The Sustainability and Costs of Increasing Efficiency Impacts: 

Evidence from Experience to Date 

Kenji Takahashi and David Nichols, Synapse Energy Economics, Inc. 

ABSTRACT 

As interest in increasing the scale of electric energy efficiency programs grows, one 

question is whether such programs can achieve and sustain high levels of impact. Another 

question is how the unit cost of saved energy (CSE) for overall portfolios of programs may 

change as program scale increases. To see what light experience casts on these questions, data 

covering program performance for numerous jurisdictions and years were collected. 

We define high savings as incremental reductions in annual energy sales of one percent 

or more due to one year’s program activities. Data show that some utilities have achieved high 

levels of savings over several years, implying that high levels of savings can be sustained. 

A more complex question concerns CSE. One view is that CSE should increase as more 

of the energy savings potential is tapped. Steady-state analysis can readily arrange technologies 

on a “conservation supply curve” of increasing costs per unit of saved energy. Alternatively, 

CSE may not increase in this way given innovation, economies of scale, and learning curves. To 

analyze how CSE changes in practice as the scale of programs increases, we analyzed CSE for 

several programs in different regions that have pursued energy efficiency on a comprehensive 

basis. 

This analysis of actual program CSE finds that program CSE seems to decrease as 

program scale and impact grows. Of course, program CSE fluctuates due to many factors such as 

year, utility, and program type and size. Analysis of factors contributing to decreases or increases 

in CSE is underway. 

Introduction and Background 

Electric energy efficiency has been seen as one of the most promising and cost-effective 

strategies for addressing numerous problems associated with conventional power generation 

including climate change disruption. Many states and utilities are intensifying their consideration 

of the potential of energy efficiency measures and are interested in the cost of achieving higher 

level of energy savings and greenhouse gas emission reductions. Various stakeholders are 

investigating the possibility of increasing the scale and impact of buildings and industry sector 

energy efficiency programs operated by utilities or other administrators. At the same time, there 

are concerns that unprecedented efforts and resources would be required to achieve significantly 

increased energy savings. 

This analysis of empirical data on efficiency programs is intended to provide some useful 

information and more confidence to program administrators and policy makers with regard to 

how much savings can be realistically achieved and at what cost. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-363
Empirical Evidence on High Energy Savings Performance 

It is useful to inquire whether a number of utilities or other administrators have achieved 

high energy savings over a number of years. The National Action Plan for Energy Efficiency 

(NAPEE) states that well-designed energy conservation programs are “delivering annual energy 

savings on the order of 1 percent of electricity and natural gas sales.” (DOE and EPA 2006, ES4) But such activities have not been documented comprehensively, in NAPEE or elsewhere. 

Consequently, we have investigated this issue in order to identify the utilities (or other program 

administrators) that have achieved high savings, addressing only electricity savings. For 

purposes of this paper, we define high savings as incremental reductions in annual energy sales 

of one percent or more due to one year’s program activities. Table 1 presents a list of entities that 

achieved high energy savings and the sources for the energy impacts shown. Where high savings 

were achieved in several years, only the year of highest savings is shown. 

Table 1. Examples of High Annual Electric Energy Savings Realized through DSM 

Jurisdiction or Entity 

Annual 

Savings 

(Percent) 

Year(s) Source 

Interstate Power & Light (MN) 3.0 2001 Garvey, E. 2007. “Minnesota’s Demand Efficiency 

Program.” 

San Diego Gas & Electric 

(SDG&E) (CA) 2.1 2005 SDG&E 2006. Energy Efficiency Programs Annual 

Summary 

Minnesota Power 1.9 2005 Garvey, E. 2007 

Sacramento Municipal Utility 

District (SMUD) (CA) 1.9 1994 Data provided by SMUD 

Vermont 1.8 2007 Efficiency Vermont 2008. 2007 Preliminary Results 

and Savings Estimate Report 

Southern California Edison 

(SCE) 1.7 2005 SCE 2006. Energy Efficiency Annual Report 

Western Mass. Electric Co. 

(MA) 1.6 1991 MA Dept. of Telecommunications & Energy (DTE) 

2003. Electric Utility Energy Efficiency Database 

Pacific Gas & Electric (PG&E) 

(CA) 1.5 2005 PG&E 2006. Energy Efficiency Programs Annual Summary 

Massachusetts Electric Co. 1.3 2005 MECo 2006. 2005 Energy Efficiency Annual Report 

Revisions 

Connecticut IOUs 1.3 2006 CT Energy Conservation Management Board 

(ECMB). 2007 

Commonwealth Electric (MA) 1.2 1990 MA DTE 2003. 

Cambridge Electric (MA) 1.1 2000 MA DTE 2003. 

Seattle City Light (WA) 1.0 2001 Seattle City Light 2006. Energy Conservation 

Accomplishments: 1977-2005

Eastern Edison (MA) 1.0 1994, 1998 MA DTE 2003. 

We also investigated whether such high energy savings were sustained over multiple 

years. We identified seven cases where utilities have sustained 1% or more savings over multiple 

years. Those cases are presented in Table 2 and Table 3. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-364
Table 2. Examples of High DSM Performance (Part 1) 

Mass. 

Electric SMUD 

W. Mass. 

Electric 

1991 1.0% 0.7% 1.6% 

1992 0.7% 1.3% 1.0% 

1993 0.7% 1.1% 1.3% 

1994 1.0% 1.9% 0.8% 

1995 1.0% 1.6% 0.7% 

1996 0.9% 0.9% 0.8% 

1997 1.0% 0.4% 1.0% 

1998 0.8% 0.4% 0.8% 

1999 0.9% 0.3% 0.7% 

2000 0.7% 0.3% 1.0% 

Data source: SMUD 2007; MA DTE. 2003. 

Table 3. Examples of High DSM Performance (Part 2) 

 CT IOUs 

Efficiency 

Vermont 

Interstate 

Power & 

Light SDG&E 

2000 0.9% 0.4% 0.8% 

2001 1.1% 0.7% 3.0% 1.2% 

2002 0.9% 0.8% 2.5% 1.2% 

2003 0.4% 1.0% 2.7% 0.7% 

2004 1.0% 0.9% 2.6% 1.3% 

2005 1.1% 1.0% 2.6% 2.1% 

2006 1.3% 1.0% 0.8% 

2007 1.8% 

Data source: CT ECMB 2001-2007; Efficiency Vermont 2007b; Garvey, E. 2007; 

California Energy Commission (CEC) 2008 

The data summarized above show that utilities or program administrators in varying 

regions of the country and during varying periods of time have indeed achieved annual 

incremental levels of energy efficiency savings equal to or in excess of one percent of annual 

electricity sales. Some have occasionally saved over two percent of annual energy sales per year. 

The data themselves provide a sort of “existence proof” that incremental annual energy savings 

of at least one percent of annual sales can be achieved and sustained. But at what cost? And does 

the cost increase as the penetration reaches or exceeds one percent of annual sales? These 

questions are especially relevant in light of the growing interest in several jurisdictions in 

achieving, sustaining, or even surpassing these high levels of savings. 

Analysis of Trends in the Cost of Saved Energy 

The Conservation Supply Curve 

One view expects that the CSE should increase as more of the energy savings potential is 

tapped. Steady-state analysis can readily arrange efficiency technologies on a “conservation 

supply curve” (CSC) of increasing costs per unit of saved energy so that it would appear as if 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-365
increasing acquired savings would require an increase in the cost per unit savings. (See Figure 

1.) 

Figure 1. Illustrative Energy-Efficiency Supply Curve 

Source: XENERGY Inc. 2002. California’s Secret Energy Surplus: the Potential for Energy Efficiency 

Many studies develop CSE," also known as “energy-efficiency supply curves”, for use in 

energy planning studies and policy analysis. (Bernow 2002; Coito & Rufo 2002; Donovan et al. 

2003). These curves are always presented with "steps" that increase as one moves along the 

horizontal axis from left to right (increasing energy savings). This notion of an "increasing cost 

supply curve for saved energy" is theoretically appealing. It reflects a logical order of 

prioritization of opportunities. Why would someone implement a high cost measure but not a 

lower cost measure? 

There is no question that CSC analysis is a useful tool for comparing the relative costs of 

energy efficiency measures and for understanding the aggregate potential for cost-effective 

energy efficiency that is available up to any given CSE level. However, CSCs are generally 

constructed in a manner that is limited to demonstrated and currently well-understood measures 

and programs. They may imply increased market share for advanced technologies, but only 

rarely do they reflect true technological or institutional improvement over time. In contrast, 

analysts of fossil fuel supply do not limit their analyses to "proven" resources, but routinely 

include hydrocarbon reserves that are described as "undiscovered," "possible," or "prospective." 

(Bruce Biewald 2004). This appears to be a bias against demand-side resources in long-term 

energy modeling. 

Further, CSC analysis can lead to an assumption that energy efficiency programs must 

mimic the CSC curve, such that the greater their amount of savings, the greater their program 

cost. However, program CSE might be expected to differ from the technology CSE that underlies 

CSC analysis, for several reasons. Beyond the obvious fact that energy efficiency program costs1

typically cover a substantial fraction, but not all, of efficiency’s incremental costs, the program 

CSE fluctuates due to many factors such as year, utility, sector, type of program, and size of 

program. 

 

1

 The costs of the utility or other program administrator including the costs for marketing, administration, program 

rebates, and measurement and verification of energy savings. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-366
In fact, the data for actual energy efficiency programs do tell a different story from that 

which might be inferred from CSCs. Utility and non-utility DSM programs generally include a 

range of measures, ranging from zero (or even negative net cost) per kWh saved up to (and in 

rare occasions) exceeding avoided costs. The overall cost per kWh saved for a utility program in 

a particular year turns out to be lower for the more ambitious programs. This could be because 

there are fixed costs that can be spread out over more measures or participants. On the electricity 

supply side it is generally accepted that there are "economies of scale" in power plant 

construction costs (i.e., larger equipment costs less on a per MW basis). 

Utility efficiency programs are often composed of various programs that target each 

sector, including the low-income, residential, and commercial and industrial sectors. Therefore, 

the overall cost of saved energy for the program is always the weighted average cost of saved 

energy through a portfolio of various measures. This reality is different from a wholesale power 

market which sets market clearing prices of the market according to the costs of most expensive 

power plants to meet incremental energy consumption. 

As noted above, CSC analysis often does not address economies of scale. Theoretically, a 

company can enjoy economies of scale by expanding its operation of energy efficiency 

programs. For example, a large program allows for bulk purchase of certain efficiency measures 

at a lower price or allows for bulk discounts for contracts with energy service companies to 

deliver energy savings. In another instance, large-scale programs can allocate the cost of 

marketing and administration of those programs over greater amount of energy savings, which 

would tend to reduce program cost per kWh saved as program scale increases. 

Also, marketing and customer education will increase customers’ adoption of new 

technologies, which in turn will accelerate the mass production of such technologies and thus 

reduce price per unit in the long term. Furthermore, greater scope of programs could reduce 

marketing expense or provide synergistic savings.2

Historical Trends in Program CSE 

We analyzed several comprehensive energy efficiency programs in varying regions in 

order to explore the empirical relationship between program CSE and program scale. In this 

investigation, we collected and analyzed numerous amounts of data (over 160 cases) with regard 

to expenditures, costs, and savings associated with energy efficiency programs delivered by 

numerous utilities and over many years. This analysis focused on utilities or other entities with 

comprehensive electric energy efficiency programs, and was not restricted to the 13 programs 

that have achieved or surpassed savings equal to one percent of sales. We obtained the data 

either from utility efficiency annual reports, directly from program administrators or staff at state 

energy offices or regulatory commissions. Note that data on achieved energy savings originate in 

reports by program administrators and/or regulators. Data on savings are inherently less certain 

than data on costs. Regular impact evaluation activities to verify savings estimates have been 

conducted by virtually all entities that have pursued comprehensive energy efficiency programs 

on a sustained basis;3

 nevertheless, uncertainty regarding exact savings necessarily remains. 

 

2

 For example, combining a lighting retrofit with a large commercial AC retrofit can reduce the size of the AC unit 

needed, making the retrofit both cheaper and more cost-effective. 

3

 For detailed information how M&V has been conducted in Northeast states see Northeast Energy Efficiency 

Partnerships, Inc. 2006. The Need for and Approaches to Developing Common Protocols to Measure, Verify and 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-367
Moreover, the quality of savings estimation and verification could vary from jurisdiction to 

jurisdiction; this is not an issue we explored for this analysis. We think it unlikely that variability 

in the quality of savings estimates accounts for the trends found in the analysis described below, 

particularly since our analytical focus is on the relation of costs to the level of savings achieved 

within different entities’ programs (as opposed to across them). 

We analyzed these data to compare lifetime CSE per MWh (defined as the levelized cost 

of energy savings) for a given delivery entity and given year of program delivery with annual 

energy savings as percentage of sales for that entity and year. We also compared lifetime CSE 

with the quantity of projected lifetime energy savings. 

Some utilities reported both first year and lifetime savings. Other utilities reported only 

first year savings. Where lifetime savings were not available, we assumed extrapolated lifetime 

savings based on the average lifetime of efficiency measures for a specific program or sector 

within a specific utility from other years for which lifetime savings for that utility were available. 

Where no lifetime savings data were available for a specific utility or program, we used a 12-

year average lifetime that has been recognized as an industry rule of thumb estimate (DOE & 

EPA 2006; Martin, York, & Witte 2005; Bender et al. 2005).4

 For cases in which available 

information indicated that savings were measured at the customer level, we adjusted savings 

from customer to generation level to account for transmission and distribution line loss. We then 

estimated levelized CSE with a 4 percent discount rate used by CEC (2005) and the following 

formula: 

Levelized CSE = Program Costs x Capital Recovery Factor / First year kWh saved 

Capital Recovery Factor = i (1 + i)n

 /{(1 + i) n – 1} 

i = real discount rate 

n = weighted average of useful measure life (years) 

We considered the use of program cost per lifetime MWh energy savings versus the use 

of program cost per first year savings. First-year savings is a somewhat more certain number 

because first year savings are often measured and verified savings while lifetime savings are 

projected. Although lifetime savings projections are usually based on empirical data concerning 

measure life, there are some uncertainties concerning actual measure lifetimes as well as energy 

savings performance over time. However, we chose a levelized cost of energy efficiency as a 

normalized value over the potential, useful energy savings, and one that is comparable to the cost 

of power generation. Additionally, the cost of first year energy savings ignores the fact that 

measure lives vary considerably among different types of technologies. 

Program CSE versus annual incremental savings as percentage of annual retail sales is a 

measure of the relative aggressiveness of each utility program and addresses economies of scale 

 Report Energy Efficiency Savings in the Northeast, January 2006. Also for California’s historical practice on M&V, 

see page 26 of Edward Vine and Jayant Sathaye 1999. Guidelines for the Monitoring, Evaluation, Reporting, 

Verification, and Certification of Energy-Efficiency Projects for Climate Change Mitigation, March 1999L 

Lawrence Berkeley National Laboratory. 

4

 For example, we used the 12 year average lifetime number to estimate lifetime savings for SMUD, Seattle City 

Light, and Iowa IOUs. For California IOUs’ data, slightly less than half of the data have projected lifetime savings 

and for the other half of the data, we used the average life of a certain program (e.g., residential, non-residential, 

new construction, others) to estimate the values for the same program in different years.

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-368
to some extent. Substantial absolute energy savings do not necessarily mean a utility is 

aggressively pursuing energy efficiency if such savings are small relative to the size of annual 

sales. On the other hand, small absolute savings could mean a utility is aggressively saving 

energy and working efficiently. This aggressiveness and efficiency of program administration 

could lower the cost of per-unit energy savings. For example, if a utility allocates relatively fixed 

marketing, planning, and administration costs over more sizable savings, the cost per unit of 

energy savings could decrease. Additionally, the materials and services that utilities obtain from 

vendors and contractors may have a lower relative cost as the scale of their programs grows. 

Figure 2 presents a comparison between utility levelized CSE in real 2006$ per MWh saved and 

projected lifetime savings. We generated linear trend lines to all datasets and identified slopes 

(coefficient) and R-square values for those lines. Figure 2 includes the trend line for SCE as an 

example. Table 4 presents the coefficient and R-square values for all data sets in Figure 2. 

Program CSE versus lifetime savings measures the absolute size of each utility program 

and also addresses economies of scale to some extent. Bulk discounts for efficient products or for 

contracts with energy service companies might be more pronounced in this measurement than 

the savings as % of sales. We also drew linear trend lines for all datasets in this analysis, the 

results for which are presented in Table 5.5

 

Figure 2. Utility Cost of Saved Energy (2006$/MWh) vs. Incremental Annual Savings as % 

of Sales 

SCE: y = -854.79x + 29.352

R2

 = 0.5525 0

20

40

60

80

100

120

140

160

180

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

Annual Incremental Savings as % of Annual Sales

CSE 2006$/MWh Saved

CT IOUs 2000-2005

MA IOUs 2003-2006

Efficiency Vermont 2000-

2007

SMUD 1991-2006

Seattle 1984-2005

PG&E 2000-2006

SDG&E 2000-2006

SCE 2000-2006

Mass. Electric 1989-2002

W. Mass. Electric 1990-

2002

Boston Ed/Nstar 1989-2002

Cambr. Elec. 1990-2000

Com. Elec. 1989-2000

Eastern Ed. 1989-1999

Fitchb. G&E 1990-2002

IA IOUs 2001-2006

Linear (SCE 2000-2006)

 

5

 Due to the page limit of this paper, the figure for this analysis corresponding to Table 5 is not presented here. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-369
Table 4. Slope Coefficient and R-Squared Values for Linear Trend Lines for Figure 2. 

Data Coefficient R-square 

CT IOUs 2000-2005 -1073 0.462 

MA IOUs 2003-2006 -1798 0.834 

Efficiency Vermont 2000-2007 -659 0.591 

SMUD 1991 - 2006 -1257 0.136 

Seattle 1984 - 2006 -13935 0.715 

PG&E 2000-2006 -1936 0.526 

SDG&E 2000-2006 -561 0.400 

SCE 2000-2006 -855 0.553 

Mass. Electric 1989-2002 -1185 0.050 

W. Mass. Electric 1990-2002 -220 0.006 

Boston Ed/Nstar 1989-2002 -9855 0.403 

Cambr. Elec. 1990-2000 -48857 0.271 

Com. Elec. 1989-2000 -8189 0.213 

Eastern Ed. 1989-1999 -858 0.020 

Fitchb. G&E 1990-2002 -1903 0.125 

Iowa IOUs 2001-2006 -1712 0.943 

Table 5. Slope Coefficient and R-Squared Values for Linear Trend Lines for CSE vs. 

Projected Lifetime Savings 

Data Coefficient R-square 

CT IOUs 2000-2005 -2.695E-06 0.457 

MA IOUs 2003-2006 -4.950E-06 0.676 

Efficiency Vermont 2000-2007 -1.135E-05 0.658 

SMUD 1991 - 2006 -1.590E-05 0.207 

Seattle 1984 - 2006 -1.271E-04 0.731 

PG&E 2000-2006 -1.841E-06 0.552 

SDG&E 2000-2006 -2.249E-06 0.420 

SCE 2000-2006 -6.484E-07 0.591 

Mass. Electric 1989-2002 -9.022E-06 0.168 

W. Mass. Electric 1990-2002 -8.284E-06 0.026 

Boston Ed/Nstar 1989-2002 -4.542E-05 0.454 

Cambr. Elec. 1990-2000 -1.747E-03 0.183 

Com. Elec. 1989-2000 -1.390E-04 0.186 

Eastern Ed. 1989-1999 -2.854E-05 0.034 

Fitchb. G&E 1990-2002 -1.760E-04 0.078 

Iowa IOUs 2001-2006 -3.927E-06 0.948 

A key result is that, among all of the datasets that we have collected, all of the slope 

coefficients of the linear trend lines are negative. This strongly suggests that per-unit cost of 

energy efficiency (EE) decreases as the amount of EE savings increases. It is important to 

emphasize that this finding contradicts the generally accepted theory that costs of EE increase 

when EE savings amounts increase.6

 The fact that the coefficient is negative in every case is 

 

6

 For example, the data for SCE, PG&E, MA IOUs, and Seattle City Light show that a more than 50% of the 

variation in CSE can be explained by the amount of energy savings; for others, like Mass. Electric, Cambridge 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-370
particularly striking. While there exists a possibility that unit costs might begin to increase at 

much higher levels of EE program savings, this evidence suggests that current program savings 

levels have not yet approached any such point. 

It is also important to note that variation of per unit cost of energy efficiency for a given 

utility generally decreases as the amount of efficiency savings increases. In other words, the data 

show that the cost of efficiency becomes smaller and less variable for higher savings amounts. 

For example, there are several cases where costs of efficiency reach over $100 per MWh saved 

below 0.5% savings penetration. 

Possible reasons for this finding include (1) economies of scale are at work (e.g., 

allocating marketing and administration costs over more EE savings, achieving lower unit costs 

for program inputs); (2) economies of scope are at work (e.g., exploiting synergies among 

different measures); (3) administrators become smarter and more organized in designing and 

developing EE programs; or (4) administrators have more credibility or more resources available 

for quality program design and development, etc. 

We recognize that the data and analysis presented here are preliminary. They by no 

means pin point the underlying sources of variation in program CSE across utilities and across 

years. However, the macro-level trends identified do at least call into question what is a common 

explicit or implicit assumption concerning the achievability and program costs of large-scale EE. 

 

Conclusion 

There is growing interest in increasing the scale and impacts of electric energy efficiency 

programs in the United States. The experience of the utilities or other administrators that have 

achieved the greatest levels of savings in the past suggests that high levels of program impact can 

be achieved and sustained. At least 13 programs have reported overall incremental annual 

electricity savings equal to or exceeding one percent of annual sales in one or more years. 

Recently some have even achieved savings levels of two or more percent annually. 

We explored the relationship of program cost for saved energy to the level of electricity 

savings achieved by numerous utilities over many years. This analysis focused on utilities or 

other entities with comprehensive electric energy efficiency programs, and was not restricted to 

the 13 programs that have achieved or surpassed savings equal to one percent of sales. 

One aspect of this analysis compared each utility’s program CSE for a given year to the 

total quantity of lifetime electricity savings projected from that year’s energy efficiency program. 

For every program analyzed, we found a trend of decreasing utility CSE as the absolute quantity 

of EE savings increased. Another aspect of this analysis compared each utility’s program CSE to 

annual incremental savings as percent of sales. For every program analyzed, we found a trend of 

decreasing utility CSE as the relative quantity of EE savings increased. 

Our overall finding thus is that the per-unit program cost of achieving energy efficiency 

seems to decline as program scale and aggressiveness increase, at least based on the experience 

to date of many utilities with comprehensive programs. This may surprise those who would 

expect the opposite trend based on theoretical conservation supply analysis. 

This is a preliminary analysis that we expect to extend in several directions. Possibilities 

for further research include: 

 Electric, and Eastern Edison, show the amount of energy savings explains a smaller fraction of the variations in 

CSE. 

2008 ACEEE Summer Study on Energy Efficiency in Buildings 8-371
• Adding data for additional utilities and regions to the analysis. 

• Investigation of CSE by type of programs or sector (e.g. residential versus nonresidential). 

• Review of the data sets to ensure that load management type programs have been fully 

excluded. (As this was done where evident, this would be in the nature of doublechecking.) 

• Explicit analysis of the share of administrative and marketing costs to total program costs 

as a function of program impact, to test one of the hypotheses about economies of scale. 

• Investigation of the impact of bulk discounts for products and service contracts on 

program costs. 

• Investigation of the impact of synergistic program effects on program costs. 

• Review of the savings evaluation and verification history associated with each data set. 

• Comparison of total CSE (i.e., utility plus participant costs) with electricity savings 

achieved. 

• Analysis of CSE/savings trends among gas energy efficiency programs. 

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