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Recent estimates of energy efficiency potential in the U.S.

Priya Sreedharan, PhD, PE 

Energy and Environmental Economics (E3), 101 Montgomery Street, 

16th Floor, San Francisco, CA 94104, USA. 

Tel.: + 1 415 391 5100; fax: + 1 415 391 6500. Email: priya@ethree.com 

Abstract

Understanding the potential for reducing energy demand through increased end-use energy 

efficiency can inform energy and climate policy decisions. However, if potential estimates are 

vastly different, they engender controversial debates, clouding the usefulness of energy 

efficiency in shaping a clean energy future. A substantive question thus arises: is there a general 

consensus on the potential estimates? To answer this question, this paper reviews recent studies 

of U.S. national and regional energy efficiency potential in buildings and industry. Although 

these studies are based on differing assumptions, methods, and data, they suggest technically 

possible reductions of ~ 25%–40% in electricity demand and ~ 30% in natural gas demand in 

2020, and economic reductions of ~ 10%–25% in electricity demand and ~20% in natural gas 

demand in 2020. These estimates imply that electricity growth from 2009–2020 ranges from 

turning U.S. electricity demand growth negative, to reducing it to a growth rate of ~ 0.3% per 

year (compared to ~ 1% baseline growth). 

Keywords: energy efficiency; greenhouse gas mitigation; potential study 
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1 Introduction  

Energy efficiency and sustainable energy technologies are at the center of discussions on 

climate change. In the U.S., the commercial, residential and industrial sectors account for 

approximately 50% of U.S. greenhouse gas (GHG) emissions (U.S. Environmental Protection 

Agency, 2008). Improving the energy efficiency of these sectors is a key component of regional 

and state climate policies and proposed federal climate legislation. At the US federal level, the 

American Clean Energy and Security Act of 2009 (“Waxman Markey” bill) implements energy 

efficiency through a combination of building energy codes, appliance standards, a renewable 

energy/energy efficiency resource standard, and funding of efficiency programs (Waxman and 

Markey, 2009). At the state level, the California Global Warming Solutions Act (“AB 32”) 

Scoping Plan requires an additional 10% of energy efficiency savings in 2020 beyond the 

efficiency savings from existing policies, roughly 1% savings per year. Twenty-four states have 

also enacted energy efficiency resource standards that require utilities (or other program 

administrators) to implement programs that deliver ~ 0.5–1% savings per year, up to 2% in outer 

years (ACEEE 2010). 

Besides employment and health benefits, the federal and state legislative actions are driven 

by recent reports of significant and cost-effective opportunities for increased energy efficiency 

across the U.S. A case in point is that ~ 50% of the potential GHG emissions reductions 

measures, specifically the energy efficiency ones, can be had at a negative cost, with energy 

saving benefits exceeding the energy-efficiency investment costs (McKinsey 2007 and 2009a). 

This finding has drawn national attention towards energy efficiency. 

However, if potential estimates are vastly different, they engender controversial debates, 

clouding the usefulness of energy efficiency in shaping a clean energy future. Is there general 
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consensus on the potential estimates? To answer this question, this paper reviews four U.S. 

national and regional energy efficiency potential in buildings and industry that include McKinsey 

(2007) (“McK07”), McKinsey (2009a) (“McK09”); EPRI (2009) (“EPRI”); and Annual Energy 

Outlook of Energy Information Administration (2008) (“AEO”). 

These studies were chosen because they constitute a relatively recent set of public studies of 

national level energy efficiency potential in the U.S. that utilize a common baseline energy 

forecast (the Annual Energy Outlook 2008 base case) and estimate savings over a common time 

frame (2010-2030). By utilizing a common energy forecast, the treatment of existing codes and 

standards, demand side management programs, and ‘natural’ adoption of energy efficiency is 

consistent. The analysis conducted in this article could be repeated with more recent studies. 

However, the overall goal of this study, to understand if there is consensus on the existence of 

energy efficiency potential, remains valid with the set of studies evaluated in this analysis. 

Although these studies use differing assumptions, methods, and data, they suggest 

technically possible reductions of ~ 25%–40% in electricity demand and ~ 30% in natural gas 

demand in 2020, and economic reductions of ~ 10%–25% in electricity demand and ~20% in 

natural gas demand in 2020. These estimates imply that electricity growth from 2009-2020 

ranges from turning U.S. electricity demand growth negative, to reducing it to a growth rate of 

~ 0.3% per year (compared to ~ 1% baseline growth). The efficiency potentials constitute 

~ 10%-100%+ of the greenhouse gas reduction goals for 2020 as required by the Waxman 

Markey climate bill. 

This paper is related to the literature of the “energy efficiency gap”: the observed energy 

efficiency investment is far below economic energy efficiency potential due to multiple market 

barriers (Hirst and Brown 1990). Factors contributing to the gap include market barriers that 
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qualify as market failures (e.g., under-pricing of energy below social marginal cost, imperfect 

information, lack of financing, and tax policies that discourage capital investment) and additional 

market barriers (e.g., high consumer discount rates, renters lacking investment incentives, and 

consumer inertia caused by status quo bias) (Brown 2001; Brown and Chandler 2008; Jaffe and 

Stavins 1994; Jaffe et al. 2004; Sanstad and Howarth 1994; Lutzenhiser 1994; Hassett and 

Metcalf 1993; Metcalf 1994; Sanstad et al. 1995; Fisher and Rothkopf 1989; Eyre 1998). In 

particular, our review confirms that the energy efficiency gap is projected to be alive and well, 

with the potential estimates remaining up to several times what is likely to be realized in the 

future. The US is taking policy actions to remedy this gap. 

2 Description of national studies  

2.1 Review of energy efficiency definitions

To provide a methodological context of the studies chosen for our review, this section 

describes what an energy efficiency analysis is (National Action Plan for Energy Efficiency 

2007; Rufo and Coito 2002; Frisch 2008; Gellings et al. 2006). The analysis typically estimates 

the benefits and costs of energy efficiency “measures”, usually to support utility planning or 

state/federal energy policy. A measure is any action, including equipment upgrades, control 

strategies or behavioral changes, that increases energy efficiency in a given end-use application. 

The scope of the study can range from individual measures for a specific end use in a utility’s 

service territory to a comprehensive group of measures on a national level. Summary results 

typically describe the energy savings for a given timeframe, relative to a baseline, and may 

include the costs to achieve these reductions (e.g., 10% reduction in electricity use within 10 

years at a cost of $50 Billion). 
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There are four major types of “potential”. The first type is the technical potential. It 

represents the theoretical maximum amount of energy use displaceable by energy efficiency, 

disregarding non-engineering constraints, such as costs of energy efficiency investment or 

consumer acceptance. The technical potential is computed from the baseline energy consumption 

forecast (which may be preexisting or be determined as part of the efficiency analysis) by 

disaggregating the baseline energy consumption by sector and end-use and applying efficiency 

improvements of different measures that are appropriate to the baseline stock characteristics. The 

timing of the measures varies by type; appliances are usually replaced at the end of their natural 

lifetime, while measures such as lighting or commissioning can be adopted sooner. 

Technical potentials (and potential studies generally) tend to ignore price response, such as 

energy reductions due to conservation-encouraging electricity rate structures, or increased energy 

consumption because technology-based efficiency investments result in lower costs of service — 

the so called ‘rebound’ effect. This phenomenon has been empirically examined in many studies 

and found to be bounded, generally, at ~30% and lower in many instances (Sorrell et al. 2009; 

Greening et al. 2000). Price response can be incorporated, for example, by using energyeconomic modeling that combines both technology and price as drivers of efficiency; this 

approach was used in a well-known national study (Interlaboratory Working Group 2000). 

The second potential type is economic potential. It is a subset of the technical potential that is 

cost effective, as compared to supply side energy resources or energy price. Its estimation 

entails defining the appropriate cost test (e.g., total resource vs. participant) and selecting 

cost-effective measures (represented by a benefit-to-cost ratio that exceeds one) from the 

technical potential. Assumptions on discount rate and measure life strongly influence the 

economic potential. 
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The third potential type is the maximum achievable potential (sometimes referred to as 

‘achievable potential’). It is a subset of economic potential that can be expected with an 

aggressive program (e.g., end users are compensated for the entire incremental cost of 

efficiency). Its computation entails applying market penetration rates to the economic potential. 

The fourth potential type is the realistic achievable potential (sometimes referred to as 

‘program potential’). It is a subset of the maximum achievable potential and reflects modest 

program funding levels. Its computation entails applying program implementation factors to the 

maximum achievable potential. 

2.2 Methods and scope comparison  

EPRI reports electricity savings for the residential, commercial and industrial sectors. Yearly 

savings up to 2030 for realistic achievable potential (RAP), maximum achievable potential 

(MAP), economic potential (EP) and technical potential (TP) are reported. They closely follow 

potential study formalisms and apply a participant cost test to estimate the EP. 

The McK09 study follows potential study conventions closely. The McK09 key scenario is 

an economic type of potential, which they describe as the net-present-value (NPV) positive 

energy efficiency potential. I refer to the NPV positive energy efficiency potential as an 

economic potential (EP). With permission from McKinsey, the unpublished technical potential 

(TP) energy savings for the McK09 study are reported. McK09 estimates energy efficiency 

savings for 2020. 

The McK07 report estimates potential GHG emissions reductions for several measures, 

including energy efficiency. I report the energy efficiency savings portion for the highrange and 

midrange scenarios estimated for 2030. Electricity and natural gas energy savings are estimated 

using measure level potential GHG emissions reductions provided by McKinsey. 
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Two AEO 2008 side scenarios related to end-use energy efficiency are included for 

comparison — the best available technology scenario (BT), which is interpreted roughly as a 

technical potential, and a high technology scenario (HT), which is interpreted roughly as an 

achievable potential. These do not employ energy efficiency potential study methods, but use the 

National Energy Modeling System (NEMS) model. The BT addresses buildings only, while the 

HT addresses industry and buildings. I generated a hybrid BT/HT scenario by including the 

industrial efficiency savings from the HT scenario. 

Table 1 lists the methods, data and assumptions for the studies. It is worth emphasizing that 

(i) the baseline energy consumption data are consistent across the EPRI, AEO-08 and McK09 

studies, (ii) the modeling approaches employed by EPRI and McKinsey are similar, and (iii) the 

broad categories of energy efficiency measures are similar, although the McK09 report 

considered a wider scope of measures (McKinsey 2009b). On the other hand, the applied 

cost-effectiveness tests and assumed discount rates vary among the three studies. McKinsey 

employed their own test, which considered a measure to be economic if the net present value of 

the energy savings, maintenance, and operational cost savings exceeded the present value of the 

incremental cost of the efficiency measure. The energy savings were estimated using industrial 

retail rates. EPRI used a 5% discount rate and a variation of the participant cost test, in which the 

present value of consumer energy savings (based on AEO projected retail rates by consumer 

class) were compared to the consumer cost for purchasing the incremental value of the energy 

efficiency measure. 

The selection of the cost test and discount rate depends on the purpose of the analysis and 

has been a subject of debate for decades (Gillingham et al. 2009; Hassett and Metcalf 1993; 

Metcalf 1994; Sanstad et al. 1995). The discount rate is important because it determines the 
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value of energy efficiency savings over the lifetime, relative to capital investments. Utility 

energy efficiency programs typically evaluate cost effectiveness using the ‘total resource cost 

test’, which represents economic impacts to society overall; the discount rate used is the utility 

weighted average cost of capital, typically ~ 5-6% (real) and ~ 7-8% (nominal); these are 

comparable to the discount rates employed in the studies evaluated. The discount rate from the 

participant perspective varies considerably and depends on factors such as required rate of return 

and risk tolerance. For businesses, an appropriate discount rate is the firm’s weighted average 

cost of capital, typically ~10-12%. However, commercial and industrial customers often require 

very short payback periods, implying discount rates much greater than 20%. The National Action 

Plan on Energy Efficiency cost effectiveness resource guide provides an excellent overview of 

utility program cost effectiveness (National Action Plan for Energy Efficiency 2008). 

The choice of the discount rate greatly affects the economics of energy efficiency. The 

complexity in selecting an appropriate discount rate and cost test underscore common 

controversies in conducting energy efficiency potential analysis. The McKinsey studies elicited 

many criticisms, for example, “If so many negative-cost opportunities are available, why are they 

not already being taken up by consumers?” In addition to cost-effectiveness, several market 

failures (e.g., lack of information) and behavioral reasons (Gillingham et al. 2009) contribute 

towards the adoption of energy efficiency. Even if consumers are aware and the economics are 

attractive, households and businesses may simply choose not to invest in cost-effective energy 

efficiency because a non-energy investment may have more value from their perspective. The 

decision to adopt energy efficiency ultimately resides with the consumer and the economics from 

the consumer perspective may not warrant an investment in energy efficiency even if the 

investment is cost-effective to societal overall as determined by the total resource cost test. 
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Table 1 Methods, data and key assumptions from national studies 

Study Modeling approach Baseline 

data 

Discount 

rate 

Cost 

benefit test 

Sample measures 

EPRI TP 

Buildings: bottom-up 

Industry: top-down 

Equipment replacements 

occur at end of life 

AEO 

2008 -- -- 

Efficient HVAC 

equipment, appliances, 

motors, lighting, cool roofs, 

building envelopes, 

controls (water reset 

temperatures, 

programmable thermostats, 

energy management 

systems), air-side 

economizers, retrocommissioning 

EPRI EP Subset of TP that passes 

cost/benefit test 

AEO 

2008 5% 

Variation 

of 

participant 

cost test a

EPRI MAP 

“Market acceptance rates” 

applied to TP for each 

measure 

AEO 

2008 5% 

EPRI RAP 

“Program implementation 

factors” applied to MAP for 

each measure 

AEO 

2008 5% 

McK07 

Bottom up stock and flow 

modeling; 

Equip replacements occur 

at end of life 

AEO 

2007 b 7% 

Net-present 

value 

positive 

test c

Efficient electronics, 

lighting, building shells 

HVAC, commercial CHP, 

water heaters, control 

systems 

McK09 

Bottom up stock and flow 

modeling; early equip 

retirement if economical 

AEO 

2008 7% 

Net-present 

value 

positive 

test c

Measures in 2007 report 

considered, plus additional 

ones 

AEO-08 BT 

NEMS model used 

Addresses buildings only 

Cost ignored 

AEO 

2008 -- -- Efficient equipment and building shells 

AEO-08 HT 

NEMS model used. 

Buildings & industry 

modeled. Cheaper & more 

efficient equipment 

available earlier 

AEO 

2008 -- -- 

Efficient equipment and 

building shells (including 

use of Energy Star) 

 

a

 Incremental cost to a consumer for the efficient technology is compared to the expected bill savings over the useful 

life (EPRI, 2009). The participant was assumed to pay the full amount of the incremental cost (Wickler 2009). 

b

 McK07 potentials were roughly adjusted to AEO 2008 baseline data by removing efficiency measures included in 

the Energy Independence and Security Act of 2007. 

c

 This includes the portion of energy savings for which the direct energy, operating ,and maintenance cost savings 

over the equipments’ useful life, brought to present value, minus the equipment and installation costs, is positive. 

Energy savings were calculated using industrial retail rates and converted present value terms. 
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3 National energy efficiency potential results  

The tables and figures reported here draw upon a combination of information from the 

published reports and raw data provided by the reports’ authors. EPRI reports their estimated 

potentials relative to both the AEO 2008 baseline and an adjusted EPRI baseline. In this paper, 

all reported EPRI savings are relative to the AEO 2008 baseline. McK07 and McK09 also report 

energy savings from combined heat and power (CHP). In McK09 they are excluded from the 

total and listed separately. In representing the McK07 savings, industrial CHP savings are not 

included for consistency with the other studies. 

3.1 Aggregate national level energy savings  

Table 2 lists the national-level potential energy efficiency potentials in 2020 and 2030 with 

the electricity savings illustrated in Fig. 1. These studies estimate technically possible reductions 

of ~ 25%–40% in electricity demand and ~ 30% in natural gas demand in 2020, and economic 

reductions of ~ 10%–25% in electricity demand and ~20% in natural gas demand in 2020. 

The estimated electricity savings vary significantly, ranging from a 3% realistic potential in 

2020, as estimated by EPRI, to an economic potential of 25% in 2020 as estimated by McK09, 

and technical potentials of ~25-40% in 2020. The achievable potentials for the EPRI range from 

~ 3-10% in 2020. The achievable potentials in 2020 and 2030 across the McK07 and EPRI 

studies, on a normalized basis, are ~ 0.4-1%/y. 

 
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Table 2 Energy demand reductions from national studies 

Scenario a Reductions (%) b Normalized savings 

(% per year) 

2030 savings 

achieved by 

2020 (%) 

2020 2030 2020 2030 

Electricity 

EPRI TP c 25 27 2.1 1.2 85 

EPRI EP 11 11 0.9 1.5 95 

EPRI MAP 9 8 0.7 0.4 97 

EPRI RAP 3 5 0.3 0.2 60 

McK07 highrange d,e -- ~23 -- ~1.1 -- 

McK07 midrange d,e -- ~20 -- ~0.9 -- 

McK09 TP 38 -- 3.8 -- -- 

McK09 EP 25 -- 2.5 -- -- 

AEO-08 BT/HT 13 17 1.1 0.8 67 

AEO-08 HT 4 5 0.3 0.2 63 

 

Nat gas 

McK07 highrange d,e -- ~17 -- ~0.8 -- 

McK07 midrange d,e -- ~11 -- ~0.5 -- 

McK09 TP 32 -- 3.2 -- -- 

McK09 EP 19 -- 1.9 -- -- 

AEO-08 BT/HT 7 10 0.6 0.5 66 

AEO-08 HT 2 3 0.1 0.1 56 

a

 Author and type of potential where “TP” is technical potential, “EP” is economic potential, “MAP” is maximum 

achievable potential, and “RAP” is realistic achievable potential 

b

 All savings are relative to the baseline energy consumption reported in AEO 2008 as “sales” to industrial, 

residential and commercial sectors. 

c

 EPRI savings are relative to the AEO 2008 baseline, not EPRI baseline. d

 The natural gas and electricity reduction potentials were estimated from measure-level CO2 emissions reductions 

provided by McKinsey. These estimates have not been validated by McKinsey. 

e

 Energy savings relative to the AEO 2008 baseline were roughly approximated by removing the savings from 

measures covered under the Energy Independence and Security Act (EISA) of 2007. 
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Figure 1. U.S. energy efficiency potential in 2020 and 2030 

The studies estimate significant economic and technical energy efficiency potentials for 

natural gas, ranging from 2%-20% in 2020 and 3%-11% in 2030. Note, however, the AEO-08 

side scenarios cannot be compared directly to the McKinsey results because the fundamental 

modeling methodology differs. Furthermore, as discussed the AEO side scenarios do not include 

the equivalent of an all-sector technical potential. 

The relative magnitude of different potential types — such as technical to economic, or 

economic to achievable potentials — reflect the assumptions on economic and market 

penetration factors. A relatively larger achievable-to-economic potential ratio indicates 

assumptions of larger market penetration and acceptance rates; a larger economic-to-technical 

AEO-08 

HT AEO-08 

HT

EPRI RAP, 

MAP, EP

EPRI RAP, 

MAP, EP

EPRI TP

EPRI 

TP

Mck09 

EP

Mck09 TP

Mck07 highrange 

& midrange

0%

10%

20%

30%

40%

50%

2020 2030

Electricity savings (% of AEO baseline consumption) 

AEO-08 HT EPRI RAP EPRI MAP

EPRI EP EPRI TP Mck09 EP

Mck09 TP Mck07 Highrange Mck07 Midrange
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potential suggests lower costs of energy efficiency, lower discount rates, or both. The EPRI 

economic potential (electricity) is ~45% (2020) and ~40% (2030) of the technical potential, and 

maximum achievable potentials are ~80% (2020) and ~70% (2030) of the economic potentials. 

The McK09 economic potential is ~65% of its technical potential for electricity and ~60% for 

natural gas in 2020. Compared to EPRI, a larger fraction of the technical potential is economic, 

likely from lower assumed costs of saved energy. The McK07 midrange potential, which may 

be construed roughly as an achievable potential, is ~90% of the McK07 highrange potential, 

which may be construed roughly as a technical potential. 

In all cases, a majority of the 2030 savings are achieved by 2020, suggesting that many of 

the savings from measures adopted through 2020 are available in 2030. These studies did not 

evaluate how program timing impacts the economics of reaching long term energy efficiency 

goals. However, this factor is likely to influence the cost-effectiveness of reaching long term 

goals due to the stock-roller effect. That is, by waiting to pursue energy efficiency, some costeffective opportunities may become unavailable, for example if a home owner replaced their 

refrigerator or air conditioning unit just prior to the program start date. 

Table 3 describes the impact on electricity growth rates. The impacts vary from lowering the 

electricity growth rate from 1% to 0.7% (EPRI RAP) or 0.3% (EPRI MAP), to turning it 

negative (McK09 potentials, McK07 highrange scenario, and EPRI TP). The AEO-08 high 

technology scenario roughly estimates the same demand reductions over these periods as the 

EPRI RAP scenario. 

Table 3 Impact on national electricity growth rates 

Scenario Demand growth (TWh) a Average annual growth (%) 
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2008-2020 2008-2030 2008-2020 2008-2030 

Baseline (AEO 08) 490 930 1.0 1.0 

EPRI TP -585 -320 -1.4 -0.4 

EPRI EP 15 430 0.0 0.5 

EPRI MAP 115 550 0.3 0.6 

EPRI RAP 345 695 0.7 0.8 

McK09 TP -1110 -- -2.5 b

 -- 

McK09 EP -595 -- -1.3 b

 -- 

AEO-08 BT/HT -55 130 -0.1 0.2 

AEO-08 HT 330 680 0.7 0.8 

a

 Rounded to the nearest 5 TWh. 

b

 Approximated using 2008 and 2020 electricity demand (in lieu of calculating year-to-year impacts). 

3.2 Greenhouse gas emissions reductions  

To illustrate an approximate relationship between energy efficiency potentials and GHG 

emissions, and the relative contribution of efficiency to goals specified in recent legislation, 

potential CO2 emissions reductions are estimated from the energy efficiency potentials by 

applying average electricity generation emissions factors from non-baseload units and natural 

gas combustion emissions factors (Table 4). The electricity emissions factor, obtained from the 

Environmental Protection Agency, is a proxy for a marginal emissions factor. The efficiency 

potentials overall constitute ~ 10%-100%+ of the greenhouse gas emissions reduction goals for 

2020 as required by the Waxman Markey climate bill (H.R. 2454); the economic electric 

efficiency potentials constitute ~ 35%-80% of these goals. 

The achievable GHG emissions reductions from potential electricity energy savings range 

from ~105-270 MMTCO2 in 2020 and 170-280 MMTCO2 in 2030. Reductions from the 

technical and economic potentials range from ~ 345-1165 MMTCO2 in 2020 and ~ 365-915 

MMTCO2 in 2030 (The upper limit for 2020 is from McK09, which did not publish savings for 

2030). For context, the GHG emissions reductions required in 2020 and 2030 by the Waxman 

Markey bill are 960 and 2850 MMTCO2 (on a CO2 equivalent basis), respectively. The 
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approximated energy efficiency CO2 emissions reductions of the electricity potential savings are 

a higher percentage of Waxman Markey required reductions in 2020, ranging from ~10% (EPRI 

RAP and AEO-08 high technology case) to ~120% (McK09 TP). The wide range between the 

EPRI MAP and EPRI EP, and the McK09 EP suggest that energy efficiency could provide 

anywhere from a third to a majority of the required GHG emissions reductions in 2020, if 

appropriate policies were affected. For 2030, the approximated CO2 emissions reductions from 

electricity savings are significantly less, with the EPRI technical potential constituting about a 

third of the total required GHG emissions reductions. 

Table 4 Possible carbon dioxide emissions reductions 

Scenario From electricity savings 

(MMTCO2) a

 

From natural gas savings 

(MMTCO2) b 

2020 2030 2020 2030 

EPRI TP 750 880 -- -- 

EPRI EP 330 350 -- -- 

EPRI MAP 260 270 -- -- 

EPRI RAP 100 165 -- -- 

McK 07 highrange -- ~ 760 -- ~ 140 

McK07 midrange -- ~ 645 -- ~ 90 

McK09 TP 1120 -- 255 -- 

McK09 EP 755 -- 150 -- 

AEO-08 BT/HT 380 560 55 85 

AEO-08 HT 110 175 15 25 

 

GHG reductions required in Waxman Markey, H.R. 2454 (MMTCO2e) c

2020 960 

2030 2850 

a

 CO2 reductions estimated using an emission factor of 0.70 kg CO2 /kWh (rounded to 5 MMTCO2) which 

represents the 2007 U.S. CO2 emissions rate from non-baseload units. (http://www.epa.gov/cleanenergy/energyresources/refs.html, accessed July 14, 2012) b

 CO2 reductions estimated using an emission factor of 0.047 kg CO2 / MJ (5.0 kg CO2/therm) (rounded to 5 

MMTCO2) c

 U.S. Environmental Protection Agency, 2009 
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3.3 Savings by sector and region  

Fig. 2 illustrates the potentials by sector (residential, commercial, industrial). Energy saving 

opportunities exist across all sectors. For electricity, each study indicates a significantly larger 

technical potential in the residential sector, followed by the commercial and then industrial 

sectors. Each study also indicates that a greater portion of the commercial and industrial 

measures are economic, compared to the residential measures. The magnitude of economic and 

technical industrial electricity savings measures rank below the residential and commercial 

sectors, although both McK09 and EPRI find a greater percentage of industrial opportunities 

economical, compared to the commercial and residential opportunities. The natural gas savings 

also indicate large economic and technical potential in the residential sector, although the relative 

magnitude of the economic savings suggest many measures are not cost effective. Industrial and 

commercial opportunities are generally more economic. 
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Figure 2.Site energy savings by sector in 2020 and 2030 

Assuming a comprehensive scope of measures, the technical potentials in each sector, 

normalized to the sector baseline consumption (indicated on Fig. 2), indicates the modelers’ 

assessment of that sector’s current state of energy efficiency. McK09 and EPRI indicate the 

residential sector to be the least efficient with ~30-60% reductions technically feasible; ~20-25% 

of industrial consumption are technically feasible. While McK09 suggests the industrial sector to 

be the most efficient, EPRI suggests the commercial sector to be more energy efficient. 

Fig. 3 shows how electricity savings vary by region. Almost uniformly, each study suggests 

significantly more electricity savings opportunities in the South, relative to the other three census 

regions; these savings are due both to the larger baseline energy use and lower levels of energy 

efficiency in the South, relative to the other regions. One exception is the EPRI realistic 

0%

5%

10%

15%

20%

25%

RAP MAP EP TP HT BAT EP TP HT BAT EP TP

EPRI AEO-08 MCK 09 AEO-08 MCK 09

Energy savings (% of AEO baseline consumption) 

Residential

Commercial

Industrial

Electricity Nat gas

Baseline split (resi, comm, ind): 

Elec: ~35, 40, 25% 

Nat gas: ~35, 20, 45%
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achievable potential, which does not indicate greater savings in the South, relative to the other 

regions. Note that the EPRI report does indicate larger absolute realistic savings in the South, 

relative to other regions. However, the discrepancy between Fig. 3 and the figures in EPRI 

(2009)1 is due to the differences between the EPRI and AEO 2008 baseline; specifically, the 

EPRI baseline indicates significantly larger projected energy consumption in the South and 

Northeast for 2020. We observed negligible differences in the regional breakdown of energy 

savings (on a normalized basis) for the economic, maximum achievable and technical potentials. 

Figure 3. Site energy savings by region in 2020 

 

1

 See Figure ES-13 for the regional breakdown of the EPRI RAP for 2020 (EPRI 2009). 

~ 20%

~ 45%

~ 15%

~ 20%

0%

5%

10%

15%

20%

Midwest Northeast South West

Electricity savings (% of AEO baseline consumption) 

EPRI RAP

EPRI MAP

EPRI EP

EPRI TP

MCK 09 EP

MCK 09 TP

regional baseline consumption indicated (as % of total) 
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While the absolute EPRI economic potential is greatest in the South, the relative portion of 

the technical potential that is economic is lower compared to other regions. While there are 

significant cost-effective opportunities in the South, some combination of high technology costs 

or low financial savings from energy efficiency due to low energy prices may prevent a greater 

portion of the technical opportunities from being economic. 

EPRI estimates a greater relative disparity between the realistic achievable and maximum 

achievable potentials in the South. Factors such as utility financing limits, political and market 

barriers to energy efficiency limit the implementation of energy efficiency. These limits are 

reflected in the assessment of realistic potential and are, based on the observed disparity between 

the RAP and MAP, assumed to be larger in the South than in other areas. 

Fig. 3 also indicates the regional breakdown of baseline electricity consumption. When the 

regional efficiency potentials are normalized by its regional consumption, there is little disparity 

in the efficiency potentials across the studies, although the South does exhibit somewhat lower 

relative maximum achievable and realistic achievable savings. 

Overall, the national studies are consistent with recent reports that estimate large efficiency 

potential in the Southeast — Chandler and Brown (2009) and Chandler (2010), which are 

meta-level analysis of energy efficiency potential studies and Brown et al. (2010), a study 

employing NEMS. 

3.4 Reconciling differences between EPRI and McKinsey studies  

Fig. 4 reproduces (with permission) the reconciliation between the EPRI and McK09 

economic potentials (McKinsey 2009b). The differences between the economic potentials are 

explained by differences in the scope, type and costs of technologies considered, and 

assumptions on technology innovation. With the exception of the categories of heat pumps and 
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commercial lighting, the technology assumptions applied by EPRI are less aggressive than those 

assumed by McKinsey. 

Figure 4 Comparing EPRI and McKinsey 2020 economic potentials (McKinsey, 2009) 

The difference between the EPRI and McK09 economic potentials is ~610 TWh or ~14% of 

the AEO 2008 baseline consumption in 2020. Three categories account roughly for this entire 

difference — the inclusion of additional electrical devices by McKinsey (~250 TWh or ~40% of 

the difference), the inclusion of additional technologies in some end uses by McKinsey 

(~160 TWh or ~25% of the difference), and accelerated equipment replacement by McKinsey 

(~180 TWh or ~30% of the difference). Accelerated equipment replacement occurred when the 

full equipment cost (not incremental) could be offset by energy savings; potential studies 

typically assume “natural” equipment turnover, rather than accelerated replacement (National 

473

250

160

80

180

60 120

1080

0

200

400

600

800

1000

1200

1400

EPRI EP McK: addt'l

elec devices

McK: more

tech's in

some end

uses

McK: more

market

segments

McK:

accelerated

equip

replacement

McK:

evolution of

LED lighting

EPRI: greater

heat pump

and comm'l

lighting

potential

McK EP

Electricity savings potential (TWh) 

~

~

~

~

~

~ * Includes small differences in technology 

performance and cost assumptions, discount 

rates, and electricity rates between the two 

reports. 

*

*

* * ~
 July 2012 

22 

Action Plan for Energy Efficiency 2007). The “additional electrical devices” include items such 

as more energy efficient televisions and other products. 

To better understand how economic assumptions vs. technology performance or scope 

assumptions drive the results, I compare the ratio of technical potentials with the ratio of 

economic potentials (i.e., McK09 to EPRI). The technical potential ratio is 1.5 and economic 

potential ratio is 2.3. While differences in scope and additional technologies cause the McK09 

technical potential to be ~ 50% larger than the EPRI potential, differences in economic 

assumptions drive the economic potentials further apart. EPRI assumes a 5% discount rate and 

consumer specific retail rates, while McK09 assumes a 7% discount rate and industrial rates. 

Both assumptions would serve to drive the economic potentials closer together. Lower 

technology costs and accelerated deployment assumed by McK09 compensate for the counter 

effects of the discount rate and electricity rates and drive the economic potentials such that the 

McK09 economic potential is ~130% larger than the EPRI potential. 

3.5 Costs for achieving energy efficiency potentials   

The costs reported in EPRI and McK09 are summarized.2

 McK09 estimates a total upfront 

investment cost of ~$520 Billion to pay for the estimated efficiency savings (or ~$50 Billion 

over 10 years). These costs represent capital investments only; McK09 did not estimate program 

administration costs. EPRI provides a low and high range of costs, based on historical levelized 

costs of saved energy, to achieve the realistic and maximum achievable potential savings in 

 

2

 For the purposes of this paper, capital investments include equipment, installation, operational costs for energy 

efficiency; program administration costs are a separate cost to the capital investments. Depending on the level of the 

incentive, total program costs may include capital investments in addition to program administration costs. 
 July 2012 

23 

2010, 2020 and 2030. The total implementation costs, on an annualized basis, for 2020 are 

estimated at ~$12-$34 Billion for the maximum achievable potential and ~$5-$18 Billion for the 

realistic potential. These include program administration and capital investments. 

It is not possible to directly compare these investments since the McK09 estimate represents 

total upfront capital investments for all fuel savings, and excludes program administration costs, 

while the EPRI estimate represents annualized implementation costs for 2020, rather than 

upfront investments, and are for electricity savings only. The average cost of saved energy, 

however, provides a basis for comparison. The average cost of saved energy to reach the McK09 

2020 economic potential is ~$0.018/kWh, while EPRI assumes a range of ~$0.03-0.06/kWh 

(EPRI 2009; Wickler 2009). 

It is appropriate to provide context of these costs based on published cost estimates of 

energy efficiency programs. Gillingham et al (2006) provides an excellent review on the efficacy 

and costs of energy efficiency policies and cites energy efficiency costs of utility programs from 

$0.008/kWh to $0.229/kWh across the literature. Other studies, using econometric methods, 

estimate costs of energy efficiency at an average of $0.05/kWh (5% discount rate) (Arimura et 

al. 2012) and range of $0.05-$0.13/kWh (Auffhammer et al. 2008). 

4 Comparison with recent state and regional studies  

Table 5 lists the results of recent state and regional studies of energy efficiency. With 

different baselines, timelines, data, and assumptions on technology, it is challenging to directly 

compare these with the national results; but they still provide useful policy input. Table 5 reports 

the normalized annual savings (total savings divided by total years) and compound annual 

savings. While it is hard to draw general conclusions from the numbers because of variability 
 July 2012 

24 

among the studies, achievable potentials average at ~ 1%/y (based on either a normalized or 

compound average savings rate); economic potentials are roughly double achievable savings and 

technical potentials are roughly double the economic potentials. 
 July 2012 

25 

Table 5 Energy demand reductions from recent state and regional studies a

Region Source Years Total savings (%) Normalized savings (%/y) b C

TP d EP d MP d TP EP MP T

Electricity: 

California Rufo and Coito 2002 10 19% 14% 10% 1.9% 1.4% 1.0% 1.Georgia ICF 2005 5 29% 20% 6% 5.8% 4.0% 1.1% 1.Iowa Eng Cent Wisc 2009 11 22% 13% 2.0% 1.2% Mass RLW 2007 5 19% 3.8% New Mex Itron 2006 10 8% 0.8% North Carolina GDS 2006 11 33% 20% 14% 3.0% 1.8% 1.3% 1.Texas OEI 2007 15 20% 1.3% Utah Tellus 2001 6 9% 1.5% Vermont GDS 2006 6 35% 19% 5.8% 3.2% 1.Wisconsin Eng Cent Wisc 2009 7 18% 11% 2.6% 1.6% 

Avg 4.1% 2.7% 1.2% 1.Min 1.9% 1.4% 0.8% 1.Max 5.8% 4.0% 1.6% 1.

Natural gas: California ACEEE 2003 20 35% 21% 9% 1.8% 1.1% 0.5% 1.

Georgia ICF 2000 5 10% 11% 4% 2.0% 2.2% 0.7% 1.Iowa Eng Cent Wisc 2009 11 22% 20% 2.0% 1.8% Midwest Quantec 2005 20 10% 25% 0.5% 1.3% 1.New York OEI/VEIC/ACEEE 2002 1.Utah GDS/Quantum 2004 10 38% 20% 3.8% 2.0% 1.Wisconsin Eng Cent Wisc 2009 7 19% 16% 7% 2.3% 1.0% 

Avg 3.5% 2.6% 1.1% 1.Min 0.5% 1.1% 0.3% 1.Max 9.3% 5.6% 2.0% 1.

a Summary data are drawn from the National Action Plan for Energy Efficiency (2007), Frisch (2008) and Energy Center of Wisconsin b Total savings divided by the years to reach the savings. c Calculated by (1-total savings) raised to the exponent (1/years-1). d TP = technical potential, EP = economic potential, MP – maximum achievable potential 
 July 2012 

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The state studies are comparable to the savings estimated by Mck07 and Mck09 but more 

aggressive than EPRI or AEO-08 side scenarios. There is a rough trend where studies with 

shorter timelines exhibit larger energy efficiency potentials, in part, possibly because many 

potential studies do not assume technological innovation over time (which makes it harder for 

estimated energy efficiency gains to offset growth in consumption). 

5 Conclusions  

From a policy perspective, transparent methods, assumptions and data are essential to 

interpret and validate the results of studies to estimate energy efficiency potential. For the 

purposes of this paper, many issues were confirmed offline with the authors. Potential studies 

should address issues such as uncertainty of input data assumptions and details on how measures 

are modeled, including how interactive effects among measures are treated (e.g., savings from 

efficient heating systems are affected by building envelope improvement). As shown by the 

McKinsey and EPRI study reconciliation, scope and cost-effectiveness assumptions can have 

dramatic impacts on results. 

Despite the variations in the methods, data and results, the reviewed potential studies inform 

U.S. national-level energy efficiency potential and energy policy. Both McK09 and EPRI 

underscore the huge potential for energy efficiency in the south. The national studies estimate 

achievable potentials of ~0.2%/y - 1%/y and state/regional studies estimate average savings of 

~1%/y. Following the completion of the analysis in this paper, the National Academies 

evaluation of energy efficiency potential was released (National Academies 2010). The findings 

of the National Academies study suggest significant achievable energy efficiency potential and 

contain results that are generally comparable to the results in the studies evaluated in this article; 
 July 2012 

27 

for example, energy efficiency could reduce U.S. energy use ~30% below forecasted 2030 

consumption levels and potentially reduce consumption below 2008 levels. 

Since the completion of the studies reviewed in this paper, natural gas prices have decreased 

substantially due to fracking and economic growth has slowed. These factors would alter the 

baseline and serve to lower estimates of energy efficiency potential. The analysis conducted in 

this report could be repeated with more recent studies (to the extent they have common baseline 

and scope). However, the objective of this paper was to identify consensus of energy efficiency 

potential among a comparable set of recent studies. Lower natural gas prices and slower 

economic growth is likely to affect each study similarly, thus, not affecting the overall objective. 

These studies may be useful for informing energy efficiency goals that could be achieved 

through either voluntary or regulatory programs, such as energy efficiency resource standards, 

and are already the basis for utility energy efficiency program goals in some states, such as 

California. By understanding the relative impact of market barriers, which reduce economic to 

achievable potentials, complementary policy mechanisms can be designed to target specific enduses or consumer classes. Energy efficiency potential studies may be useful, also, for informing 

climate policy design, in which multiple strategies are being explored and will likely be needed. 

Acknowledgements  

This work was supported in part by a fellowship administered by the American Association 

for the Advancement of Science (AAAS), in cooperation with the U.S. Environmental Protection 

Agency (under Cooperative Agreement No. X3 83232801). The author especially thanks Alan 

Sanstad of the Lawrence Berkeley National Laboratory, Joe Bryson and Jeff Brown of the U.S. 

Environmental Protection Agency, and C.K. Woo of Energy and Environmental Economics for 
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28 

their helpful suggestions. The following individuals are thanked for providing clarification on 

their respectively authored studies: Omar Siddiqui of the Electric Power Research Institute and 

Greg Wickler of Global Energy Partners; Jon Creyts, Phil Farese, Anton Derkach and Doug 

Weiss of McKinsey and Co.; John Cymbalsky and John Conti of the Energy Information 

Administration. I thank Omar Siddiqui for providing information from their report in electronic 

form and Jon Creyts for providing unpublished data and the permission to use them in this paper. 

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Figure Captions  

Figure 1. U.S. energy efficiency potential in 2020 and 2030 

Figure 2. Site energy savings by sector in 2020 and 2030 

Figure 3. Site energy savings by region in 2020 

Figure 4. Comparing EPRI and McKinsey 2020 economic potentials (McKinsey, 2009)