represent at least 44% of total residential lifetime savings.48

Appliance Recycling programs (which we also included in the product rebate category) 

48 We indicate at least 44% because other program types also can, and often do, include lighting related products.

21 

 
accounted for 6% and appliance rebates made up 2% respectively of all residential sector lifetime 

gross savings. Consumer Electronics programs, the fourth detailed program type in the consumer 

product rebate category, garnered less than 1% of residential sector savings. 

Figure 3-3. Lifetime gross electricity savings for 2009-2011 residential consumer product rebate 

programs 

We also analyzed C&I sector expenditure and savings data by simplified program type (see 

Figure 3-4) and found the following: 

• At 36%, custom programs represented the largest share of all C&I expenditures as well as 

the largest share of all C&I total lifetime savings at 38%. 

• Prescriptive and small commercial programs accounted for comparable shares of C&I 

expenditures at about 21% each; although reported lifetime savings were much greater 

for prescriptive programs (30% of all savings) compared to small commercial programs 

(11% of all C&I savings). 

• Commercial new construction programs accounted for 12% of C&I expenditures and 

10% of the sector’s savings. 

• Programs specifically targeting the institutional market (municipal and state 

governments, universities, colleges, K-12 schools and hospital/healthcare facilities, also 

collectively known as the MUSH market) made up 7% of total C&I program 

expenditures and 4% of the savings, although it should be noted that institutional sector 

customers can and do participate in many other types of C&I programs as well.

22 
Figure 3-4. Program administrator expenditures and gross lifetime savings for 2009-2011 

commercial and industrial electricity efficiency programs

We also created a region data field and coded efficiency program data provided by program

administrators into the appropriate region, using U.S. Census region definitions (see Table 3-2). 

As can be seen from Table 3-2, we have a limited number of states (four) with program-level 

data from the South region as well as a relatively limited number of efficiency programs in total 

from southern states in the database. 

Table 3-2. U.S. Census Regions and states in the LBNL DSM Program Impacts Database49

Region States in the LBNL DSM Program Impacts Database

Midwest MI, MN, IL, IA, OH, WI, IN

Northeast PA, VT, CT, ME, NH, NY, RI, NJ, MA

South MD, NC, FL, TX

West CA, WA, MT, ID, OR, HI, CO, NV, UT, AZ, NM

For the programs in the database, program administrator costs for electricity programs were 

highest for the West at $2.0 billion, followed closely by the Northeast at just over $1.9 billion. 

49 U.S. Region Definitions may be found at: 

http://www.census.gov/econ/census07/www/geography/regions_and_divisions.html

23 

 
Program administrator expenditures totaled just under $1 billion in the Midwest and about $505 

million in the South (see Figure 3-5).

Figure 3-5. Program administrator expenditures by region for 2009-2011 electricity efficiency 

programs 

The regional breakdown of lifetime savings for programs in the database looks much different 

compared to expenditures (see Figure 3-6). Program administrators in the Midwest reported 

about 20% more lifetime electricity savings than program administrators in the Northeast and 

about 75% of the savings for program administrators in the West, although expenditures in the 

Midwest were less than half of those in the West or Northeast.

As can be seen from Figure 3-5 and Figure 3-6, savings reported by program administrators 

come predominantly from the C&I sector, except for the South where residential and C&I 

program savings are more balanced. In the Midwest, C&I programs accounted for a little more 

than half of the region’s total expenditures, but C&I programs accounted for nearly 70% of the 

savings. In the West, the expenditure and savings proportions were more comparable; C&I 

programs accounted for about 60% of total expenditures and about 65% of the savings, while 

27% of expenditures and 21% of savings occurred in the residential sector. Low-income program 

expenditures were significantly higher in the Northeast than in the other regions. 

24 
Figure 3-6. Program administrator lifetime savings by region for 2009-2011 electricity efficiency

3.1.2 Gas Program Expenditures and Savings

Program administrator expenditures for identifiable natural gas programs50 in the LBNL DSM 

Program Impacts database for the years 2009–2011 totaled just under $1.3 billion, about 20% of 

program administrator expenditures for electric programs (see Table 3-3). Residential programs 

accounted for about 60% of aggregated gas program expenditures, while C&I programs 

accounted for about a quarter of total program expenditures, which is the converse of spending 

breakdown in electric efficiency programs (i.e., C&I programs account for 60% and residential 

programs about 30% of total spending). 

50 Fifty program administrators reported natural gas program data.

25 

 
Figure 3-7. Program administrator expenditures, first- year and lifetime gross savings for 2009–

2011 natural gas efficiency programs

As with the residential sector programs, we compared the share of total program administrator

expenditures with the share of first-year and lifetime savings for each market sector (see Figure 

3-7). Expenditures for the C&I sector accounted for about a quarter of total gas program 

expenditures, yet C&I programs generated more than half of total gas program savings (56% of 

first-year savings and 62% of the lifetime gross savings), indicating the importance of this sector 

for natural gas energy efficiency.

Table 3-3. Program administrator expenditures for 2009-2011 natural gas efficiency programs 

Market Sector Share of Total Program 

Administrator Expenditures

Total Program Administrator 

Expenditures (million 2012$)

Residential 58% $742

C&I 23% $291

Low Income 10% $123

Cross Sector/Other 9% $121

TOTAL 100% $1,277

On the other hand, while residential programs made up about 60% of total gas program 

expenditures, they garnered 35% of first-year savings and 40% of the total lifetime savings for 

all programs. Low income gas programs follow a similar pattern as low-income electricity 

efficiency programs, accounting for 10% of total expenditures and 6% of first-year and 5% 

lifetime savings. 

26 
3.2 Observations on the Cost of Saved Energy 

3.2.1 National Observations 

CSE values are presented as either (a) savings-weighted average values; (b) as an inter-quartile 

range with median51 values; or (c) both.52 The savings-weighted average CSE is calculated using 

all savings and expenditures at the level of analysis (e.g., region, sector, program category).53 For 

example, the national savings-weighted average CSE for the residential sector includes all the 

residential program portfolio costs in the database (even for programs without reported savings) 

divided by all the savings reported for the residential sector; thus “weighting” the CSE of larger 

programs more than small programs. The inter-quartile range and median CSE values are based 

on calculations for each individual program; thus giving equal weighting to all programs 

irrespective of their relative size (either in terms of savings or costs). The inter-quartile range and 

median CSE values exclude programs where a CSE cannot be calculated.54 

CSE values are reported using three different metrics: a cost of lifetime saved energy, a levelized 

cost of energy savings using two discount rates (3% and 6% real), and a cost of first-year energy 

savings (see Table 2-2 for definitions of these CSE metrics). Appendix E contains detailed 

national and regional levelized CSE values by sector, simplified program type and detailed 

program type; tables in Appendix E show the savings-weighted average CSE, the first quartile, 

the median, and the third quartile levelized CSE values and the total number of programs for 

each category. 

Table 3-4 shows national saving-weighted average CSE values for the identifiable electricity 

efficiency programs55 in the database. Figure 3-8 depicts the lifetime and levelized CSE values 

($/kWh) by sector. The national CSE values for electricity efficiency programs rounds to 

approximately $0.02/kWh for the levelized CSE using both the 3% and 6% real discount rates 

and a lifetime CSE (without discounting) of $0.015/kWh. 

51 The inter-quartile range is the middle 50 percent of the range of program CSE values. The median is the 

numerical value separating the higher half of a data sample from the lower half. 

52 The CSE values in this section are based on program administrator costs and gross energy savings. When used, 

the lifetime energy savings may be based on reported values or values derived from estimates of program average 

measure lifetime. See Chapter 2 for a discussion of the basis for using program administrator costs and gross 

savings, the protocol for calculating lifetime energy savings, and discussion of the limitations in the efficiency 

program data used to calculate CSE values. 

53 We have observed that program administrators are not consistent in how they report program support costs (i.e. 

administration, EM&V, marketing & education, etc.). Some program administrators reported those costs at the 

program level, others reported those costs at the sector or portfolio level, and several reported those costs as, 

effectively, separate programs. For the purposes of this report, costs associated with specific programs stay 

associated with those programs. Costs that occur at the portfolio or sector levels are included in the analysis as 

separate programs. This allows us to account for those costs at the sector and portfolio levels but may appear as 

though individual programs within the same category cost less than their counterparts who report costs at the 

program level.

54 Some programs did not report savings (e.g., education/information programs) and others were not designed to 

achieve savings (i.e. programmatic support programs including EM&V, marketing). Where savings are not reported, 

it was not possible to calculate a CSE for that particular program.

55 Eighty-eight program administrators reported electric program data.

27 

 
Table 3-4. The program administrator CSE for electricity efficiency programs by sector: national 

savings-weighted averages 

Sector

Levelized CSE

(6% Discount) 

($/kwh)

Levelized CSE

(3% Discount) 

($/kwh)

Lifetime CSE 

($/kwh)

First Year CSE 

($/kwh)

Commercial & 

Industrial (C&I) $ 0.021 $ 0.018 $ 0.015 $ 0.188

Residential $ 0.018 $ 0.016 $ 0.014 $ 0.116

Low Income $ 0.070 $ 0.059 $ 0.049 $ 0.569

Cross Sectoral/Other $ 0.017 $ 0.014 $ 0.012 $ 0.120

National CSE $ 0.021 $ 0.018 $ 0.015 $ 0.162

Values in this table are based on the 2009-2011 data in the LBNL DSM Program Impacts Database. CSE values are for program 

administrator costs and based on gross savings. Values are savings-weighted average CSE calculated using all savings and 

expenditures at the level of analysis. 

Figure 3-8. National savings-weighted average CSE for electricity efficiency programs by sector 

28 
Table 3-5 shows national saving-weighted average CSE values for the natural gas efficiency 

programs in the LBNL DSM Program Impacts Database. Figure 3-9 depicts the lifetime and 

levelized CSE values ($/therm) for gas efficiency programs by sector.56,57 Gas efficiency 

programs targeted at C&I customers had a significantly lower CSE ($0.17/therm; 6% discount 

rate) than programs targeting residential ($0.56/therm) and low-income ($0.59/therm) customers, 

indicating the importance of the C&I sector for natural gas programs. 

Table 3-5. The program administrator CSE for gas efficiency programs by sector: national savingsweighted averages ($/therm)

Sector 

(Natural Gas)

Levelized CSE

(6% discount) 

($/therm)

Levelized CSE

(3% discount) 

($/therm)

Lifetime CSE 

($/therm)

First Year CSE 

($/therm)

C&I $ 0.17 $ 0.14 $ 0.11 $ 1.61

Residential $ 0.56 $ 0.43 $ 0.32 $ 6.44

Low Income $ 0.59 $ 0.47 $ 0.36 $ 6.26

Cross Sectoral/Other $ 1.78 $ 1.55 $ 1.34 $ 12.37

National CSE $ 0.38 $ 0.31 $ 0.24 $ 3.93

Values in this table are based on the 2009-2011 data in the LBNL DSM Program Impacts Database. CSE values are for program 

administrator costs and based on gross savings. Values are savings-weighted average CSE calculated using all savings and 

expenditures at the level of analysis. 

56 Fifty program administrators reported natural gas program data. 57 There are a number of combined fuel programs that have reported interactive effects on natural gas. These impacts 

are not included in program level CSE calculations; however, they are included in portfolio and sector level 

calculations.

29 

 
Figure 3-9. CSE for natural gas efficiency programs by sector 

3.2.2 Sector and Program Level Observations for Electricity Efficiency Programs

We present CSE values at the sector and program level in this section. For simplicity, the 

remainder of this chapter presents CSE values using the levelized CSE for a 6% (real) discount 

rate (except where otherwise indicated).58 

Figure 3-10 presents the levelized CSE results on a national basis, depicting the savingsweighted average, median and inter-quartile range for each sector. We found that both C&I and 

residential electricity efficiency programs included in our database had an average levelized CSE 

of about $0.02/kWh. Looking at these sectors in more detail shows that the residential sector had

a slightly lower weighted-average CSE than the commercial sector but a higher median CSE 

(~$0.04/kWh). The CSE values for residential sector programs also had a larger inter-quartile 

range than commercial sector programs (e.g., inter-quartile range of CSE values ran from just 

58 We use a levelized CSE because we believe it is technically more appropriate for comparing resources. The 6% 

real discount rate is representative of a typical utility cost of capital. Lower discount rates result in lower CSE 

values. For example, for a program with an average measure life of 10 years for installed measures, a 6% discount 

rate results in a CSE that is about 15% higher than a 3% discount rate. There is significant interaction between 

discount rates and assumed measure lives. For example, the CSE value is 50% lower if we assume a 10 year 

measure life and 6% discount rate compared to a 20 year measure life and a 3% discount rate. See Appendix D for 

additional discussion of this issue.

30 

 
under $0.02 to $0.09/kWh for residential programs vs. $0.015 to $0.05/kWh for commercial 

programs). We suspect that this is due to the very wide range of program types in the residential 

sector.

Figure 3-10. National levelized CSE for electricity efficiency programs by sector

Low-income programs have much higher savings-weighted average and median values for the 

program administrator CSE (on the order of $0.07 to $0.08/kWh). Low-income programs 

typically have a higher program administrator CSE for several reasons. Most notably, these 

programs are designed to achieve specific social policy objectives in addition to energy resource 

acquisition goals. These programs can include a variety of health and safety actions (correct 

structural issues, window replacement, mold removal, etc.) that need to be completed prior to 

completing any efficiency upgrades, adding to the program costs. Finally, low-income programs 

are often delivered at little or no cost to participants; thus the CSE for low-income programs is 

more comparable to an all-in or total resource cost perspective (i.e., including both program

administrator and participant costs). 

The cross sector/other program category, illustrated in Figure 3-10, is quite broad and includes a 

diverse mix of program types (e.g., equipment rebate programs that include both residential and 

non-residential customers, workforce development and training programs). Thus, at a high level, 

it is difficult to draw conclusions for the sample of programs included in this category. 

At a national level, we observe a wide variation in CSE values for programs in most sectors (e.g., 

CSE values for programs in a sector have an inter-quartile range that varies by a factor of three to 

five). We also find that the savings-weighted average CSE was typically lower than the median 

value for CSE for a sector or program category (see Figure 3-11 and Figure 3-12). This suggests 

31 
that much of the savings for each sector is coming from programs or program types on the low 

end of the CSE range for that program or sector. 

Figure 3-11 and Figure 3-12 show levelized CSE values for the simplified program categories for 

C&I and residential sectors, respectively.59

The simplified C&I program categories had median values for the program administrator’s CSE 

that range from $0.01/kWh to $0.05/kWh. It is worth noting that the savings-weighted average 

CSE for custom and prescriptive rebate program categories were $0.018/kWh and $0.015/kWh, 

respectively. Since these two program categories accounted for almost 70% of C&I sector 

savings (see Figure 3-4), they tended to drive the overall CSE results for the C&I sector: 

program administrators had an average levelized CSE of less than $0.02/kWh in the C&I sector. 

The C&I programs (Figure 3-11) also had a relatively smaller inter-quartile range of CSE values 

compared to the residential program categories (Figure 3-12). 

Figure 3-11. National levelized CSE for commercial and industrial sector simplified program 

categories

59 Note that the y-axis scales for CSE are different in Figures 3-11 and 3-12, illustrating differences in the range of 

CSE values in C&I and residential sector programs.

32 

 
Figure 3-12. National levelized CSE for residential sector simplified program categories

For the residential programs, several program categories had a relatively tight range of program

administrator CSE values. For example, Consumer Product Rebate programs had an interquartile range of $0.01/kWh to nearly $0.04/kWh and a low savings-weighted average 

(~$0.01/kWh). However, the Residential Prescriptive ($0.03/kWh to $0.11/kWh), New 

Construction ($0.03/kWh to $0.11/kWh) and Whole-Home Upgrade (slightly more than 

$0.03/kWh to $0.21/kWh) program types had significantly larger ranges. There are several 

possible reasons for the larger range of CSE values in each of these program categories. The 

prescriptive simplified program category includes detailed program types that implement a wide 

variety of measures (e.g., HVAC, insulation, windows, pool pumps) as well as some generic 

“prescriptive” programs60 that often include measures also found in the Consumer Product 

Rebate category. This broad measure mix and the variation in costs and measure lifetimes 

associated with those measures are possible drivers for the wide range of CSE values for the 

prescriptive category.

60 Some programs include all their rebated measures under the same program title and it is not possible to determine 

where the majority of the savings is coming from. In these cases, the programs were categorized as “Residential 

Prescriptive.”

33 

 
For the Whole-Home Upgrade program category, the broad range of program designs and 

delivery mechanisms (this category includes audit, direct install, and retrofit/upgrade programs) 

may help explain the relatively wide range of CSE values. Figure 3-1361 shows program 

administrator CSE values for detailed program categories under the Whole-Home Upgrade 

program category. We observe that the inter-quartile range of CSE values for both direct install 

and whole-home upgrade programs ranged from about $0.03/kWh to about $0.26/kWh, with 

median values of $0.06/kWh and $0.12/kWh, respectively. Whole home audit programs have a 

much smaller inter-quartile range, from $0.03/kWh to $0.11/kWh, and a median value of 

$0.07/kWh. 

Figure 3-13. National levelized CSE for residential whole home detailed program category

Recall that about 44% of the residential sector lifetime gross savings came from lighting rebate 

programs that are part of the Consumer Product Rebate simplified program category (see Figure 

3-13). Thus, we took a closer look at the CSE results for the four detailed program types within 

this category (see Figure 3-14).

The median and average levelized CSE values for lighting rebate programs were quite low 

(about $0.01/kWh) with a small inter-quartile range (see Figure 3-14). Future investigation of 

these programs’ CSE values, savings estimates, and drivers is probably warranted given that a 

61 Note that the y-axis scale in Figure 3-13 has higher CSE values than other figures in this chapter.

34 

 
large percentage of savings came from lighting measures and that lighting CSE may rise as 

baselines (and thus perhaps savings) are lowered for many of these measures given 

implementation of more aggressive lighting equipment standards. 

Figure 3-14. National levelized CSE for residential consumer product rebate detailed program 

categories

3.2.3 Regional Observations in Electricity Efficiency Programs

In this section, we examine some of the potential underlying drivers of CSE, including region 

(i.e., geographic location), climate, and baseline building efficiency requirements. Figure 3-15

presents regional CSE values for programs in the database (see Table 3-2 for assignment of 

states to region). 

Across all programs, the savings-weighted average CSE ($0.014/kWh) and median CSE 

($0.019/kWh) values were lowest in the Midwest. This is consistent with the information in 

Figure 3-5 and Figure 3-6, which shows that program administrators in the Midwest in aggregate 

reported relatively low expenditures and relatively high savings (compared to other regions). 

Possible explanations for this phenomenon include the relative “newness” of the Midwest energy 

35 
efficiency programs and savings targets. Most of the states in this region enacted their first EERS 

targets in the late 2000s (Barbose et al. 2013). As a result, most of these states are perhaps still 

able to achieve significant savings from programs targeting low cost measures (i.e., lighting 

rebate programs). Another possible explanation is that gross savings values and/or measure 

lifetimes are higher because of baseline conditions or because EM&V practices are less mature 

in some states.

In contrast, many states in the Northeast region have consistently been running efficiency 

programs for many years, have much higher savings targets (e.g., “all cost effective” efficiency 

mandates) and relatively well established and rigorous savings evaluation requirements. In 

aggregate, program administrators in the Northeast have a higher savings-weighted CSE 

($0.033/kWh) and a much wider range of CSE values among types of programs, which possibly 

indicates that there was a broader mix of program designs and delivery mechanisms, as well as 

desire to achieve more comprehensive savings driven by state policy objectives (e.g., regulatory 

decisions or legislation that directs program administrators to achieve all cost-effective 

efficiency). 

Figure 3-15. Levelized CSE for electricity efficiency programs by region

We also looked at average CSE values for all C&I and residential programs (excluding lowincome programs) among program administrators in states (see Figure 3-16). Low-income 

programs were excluded for several reasons: (1) not all states either offer or reported information 

on their low-income programs; (2) the policy rationale(s) for low-income efficiency programs 

36 
differs among states: some states require low-income programs to pass cost-effectiveness 

screening tests while other states use multiple criteria to assess budgets and design of lowincome programs (e.g., equity reasons, cost-effectiveness); and (3) the scale of low-income 

programs varies significantly among states. Thus, including low-income program data has the 

potential to skew state by state observations in CSE. 

With several exceptions, we observe some clustering of average CSE values for efficiency 

programs for states in a region (see Figure 3-16) with several exceptions (e.g., FL, PA, NJ). It is 

worth noting that Massachusetts and Vermont have all cost-effective efficiency mandates and 

both of those states had a savings-weighted average CSE over $0.04. Conversely, Pennsylvania 

has many characteristics that are typical of other states in the Midwest (e.g., relatively new 

efficiency programs, similar climate, economies) and had an average savings-weighted CSE 

more similar to program administrators in the Midwest than the Northeast. At this time, we 

cannot definitively explain the higher savings-weighted average CSE for program administrators 

in Florida. 

Figure 3-16. CSE values by state for electricity efficiency programs (excluding low-income 

programs) 

37 
A number of factors may influence the observed variation in the program-level CSE, including 

those that program administrators can influence (e.g., how program administrators report 

program costs, program design, incentive levels, and measure mix) and those largely outside of 

program administrator control (e.g., climate, area labor rates, building stock, regulatory 

requirements). We conducted exploratory analysis that examined two potential factors that may 

influence program-level CSE values: climate and building codes. First, we calculated the 

percentage of each region’s lifetime gross savings by savings-weighted program administrator 

CSE and climate zone for all program categories in the database (see Figure 3-16). The size of 

the bubbles in Figure 3-17 represents the percentage of the total regional lifetime savings that 

falls within the respective climate zone in which the program was administered. For example, for 

the West, there are more savings in the database in the warm climate zone that includes much of 

California. 

Figure 3-17. Percent of regional lifetime savings by climate zone and levelized CSE for electricity 

efficiency programs62

62 States were assigned to climate zones adopted for the International Energy Conservation Code (IECC), in which 

the climate zones are delineated geographically as regions defined by certain historical averages for temperature, 

humidity and precipitation. A single zone was assigned to each state based on where the majority of the state's 

population—and presumably load—is concentrated. This method is imperfect but useful as a proof-of-concept test 

for an approximate relationship with levelized CSE. A description for the climate zones was adapted from the 

38 

 
In each region, we observe a pattern that as the climate gets cooler, the savings-weighted average 

CSE decreases for electricity efficiency programs. However, we also see that the savingsweighted average CSE varied significantly within a climate zone (see mixed and cool). Had 

climate been a significant driver for CSE, we would expect to see more agreement on the CSE by 

climate zone, even in different regions. This indicates that there are probably other factors that 

have more impact on the regional CSEs than climate zone. Additional analyses may be required 

to focus only on program types with climate dependent measures (e.g., cooling and heating 

system retrofits) or conduct more detailed analysis of participant costs and incentives which can 

vary by climate zone as cost effectiveness varies (e.g., a cooling system retrofit would be more 

cost-effective in a very hot climate than a cool one, possibly justifying higher incentives, but also 

perhaps not requiring them since the participant benefit to cost ratio would also be higher). 

Figure 3-18. Levelized CSE for residential new construction programs compared to residential 

building energy codes adopted by states in each region63

Building America discussion of IECC and Building America climate zones found here: 

http://apps1.eere.energy.gov/buildings/publications/pdfs/building_america/ba_climateguide_7_1.pdf 63 U.S. DOE. 2013. Building Energy Codes Program. Washington, DC. Accessed at: 

http://www.energycodes.gov/status-state-energy-code-adoption in September 2013.

39 

 
Another potential influence on CSE values is differences in baseline building efficiency across 

states and regions. In Figure 3-18 and Figure 3-19, we examine the savings-weighted average 

CSE for new construction programs in the residential and commercial sectors, respectively. For 

the residential programs, we calculate the savings-weighted average electric levelized CSE for 

new construction programs in each region plotted against each state’s current International 

Energy Conservation Code (IECC) status.64,65 The newer the adopted code, the lower the 

assumed baseline energy consumption, which tends to reduce the incremental electricity savings 

for any given efficiency action. For example, the gross savings calculated for a fixed set of 

measures for a building than meets the 2006 IECC code would be greater than for the same set of 

measures for a building that meets the 2012 IECC code. Note that the West, as a region, has the 

most diversity among states in terms of building energy code requirements. 

Figure 3-19. Regional levelized CSEs for commercial new construction programs compared to 

commercial building energy codes adopted by states in each region66

64 The IECC (http://www.iccsafe.org/gr/Pages/IECC-Resource.aspx) is a national model energy code for the United 

States. It sets minimum requirements for energy efficiency that new buildings—as well as additions and renovations 

to existing buildings—must meet wherever the code has been adopted into law, usually on state-by-state basis. The 

IECC is updated on a 3-year cycle, and the most recent version is 2012. 65 By using current (2103) IECC code adoption status, we do not directly reflect the baseline status at time of 

program implementation (2009-2011). However, we expect that this approach may still be indicative of relative 

baseline status while not requiring state-by-state, year-by-year analysis of code status. 66 U.S. DOE. 2013. Building Energy Codes Program. Washington, DC. Accessed at: 

http://www.energycodes.gov/status-state-energy-code-adoption in September 2013.

40 

 
It might be reasonable to expect that the CSE would increase as the codes for new buildings set 

more stringent baseline efficiency requirements (e.g., incremental savings opportunities are less 

for any given investment). Some evidence for this pattern can be observed in the average CSE 

values for Midwest, Northeast and South residential programs segmented by the year of the 

building energy codes. However, the expected pattern in average CSE values does not readily 

emerge for states in the West that offer residential new construction programs. 

The picture is even less clear when looking at the savings-weighted CSE for commercial new 

construction programs plotted against commercial codes (see Figure 3-19). CSE values do not 

follow the expected pattern for states in either the West or Midwest. The savings-weighted 

average CSE values for states in the Northeast seems to have been lower where more stringent 

codes exist, although there are a limited range of code requirements among states in the 

Northeast. Thus, the effects of code status on CSE values require further inquiry. 

3.2.4 Sensitivity Analysis: Impact of Measure Lifetime

In Chapter 2, we discussed data gaps and inconsistent criteria for reporting lifetime energy 

savings (and by extension efficiency measure lifetimes), noting that lifetime savings (or program 

average measure lifetime) were not reported for about 50% of the program years in the 

database.67 In this section, we illustrate and discuss results of a sensitivity analysis that explores 

the impact of varying assumptions regarding program measure lifetime on CSE values reported 

by program administrators. 

Figure 3-20 compares the “LBNL approach” used to estimate lifetime savings for those 

programs that did not report this information to two other potential approaches in which we 

apply the minimum and maximum reported program average lifetimes for each detailed program 

type to all programs of that type. 

The minimum and maximum values for each program type (see the light and dark green bars in 

Figure 3-20) dramatize the impact on levelized CSE values of varying assumptions for the 

average measure lifetime of efficiency programs. For five of the 12 reported program categories, 

if we use the minimum reported program average lifetime (and apply it to all other programs in 

that category), the levelized CSE values more than doubles compared to the CSE values using 

the LBNL measure lifetime approach. This underscores the importance of understanding and 

accurately reporting the average measure lifetime of measures installed in programs since it 

significantly impacts the cost of saved energy (and the underlying cost-effectiveness of 

efficiency actions).

67 For those programs, we calculated a program-average measure lifetime by detailed program category and applied 

those values to the reported gross first-year savings to calculate lifetime savings.

41 

 
Figure 3-20. Impact of different program average measure lifetime assumptions on the levelized 

CSE for electricity efficiency programs

3.2.5 Program Administrator and Participant Cost Analysis: The Total Resource Cost of Saved 

Energy

This study focuses primarily on the program administrator CSE because participant costs were 

not consistently reported. We collected participant costs at the program level when reported, 

although this information was available for only 265 electric programs years (less than 10% of 

the programs in the database) in 11 states.68 When reported, participant costs are subject to at 

least two additional sources of uncertainty: (1) whether the participant costs are based upon full 

program measure costs or incremental program measure costs; and (2) whether participant costs 

are based upon customer receipts and/or supplier invoices (i.e., actual participants paid those full 

costs) or whether incremental participant costs are based upon deemed values drawn from 

various sources (e.g., supplier surveys). 

68 In some of the 11 states, participant costs are only reported for select programs and not the entire portfolio. 

42 

 
Given small sample size and uncertain reporting of participant costs, it is difficult to assess the 

“all-in” or total resource cost of efficiency or analyze potential influences on the total cost of the 

efficiency resource. For these reasons, in Figure 3-21, we compare the program administrator’s 

levelized CSE vs. a total resource CSE for illustrative purposes only. We calculate this total 

resource CSE for the simplified program categories where both program administrator and 

participant costs were available for more than 18 program years.69

For the small sample of programs, we found that the levelized total resource CSE values are 

typically double for most program types with the exception of the Residential Whole Home 

Upgrade program category (where the total resource CSE is about 25%–30% higher than the 

program administrator CSE). Further data collection and analyses could help understand how the 

ratio of program administrator to participant costs varies as a function of sector, measure types, 

and market maturity; and how incentives and direct support might be optimized to pay no more 

than is necessary to meet efficiency uptake objectives.

Figure 3-21. Levelized savings-weighted average CSE for electricity efficiency programs that 

include program administrator costs vs. total resource costs for select program categories70

69 The “n” of 18 was selected because there was a natural break in the data and also that criteria resulted in only 

including results for which there was a meaningful number of programs from which to calculate average values. 70 This chart includes a very small sample of programs from 11 states; thus, results may not reflect current practices 

in many jurisdictions. 

43 

 
4. Testing Influences on the Costs of Saved Energy

As shown in Chapter 3, we observe a wide range of values for the program administrator CSE 

from virtually every perspective—nationally, and across regions, states, portfolios, and sectors. 

Moreover, we find significant variability within the different types of programs. The interquartile range of CSE values (the “middle” 50% of programs) for the first-year CSE can vary by 

a factor of 10 or more within a program category. In this chapter, we explore some factors that 

may be associated with this variability in the CSE. We describe the results of statistical analyses 

aimed at quantifying the relationship of CSE and a few, selected independent variables. 

To initiate these analyses, we postulated three sets of potential explanations for these ranges of 

CSE values:

• Differences internal to the programs themselves and over which program 

administrators have at least some influence (e.g., the mix of measures in programs 

and thus the adoption patterns of consumers, the scale of programs, the maturity of 

the programs, program design, and program implementation); 

• Differences external to the programs and over which program administrators have 

very little or no influence (e.g., climate, labor costs, and the policy framework within 

which programs operate).

• Incorrect information arising from problems with the primary data or faulty 

categorization of programs, or both (e.g., if gross energy savings are inaccurately 

reported in the source reports).71 

We suspect that most or all of these factors influence the CSE values, interacting in ways that 

can be difficult to disentangle. In this chapter, we focus on the first two explanations (i.e., 

potential internal and external program influences) in order to see if their hypothesized 

influences on CSE are observed or not, using the programs in the database.72

In the long run, we hope the collected data and this type of statistical analyses can:

• Inform policymakers and other stakeholders about the variability of the CSE to 

distinguish between controllable and uncontrollable sources of variability and, 

ideally, to identify ways of reducing costs or otherwise improving program design 

and delivery; and 

• Lead to predictive models that specify and quantify major influences on CSE values 

and thus could inform cost or savings projections for use by portfolio planners, 

regulators, and resource planners.

71 See Chapter 2 for a discussion of data issues and Appendix C for a description of the quality control procedures 

implemented for this project.

72 As noted in Chapter 3, CSE values are derived as follows: Program costs refer to program administrator costs 

only; the CSE values exclude participant costs. Savings are gross savings as reported by the program administrator. 

When program administrators only reported net savings values and we either had or could derive program-specific 

net-to-gross ratios, we used those ratios to calculate gross savings values from reported net savings. Savings values 

are based on savings at the end-use site and not at the power plant or natural gas pumping station and thus do not 

account for transmission and distribution losses.

44 

 
4.1 Hypotheses 

Table 4-1 indicates five hypotheses postulated as part of this research effort. We present results 

for three of these hypotheses in this report (shown in black).73 Future reports may provide more 

in-depth results for these hypotheses and analyses of other hypotheses (shown in gray), both 

indicated in Table 4-1 and under development. 

Table 4-1. Factors that may influence the cost of saved energy

Factors that 

May Influence 

the Cost of 

Saved Energy

Hypotheses Proxy Variables

Level at which 

Variable Was 

Tested

Sources for Proxy 

Variable Data

Program 

Administrator 

Experience 

Program administrators

with more experience 

learn to deliver 

programs more 

effectively and 

efficiently, with 

resulting lower CSE

Years of energy 

efficiency program 

spending from 

1999-201274 above 

a de minimis

threshold 

Portfolio and 

sector levels

U.S. Energy 

Information 

Administration 

Form 861 survey75

data, 1999-2012

Scale of Program Larger programs reap 

economies of scale and 

thus have lower CSE

Number of program 

participants

Sector and 

simplified and 

detailed 

program level

LBNL DSM Program 

Impacts Database

Labor Costs

 

 

Areas with higher labor 

costs have higher CSE 

because labor is a 

significant component 

of both administrative 

and (indirectly) 

incentive costs.

State average 

wages for the 

construction 

industry

Portfolio, 

sector, and 

simplified and 

detailed 

program levels

U.S. Bureau of 

Labor Statistics

State Policy 

Environment 

Strong efficiency policies 

can both raise the 

baseline for energy 

savings potential and 

drive program 

administrators to reach 

deeper into the 

economy for savings; 

over time, both factors 

Estimated statewide 

savings targets, as a 

percent of retail 

sales 

Portfolio, 

sector, and 

program levels 

Various reports by 

LBNL and ACEEE 

State Scorecards

73 We plan to explore other hypotheses in future reports. 74 This period was chosen largely because reporting of energy efficiency program spending and savings to EIA was 

less consistent in the early 1990s. See subsection on preliminary findings on program administrator experience for a 

discussion of the implications of selecting this period. 75 We measured experience as the number of years that each program administrator has funded program portfolios at 

0.1 percent of retail revenues for that program administrator or for utilities in that program administrator’s territory. 

Where a time series of program funding could not be obtained (e.g., through gaps in reporting or delayed recognition 

of a non-utility program administrator in the survey data), we used the launch date for a multi-sector portfolio by 

that program administrator or, in a few cases, relied upon in-house knowledge of the level of energy-efficiency 

activity by that program administrator. 

45 

 
are likely to result in 

higher CSE. 

Retail Rate 

Environment 

Higher retail energy 

costs result in lower CSE 

because the higher 

energy costs encourage 

more customers to 

invest in energy savings, 

thus lowering the 

program administrator’s

costs of securing 

participation and savings

Residential, 

commercial and 

industrial retail 

rates

Commercial and 

Industrial (C&I) 

and residential 

sectors

U.S. EIA 826 and 

861 reports (the 

Monthly Electric 

Sales and Revenue 

Report with State 

Distributions 

Report and the 

Annual Electric 

Power Industry 

Report)

Through the exercise of developing the hypotheses and identifying associated independent 

variables, it became clear that several of our theorized influences on the CSE interact in complex 

ways. Several variables operate in synergistic or countervailing ways. For example, some 

policies that are generally supportive of saving energy (e.g., energy savings targets) may dampen 

the costs of saving energy for program administrators in some circumstances and yet increase 

those costs under other circumstances. Further, the resulting effects may not operate uniformly or 

in the same direction from one market sector to another or across program types. Thus, the 

identification of potential influences on the CSEs, development of testable hypotheses and 

identification of valid independent variables is an iterative process, the early phases of which are 

described below.

4.2 Approach

For our dependent variable, we chose the first-year electric CSE, which is simply the program 

administrator cost (2012$) divided by first-year gross electricity savings (in kWh). The primary 

advantage of using first-year savings (versus 

lifetime savings) is eliminating uncertainties 

associated with the measure lifetime data; see 

Chapters 2 and 3 for discussion of limitations of 

lifetime energy savings data. 

The disadvantage of using first-year savings is 

the inability to examine the ways that potential 

influences on CSEs vary for shorter- versus 

longer-lived efficiency measures, as using a levelized or lifetime CSE might allow. Since energy 

resources are generally evaluated over their economic lifetime, we anticipate analyzing factors 

that may be associated with levelized CSE values. 

We identified and collected data on the independent variables as proxies for the factors chosen to 

represent the potential influences over CSE. We then performed single-variable ordinary least 

squares regressions to screen independent variables, followed by a limited number of 

multivariate regressions to test the correlation between variables and the relative contributions of 

the variables. Appendix F describes our data collection procedures for the independent variables, 

the statistical analysis process and contains a table of these preliminary regression results. 

Statistical Regressions

Statistical regressions do not necessarily imply 

causality. Regressions can establish correlation 

or a probability that changing one or more 

independent variables is significantly associated 

with a quantifiable change in the dependent 

variable (e.g., the CSE).

46 
4.3 Preliminary Results: Analysis of Factors that May Influence the Cost of Saved 

Energy

Our preliminary results to date suggest that many factors influence the CSE, and the degree of 

those influences varies across market sectors and programs. In the following subsections, we 

present an illustrative sampling of preliminary results and also discuss some of the challenges in 

identifying valid independent variables and interpreting results.

4.3.1 Program Administrator Experience

We hypothesized that program administrators with more experience would, to some 

demonstrable degree, have optimized the efficacy of program implementation and thus have 

lower CSE values for their portfolio of programs after an initial period. Experienced program 

administrators might realize these cost savings by one or more mechanisms, including having 

already established the necessary program infrastructure and trade alliances, identifying cost 

efficiencies in overhead expenses, and learning what measures and marketing approaches tend to 

elicit more customer participation or deeper savings. 

We defined the program administrator experience variable as follows: each year of spending 

above a minimum program spending threshold (0.1% of revenues) as reported to the Energy 

Information Administration counted as a year of experience administering efficiency programs.76

Years of experience were summed up for all years where spending exceeded the threshold to the 

program year for the data being tested. For example, utility X offered an informational energy 

audit program to customers in 2004 and expanded their programs in subsequent years such that 

spending exceeded 0.1% of revenues in 2006. Thus, we assumed that this utility had four years 

of experience for their 2010 programs and five years of experience for their 2011 programs. 

The nature of the relationship between first-year CSE values and program administrator 

experience is depicted in Figure 4-1. The blue dots in Figure 4-1 represent CSE values for the 

portfolio of programs offered each year by individual program administrators. The cost of firstyear gross electricity savings is plotted on the y-axis, the years of program administrator

experience are shown on the x-axis. 

There may be a quadratic relationship, such that program administrator experience and the cost 

of first-year savings may trace a curve in which first-year CSE declines as program 

administrators gain experience and then, beyond a certain number of years, costs increase, as 

76 See Appendix F for a more detailed explanation of the basis for determining program administrator years of 

experience. Response rates vary among program administrators from year to year in providing EIA Form-861 

information. Third-party program administrators were not included in the EIA datasets until very recently. The 

names and parent companies for some program administrators changed over time. Some EIA survey data terms and 

definitions have changed over time and program administrators may have interpreted those terms (e.g., direct vs. 

indirect spending) in different ways. These limitations increase as the data reaches back to the early years of the EIA 

survey. We therefore chose to limit the count of years above the spending threshold to a period from 1999 to 2012. 

We recognize that bounding our metric for program administrator experience to this 14-year period imposes an 

artificial ceiling on the level of experience for the most mature program administrators. This may affect the 

correlation between program administrator maturity and the cost of saved energy. However, this impact is likely to 

be limited because 80% of the program administrator s in our dataset have spent above the designated spending 

threshold for 10 or fewer years. 

47 

 
saturation of low cost measures increases and program administrators offer programs that include 

more costly measures or target harder to reach market segments. However, a regression analysis 

with a quadratic specification using the first-year CSE values at the portfolio level does not show 

a statistically significant relationship,77 and the magnitude of the effect, if it exists, is small (see a 

table of regression results in Appendix F). We plan to gather additional data, refine our method 

to estimate program administrator experience variable, and re-examine evidence for this 

relationship. 

Figure 4-1. First-year portfolio-level CSE and program administrator experience, as measured by 

years of program spending above a minimal level. 

4.3.2 Scale of Program

Based on economic theory, we would expect to see increasing economies of scale (i.e., lower 

CSE values as program fixed overhead costs are spread among more participant projects) at least 

up to a certain point. We found that the size of a program, as measured by number of 

participants, is often, but not always, indirectly associated with a decline in costs for some 

program types. This result is statistically significant for only certain program types. More 

reporting of participation levels could help determine, for different program types, when scaling 

up a program is likely to reduce the cost of saved energy. 

As an example, Figure 4-2 depicts the relationship of participant count to first-year CSE for 

residential appliance recycling programs. The blue dots in Figure 4-2 represent first-year CSEs 

77 We use a 5% level as a threshold for statistical significance.

48 

 
and reported participation for individual program years for appliance recycling programs. The 

red line is a linear fit across the data points, with the slope of the line indicating the predicted 

relationship between first-year cost performance and participation. For appliance recycling 

programs in our database, a doubling, or 100% increase, in the number of participants would, on 

average, be associated with about 0.01% of a reduction in the first-year CSE. This effect is 

statistically significant at the 5% level. 

However, we also found that this effect is not statistically significant78 for many other program 

types.

Figure 4-2. First-year CSE for appliance recycling programs and the reported number of recycling 

program participants 

78 The relationship between participation and first-year gross CSE for some other residential programs is statistically 

significant at the 20% level.

49 

 
4.3.3 Labor Costs 

We also theorized that higher labor costs result in higher CSE values (see Table 4-1). We present 

portfolio-wide CSE values as a function of state average hourly wages for construction industry 

employees in Figure 4-3. The blue dots represent CSE values for individual program 

administrator portfolios with the cost of first-year gross electricity savings plotted on the y-axis 

and the average hourly construction wages for the state in which the portfolios are administered 

on the x-axis. 

Figure 4-3. First-year portfolio-level CSE values and state average wages for construction industry 

employees ($/hour)

We selected construction hourly wages at the state level as our independent variable because 

research on the makeup of the energy-efficiency program workforce suggests that the 

construction industry is generally representative of that workforce (Goldman et al., 2010; Carol 

Zabin, UC-Berkeley Labor Center, personal communication). Our analysis shows that there is a 

positive correlation between construction wages and portfolio-level first-year gross CSEs. This 

result is statistically significant at a 5% level. However, the demonstrated effect is generally 

small, as can be seen from the fairly shallow slope of the fitted line in Figure 4-3. The effect is 

also neither uniform nor statistically significant across individual program types. As an aside, we 

also tried state average per capita income as the independent variable and found that the results 

50 
are similar to those using construction hourly wages; this seems to indicate that labor costs are 

likely to play some role in the cost of saving energy. 

4.4 Analytical Challenges 

We also conducted exploratory analysis of other hypotheses (e.g., policy and retail price 

environments in which programs operate) and found that results varied substantially by market 

sector and program type. Many of these theorized relationships with the CSE are significant only 

at the 10%-15% level; further study is warranted.

The statistical analysis results described in this chapter depend critically on defining valid 

independent variables as well as the quality and quantity of the primary data underlying both the 

independent and dependent variables. Some of the difficulty in parsing these effects is a function 

of limitations in the underlying data for the independent variables. Drawing on an example noted 

earlier, we used data that program administrators voluntarily reported to the Energy Information 

Agency (EIA) to develop proxies for years of administrator experience. Program administrators 

sometimes do not report spending for every year or have interpreted EIA survey questions in 

different ways. More work is needed to minimize these and other sources of error or uncertainty 

in values for the independent variables.

Another challenge is specifying independent variables that are not highly correlated with other 

variables, that is, some proxies for influences on CSE can be overlapping in effect. For example, 

program administrators with more experience usually are required to achieve higher levels of 

savings. States that have higher labor costs also often have higher retail rates. 

Likewise, it can be difficult to examine economies-of-scale questions when participation data are 

not provided. No participation data are reported for more than two-thirds of the program years in 

the database. In other cases, the data may be incorrect (numbers identified as participants are 

actually units sold or assumed installed) or ambiguous (unit and participant numbers are comingled or undifferentiated). Finally, many other questions pertinent to program design and 

delivery could be tested if spending breakdowns were available by program (i.e., program 

expenditures disaggregated into customer incentives, various categories of administration, 

marketing and outreach, and evaluation). 

The primary data contained in the database have limitations, as discussed earlier. For the 

regression analysis, our total sample size was 2,035 data points. Many of the program years in 

the database are for gas-only programs, which are not included in an analysis of electricity 

program CSEs. Moreover, for some programs, the administrator did not report a key value (e.g., 

did not include program-level spending or allocate program costs by fuel for combination 

electric-gas programs). 

51 
5. Discussion of Key Findings and Recommendations

In this chapter, we summarize key findings from this initial report of the LBNL CSE Project and 

discuss opportunities for improving information provided by program administrators on the costs 

and impacts of efficiency programs. 

5.1 Key Findings

We calculated the administrator costs of saving a unit of natural gas or electricity and reported 

the CSE in several ways, through first-year savings, lifetime savings and levelized savings. It is 

important to note that the CSE values presented in this report are retrospective and may not 

necessarily reflect future CSE for specific programs, particularly given updated appliance and 

lighting standards. The cost of efficiency as a function of first-year energy savings may be useful 

for budgeting to meet incremental annual savings targets. The cost of lifetime energy savings 

captures the efficiency that accrues throughout the effective lifetime of the implemented 

measures and therefore is more broadly applicable in designing programs and portfolios. In this 

study, we focused more attention on the program administrators’ levelized cost of energy savings

based on gross savings because relatively few program administrators reported the cost 

contributions of participants (or incremental measure costs) or net savings values. In future 

reports, our goals are to also provide the “all-in” or total resource CSE and to include CSE values 

based on net savings as well. 

Key findings from this study are:79

• The U.S. average electricity CSE was slightly more than two cents per kilowatt-hour 

in the period 2009-2011 when gross savings and spending are aggregated at the 

national level and the CSE is weighted by savings.80 This levelized CSE is somewhat 

lower than reported by other previous studies. In a 2009 study, for example, Friedrich 

et al. found an average program administrator levelized CSE of $0.025/kWh in 

constant 2007 dollars or $0.027/kWh in constant 2012 dollars—about 29% higher 

than is reported here.81 The LBNL DSM Program Impacts Database contains a larger 

sample of program administrators, many of whom may have used longer program 

measure lifetimes that could affect CSE values. Moreover, nearly 40% of the program

administrators in the database that administer electric efficiency programs have 

offered programs for less than four years and so may be early in accessing energy 

savings in their respective state economies or be targeting the least costly savings 

opportunities first.82 

• Other findings for electricity efficiency programs include:

79 All values reported here are program administrator CSEs for gross energy savings, levelized at a 6% real discount 

rate and given in constant 2012 dollars.

80 This average value is based on the efficiency program portfolios of 100 electric and electric-gas program 

administrators that represent just less than half of the program spending in the United States during 2009 through 

2011. These PAs are a large and diverse group in terms of geography, baseline efficiency, and historic levels of 

program activity.

81 Friedrich et al. used a slightly lower discount rate (5 percent vs. 6 percent used in this report), so that the actual 

difference is larger.

82 See Appendix A for summary of current and previous CSE research. 

52 

 
o Residential electricity efficiency programs had the lowest average levelized CSE 

at $0.018/kWh. Commercial, industrial and agricultural (C&I) programs had a 

slightly higher average levelized CSE at $0.021/kWh. Low-income programs 

show an average levelized CSE at $0.070/kWh. 

o In reviewing regional results, the Midwest programs had the lowest average 

levelized CSE ($0.014/kWh) and the Northeast programs the highest 

($0.033/kWh). The average levelized CSE values for programs in the West and 

South, to the extent sufficient reporting was found, were $0.023/kWh and 

$0.028/kWh, respectively. 

o The database provides a valuable resource for understanding the composition and 

the CSE for various efficiency measures and program types. For example, at least 

44% of the reported gross savings in the residential sector came from dedicated 

lighting programs and lighting rebate programs had a savings-weighted average 

CSE of $0.007/kWh with a small inter-quartile range. 

• Natural gas efficiency programs had a national, program administrator savings 

weighted CSE range of $0.24 (lifetime CSE) to $0.38 per therm (levelized CSE, 6% 

discount rate), with significant differences between the commercial/industrial and 

residential sectors ($0.11–$0.17 vs. $0.32–$0.56 per therm respectively). 

• Not surprisingly, the levelized CSE varied widely both among program types and 

within program types. We found that the median value was typically higher than the 

savings-weighted average for nearly all types of programs. One possible explanation 

is that our sample includes a number of very large programs and for any given 

program type, larger efficiency programs have lower CSE than smaller programs 

because administrative costs are spread over more projects (e.g., economies of scale). 

Some of our statistical analyses tend to demonstrate this relationship; however, other 

factors are probably at work as well. 

• The “all-in” or total resource cost of energy savings is subject to the uncertainties and 

very limited availability of information on participant costs. Based on our small 

sample of programs that reported participant costs, we found that the program 

administrator costs account for about a third to a half of the total CSE (including 

program administrator and participant costs). One exception is residential WholeHome Upgrade programs in our database, for which the median value for the program

administrator’s CSE is closer to three-quarters of the median CSE value that includes 

both program administrator and participant costs. 

• We developed several hypotheses regarding factors that may influence the variability 

in the cost of saved energy. Preliminary statistical analyses of cost of first year energy 

savings suggest that myriad factors both internal and external to program design and 

implementation play some role in influencing the CSE: 

o Program administrator experience and the cost of first-year savings may show a 

curve where first-year CSE declines as new program administrators gain 

experience and then, beyond a certain number of years, costs increase, consistent 

with administration of portfolios that have matured beyond acquiring the least 

expensive resources. However, the demonstrated effect is generally small and not 

statistically significant at this time. 

53 
o Higher construction labor costs are associated with higher costs of energy savings 

at the portfolio level. However, the demonstrated effect is generally small and is

not uniform (or statistically significant) across all types of programs. 

o The size of a program, as measured by the number of participants, is associated 

with a decline in costs for some types of programs, suggesting that certain 

programs (e.g., Appliance Recycling programs) can achieve economies of scale 

by spreading fixed overhead across more projects. However, we also found that 

this result is not statistically significant for many other types of efficiency 

programs. More reporting of participation data could help determine when scaling 

up a program is likely to reduce costs and for what program types. 

5.2 Discussion: Program Data Collection and Reporting 

Program administrator annual reports are typically the product of state regulatory requirements 

or traditional practices that have evolved over time. In compiling and analyzing more than 4,000 

program-years of data, we discovered a wide spectrum in the level of detail and completeness in 

annual program reporting. Barbose et al. (2013) found that over 45 states are running utility 

customer-funded efficiency programs. Many program administrators report program-level data at 

a very high level of completeness and transparency. However, we also found many examples of 

annual reports from program administrators that do not provide a complete picture of the impacts 

or costs of the efficiency investments at the program level. Although these reports may meet 

regulatory requirements in their state, they were not sufficient for the purposes of CSE analysis 

and therefore we were not able to include results from program administrators in many states. 

With respect to current program reporting practices, we found: 

• Inconsistencies in the quality and quantity of the costs and savings data which led 

LBNL to develop and attempt to apply consistent data definitions in reviewing and 

entering program data: 

o Program administrators in different states did not define savings metrics (e.g., 

varying definitions of net savings) and program costs consistently; and 

o Market sectors and program types were not characterized in a consistent fashion 

among program administrators. 

• Many program administrators did not provide the basic data needed to calculate a 

CSE at the program level (i.e., program administrator costs and annual and lifetime 

savings), which introduced uncertainties into the calculation of CSE values. 

This project brought into sharp relief the challenges of creating a program spending and savings 

database and calculating reliable, internally consistent metrics for assessing programmatic 

energy efficiency. For example, program measure lifetimes are essential for converting annual to 

lifetime savings while participant costs are essential for calculating the total resource costs of 

energy savings. We believe that nearly all program administrators must collect this information 

in order to satisfy cost-effectiveness screening requirements, yet many program administrators 

did not include this information in their annual efficiency reports: 

• Less than 45% of electric program administrators reported lifetime savings;

• About 25% of electric program administrators reported program measure lifetimes;

54 
• Only about half of electric program administrators reported both net and gross annual 

savings; and 

• Less than a third of electric program administrators reported participant costs.

As a practical matter, the quality and quantity of program data reported by program 

administrators is an important factor in assessing energy efficiency as a resource in the utility

sector. Therefore, we encourage further efforts to improve consistency in program administrator 

reporting of this information. 

Regional and national policymakers have also expressed increasing interest in integrating energy 

efficiency as a resource and the value of transparent and complete reporting of program metrics 

as a foundation for increasing their confidence in this resource.

83 For example, ISO-New 

England, New York ISO and PJM Interconnection are collecting, or are considering collecting, 

demand-side spending and savings data from program administrators.84 One objective is to 

develop better load forecasts in order to inform transmission planning, market development and 

operations. A second objective is to gain visibility into the future for wholesale energy and 

capacity markets. More rigorous and consistent reporting can help energy markets count and 

confidently value energy efficiency resources. Finally, all stakeholders that are engaged in any 

aspect of the efficiency effort share an interest in making energy-efficiency portfolios as cost 

effective as possible; consistent and more standardized reporting of efficiency program data and 

metrics are a prerequisite for this to occur. 

We believe that there is a direct connection between the maturation of energy efficiency as a 

utility and national resource and increased consistency in periodic reporting of efficiency 

program costs and impacts. Additional rigor, completeness, standard terms, and consensus on at 

least essential elements of reporting could pay significant dividends for program administrators 

and increase confidence among policymakers and other stakeholders. With more consistent and 

comprehensive reporting of program results, we may obtain additional insights on trends in the 

costs of energy efficiency as a resource as program administrators scale up efforts, why those 

costs might vary from place to place and year to year, what saving energy costs among an array 

of strategies and what cost efficiencies might be achieved.

83 The Northeast Energy Efficiency Partnerships’ (NEEP) Regional Evaluation, Measurement and Verification 

Forum (EM&V Forum) supports the development and use of common, consistent protocols to evaluate, measure, 

verify, and report the savings, costs, and emission impacts of energy efficiency. The EM&V Forum has developed 

the Regional Energy Efficiency Database (REED), launched in early 2013, which includes data from eight states, 

soon to be nine states and the District of Columbia. REED was informed by the Forum’s “Common Statewide 

Energy Efficiency Reporting Guidelines,” which were adopted by the Forum’s Steering Committee in 2010. See 

http://neep.org/emv-forum/about-the-emv-forum/index. 84 The NY ISO and ISO NE develop projections on efficiency program impacts based on future program budgets 

and cost information about past program performance. See, e.g., the NY ISO 2013 Gold Book 

(http://www.nyiso.com/public/webdocs/markets_operations/services/planning/Documents_and_Resources/Planning

_Data_and_Reference_Docs/Data_and_Reference_Docs/2013_GoldBook.pdf) and the 2014 Energy-Efficiency Data 

Review by the ISO NE Energy-Efficiency Working Group at http://www.isone.com/committees/comm_wkgrps/othr/enrgy_effncy_frcst/2014mtrls/final_2014_eefwg_data_review.pdf

55 

 
Therefore, we urge state regulators and program administrators to consider annually reporting 

certain essential data fields at a portfolio level and more comprehensive reporting of programlevel data in order to facilitate benchmarking of efficiency program results at state, regional, and 

national levels. The reporting hierarchy in Figure 5-1 illustrates this approach.

Figure 5-1. Components of annual energy efficiency program reporting

The program information included in each circle above correspond to gradually increasing 

visibility into program performance, increasing confidence in the reported values and potential 

relevance to policymakers and more stakeholders across broader geographic areas. The most 

basic level of reporting (light blue background) provides information that state regulators can use 

to ensure that programs are available to all customer classes and are cost-effective as 

implemented. The next level of reporting (teal background) provides critical information for 

calculating the CSE, assessing program efficacy and market penetration, and ensuring savings 

are attributable to program activities. The third level of reporting (purple background) enables 

comparisons of programs and cost performance in different states, reinforces assessments of 

program efficacy, and allows visibility into key assumptions to ensure those assumptions are 

valid and comparable to those used by other program administrators.

85

85 The components of annual reporting in Figure 5-1 are not exclusive. A number of states require significantly 

more, including indicators of performance on multiple fronts. Examples include estimates of market penetration; 

estimates of economic impacts; and cost breakdowns by internal spending, payments to or for external evaluations, 

payments to implementation contractors, payments to installation contractors, etc. 

56 

 
If program administrators were to report, at a minimum, the data under the baseline guidelines, 

this analysis would include nine additional program administrators among the 31 states included 

in this study, and programs from at least an additional 14 states. This would facilitate a more 

comprehensive national analysis of the impact of utility-customer funded energy efficiency. 

We also encourage program administrators, regulators and other stakeholders to provide 

feedback on our efforts to encourage consistent reporting of efficiency program results, 

particularly the program typology and data definitions. We will be soliciting input more formally 

as we move forward with the next phases of this project. Given sufficient interest and resources, 

it is our hope to update the LBNL DSM Program Impacts Database on a periodic basis and 

prepare comprehensive reports and policy briefs that are publicly available that explore key 

issues in energy efficiency programs. 

57 
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59