July 2012

1 Recent estimates of energy efficiency potential in the U.S. Priya Sreedharan, PhD, PE Energy and Environmental Economic s (E3), 101 Montgomery Street, 16 th 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 ener gy demand through increased end-use energy efficiency can inform energy and climate policy d ecisions. However, if potential estimates are vastly different, they engender controversia l debates, clouding the usefulness of energy efficiency in shaping a clean energy future. A su bstantive question thus ar ises: 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 gr owth 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 studyJuly 2012

2 1 Introduction Energy efficiency and sustainable energy tec hnologies 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 clim ate 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 en ergy codes, appliance st andards, a renewable energy/energy efficiency resource standard, a nd funding of efficiency programs (Waxman and Markey, 2009). At the state level, the Calif ornia Global Warming Solutions Act (“AB 32”) Scoping Plan requires an addi tional 10% of energy efficien cy savings in 2020 beyond the efficiency savings from existing policies, roughly 1% savings per year. Tw enty-four states have also enacted energy efficiency resource standa rds that require utilit ies (or other program administrators) to implement programs that deli ver ~ 0.5–1% savings per year, up to 2% in outer years (ACEEE 2010). Besides employment and health benefits, the fede ral and state legislative actions are driven by recent reports of significant and cost-effective opportunities fo r 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 atte ntion towards energy efficiency. However, if potential estimates are vastly di fferent, they engender controversial debates, clouding the usefulness of energy e fficiency in shaping a clean en ergy future. Is there generalJuly 2012

3 consensus on the potential estimates? To answer this question, this paper reviews four U.S. national and regional energy efficien cy potential in buildings and industry that include McKinsey (2007) (“McK07”), McKinsey (2009a) (“McK09”); EPRI (2009) (“EPRI”); and Annual Energy Outlook of Energy Information Admi nistration (2008) (“AEO”). These studies were chosen because they constitu te 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 ca se) and estimate savings over a common time frame (2010-2030). By utilizing a common energy fo recast, the treatment of existing codes and standards, demand side management programs, a nd ‘natural’ adoption of energy efficiency is consistent. The analysis conducted in this articl e could be repeated w ith more recent studies. However, the overall goal of this study, to unders tand 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 assump tions, methods, and data, they suggest technically possible reductions of ~ 25%–40% in electricity de mand 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. electric ity demand growth negative, to re ducing it to a growth rate of ~ 0.3% per year (compared to ~ 1% baseline growth). The effi ciency 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 ener gy efficiency potential due to multiple market barriers (Hirst and Brown 1990). Fa ctors contributing to the gap include market barriers thatJuly 2012

4 qualify as market failures (e.g., under-pricing of energy below social marginal cost, imperfect information, lack of financing, and tax policies th at discourage capital in vestment) and additional market barriers (e.g., high consumer discount rate s, renters lacking investment incentives, and consumer inertia caused by status quo bias) (B rown 2001; Brown and Ch andler 2008; Jaffe and Stavins 1994; Jaffe et al. 2004; Sanstad a nd 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 ef ficiency gap is projected to be alive and well, with the potential estimates remaining up to severa l 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 c ontext 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 ener gy efficiency “measures”, usually to support utility planning or state/federal energy policy. A measure is a ny action, including equipm ent upgrades, control strategies or behavioral changes, that increases energy efficiency in a given end-use application. The scope of the study can range fr om individual measures for a sp ecific end use in a utility’s service territory to a comprehe nsive group of measures on a nati onal level. Summary results typically describe the energy sa vings for a given timeframe, re lative to a baseline, and may include the costs to achieve these reductions (e.g., 10% reduction in el ectricity use within 10 years at a cost of $50 Billion).July 2012

5 There are four major types of “potential”. Th e first type is the technical potential. It represents the theoretical maxi mum amount of energy use displaceable by energy efficiency, disregarding non-engineering cons traints, such as costs of en ergy efficiency investment or consumer acceptance. The technical potential is computed from the baseline energy consumption forecast (which may be preexisting or be determ ined as part of the efficiency analysis) by disaggregating the baseline ener gy consumption by sector and e nd-use and applying efficiency improvements of different measures that are appropr iate to the baseline stock characteristics. The timing of the measures varies by ty pe; appliances are usually repla ced at the end of their natural lifetime, while measures such as lighting or commissioning can be adopted sooner. Technical potentials (and poten tial studies generally) tend to ignore price response, such as energy reductions due to conserva tion-encouraging electricity rate structures, or increased energy consumption because technology-based efficiency i nvestments 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 re sponse 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 nationa l 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 en ergy 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 bene fit-to-cost ratio that exceeds one) from the technical potential. Assumptions on discount rate and measure life strongly influence the economic potential.July 2012

6 The third potential type is the maximum ach ievable potential (sometimes referred to as ‘achievable potential’). It is a subset of economic potential th at can be expected with an aggressive program (e.g., end users are compen sated for the entire incremental cost of efficiency). Its computation enta ils applying market penetration ra tes 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 ma ximum 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 pote ntial (RAP), maximum achievable potential (MAP), economic potential (EP) and technical pote ntial (TP) are reported. They closely follow potential study formalisms and apply a part icipant cost test to estimate the EP. The McK09 study follows potential study conventi ons closely. The McK09 key scenario is an economic type of potential, which they de scribe as the net-present-value (NPV) positive energy efficiency potential. I refer to the NP V positive energy efficiency potential as an economic potential (EP). With permission from McKinsey, the unpublishe d technical potential (TP) energy savings for the McK09 study are re ported. McK09 estimates energy efficiency savings for 2020. The McK07 report estimates potential GHG emis sions 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.July 2012

7 Two AEO 2008 side scenarios related to e nd-use energy efficiency are included for comparison — the best available technology scenar io (BT), which is inte rpreted roughly as a technical potential, and a high technology scenar io (HT), which is inte rpreted roughly as an achievable potential. These do not employ energy e fficiency 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 genera ted a hybrid BT/HT scen ario 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 approach es employed by EPRI and McKins ey are similar, and (iii) the broad categories of energy efficiency meas ures are similar, a lthough the McK09 report considered a wider scope of measures (McK insey 2009b). On the other hand, the applied cost-effectiveness tests and a ssumed discount rates vary among the three studies. McKinsey employed their own test, which considered a measur e to be economic if the net present value of the energy savings, maintenance, and operational co st savings exceeded the present value of the incremental cost of the efficiency measure. Th e energy savings were estimated using industrial retail rates. EPRI used a 5% disc ount rate and a variati on of the participant cost test, in which the present value of consumer energy savings (bas ed on AEO projected reta il rates by consumer class) were compared to the consumer cost fo r purchasing the incremental value of the energy efficiency measure. The selection of the cost test and discount rate depends on th e 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 importa nt because it determines theJuly 2012

8 value of energy efficiency savings over the life time, relative to capital investments. Utility energy efficiency programs typically evaluate cost effectiveness using the ‘total resource cost test’, which represents economic impacts to societ y 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 th e studies evaluated. The discount rate from the participant perspective varies considerably and de pends 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, co mmercial and industrial cu stomers often require very short payback periods, implying discount rate s much greater than 20%. The National Action Plan on Energy Efficiency cost effectiveness re source guide provides an excellent overview of utility program cost effectiveness (Nationa l Action Plan for Energy Efficiency 2008). The choice of the discount ra te greatly affects the economic s of energy efficiency. The complexity in selecting an appropriate di scount rate and cost test underscore common controversies in conducting energy efficiency poten tial analysis. The McKins ey 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 ad dition to cost-effectiveness, several market failures (e.g., lack of information) and behavi oral 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 c onsumer and the economics from the consumer perspective may not warrant an in vestment in energy efficiency even if the investment is cost-effective to societal overall as determined by the total resource cost test.July 2012

9July 2012

10 Table 1 Methods, data and key a ssumptions 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-sideeconomizers, 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 th e 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 re tail rates and converted present value terms.July 2012

11 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 re ports’ authors. EPRI re ports their estimated potentials relative to both the AEO 2008 baseline a nd 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 th e McK07 savings, industria l CHP savings are not included for consistency w ith the other studies.

3.1 Aggregate national level energy savings Table 2 lists the national-level potential ener gy efficiency potentials in 2020 and 2030 with the electricity savings illustrated in Fig. 1. Thes e studies estimate techni cally 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 signifi cantly, ranging from a 3% realistic potential in 2020, as estimated by EPRI, to an economic pot ential of 25% in 2020 as estimated by McK09, and technical potentials of ~25- 40% in 2020. The achievable potenti als 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 ba sis, are ~ 0.4-1%/y.July 2012

12 Table 2 Energy demand reducti ons 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 consump tion reported in AEO 2008 as “sales” to industrial, residential and commercial sectors.
c EPRI savings are relative to the AE O 2008 baseline, not EPRI baseline. d The natural gas and electricity reduction pot entials were estimated from measure-level CO 2 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 Independ ence and Security Act (EISA) of 2007.July 2012

13 Figure 1. U.S. energy efficiency potential in 2020 and 2030 The studies estimate significant economic a nd technical energy efficiency potentials for natural gas, ranging from 2%-20% in 2020 a nd 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 disc ussed the AEO side scenarios do not include the equivalent of an all-s ector technical potential. The relative magnitude of different potential types — such as tec hnical to economic, or economic to achievable potentials — reflect the assumptions on economic and market penetration factors. A relatively larger achie vable-to-economic poten tial ratio indicates assumptions of larger market penetration and acceptance rates; a larg er economic-to-technical A EO-08 HT A EO-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 2030Electricity savings (% of AEO baseline consumption) AEO-08 HT EPRI RAP EPRI MAP EPRI EP EPRI TP Mck09 EP Mck09 TP Mck07 Highrange Mck07 MidrangeJuly 2012

14 potential suggests lower costs of energy effici ency, 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 te chnical 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 ener gy. The McK07 midrange potential, which may be construed roughly as an achie vable potential, is ~90% of th e McK07 highrange potential, which may be construed roughly as a technical potential. In all cases, a majority of the 2030 savings ar e 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 economi cs of reaching long term energy efficiency goals. However, this factor is likely to influe nce the cost-effectiveness of reaching long term goals due to the stock-roller e ffect. That is, by waiting to pursu e energy efficiency, some costeffective opportunities may beco me unavailable, for example if a home owner replaced their refrigerator or air conditi oning unit just prior to the program start date. Table 3 describes the impact on electricity grow th rates. The impacts vary from lowering the electricity growth rate from 1% to 0.7% (E PRI RAP) or 0.3% (EPR I 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 (%)July 2012

15 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 effici ency to goals specified in recent legislation, potential CO 2 emissions reductions are estimated fr om 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 fa ctor, obtained from the Environmental Protection Agency, is a proxy for a marginal emissions factor. The efficiency potentials overall constitute ~ 10%-100%+ of th e 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 MMTCO 2 in 2020 and 170-280 MMTCO 2 in 2030. Reductions from the technical and economic potenti als range from ~ 345-1165 MMTCO 2 in 2020 and ~ 365-915
MMTCO 2 in 2030 (The upper limit for 2020 is from McK09, which did not publish savings for 2030). For context, the GHG emissions reducti ons required in 2020 and 2030 by the Waxman Markey bill are 960 and 2850 MMTCO 2 (on a CO 2 equivalent basis), respectively. TheJuly 2012

16 approximated energy efficiency CO 2 emissions reductions of the el ectricity potential savings are a higher percentage of Waxman Markey requi red 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 s uggest that energy efficiency could provide anywhere from a third to a majority of th e required GHG emissions reductions in 2020, if appropriate policies were aff ected. For 2030, the approximated CO 2 emissions reductions from electricity savings are significan tly less, with the EPRI technical potential constituting about a third of the total required GHG emissions reductions.

Table 4 Possible carbon dioxi de emissions reductions

Scenario From electricity savings (MMTCO 2 ) a From natural gas savings (MMTCO 2 ) 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 Waxm an Markey, H.R. 2454 (MMTCO 2 e) c 2020 960 2030 2850

a CO 2 reductions estimated using an emission factor of 0.70 kg CO 2 /kWh (rounded to 5 MMTCO 2 ) which represents the 2007 U.S. CO 2 emissions rate from non-baseload units. ( http://www.epa.gov/cleanenergy/energyresources/refs.html , accessed July 14, 2012) b CO 2 reductions estimated using an emission factor of 0.047 kg CO 2 / MJ (5.0 kg CO 2 /therm) (rounded to 5 MMTCO 2 )
c U.S. Environmental Protection Agency, 2009July 2012

17 3.3 Savings by sector and region Fig. 2 illustrates the potentials by sector (res idential, commercial, i ndustrial). Energy saving opportunities exist across a ll sectors. For electricity, each stud y indicates a significantly larger technical potential in the resi dential sector, followed by the commercial and then industrial sectors. Each study also indi cates 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 measur es rank below the residential and commercial sectors, although both McK09 and EPRI find a gr eater percentage of industrial opportunities economical, compared to the commercial and re sidential opportunities. The natural gas savings also indicate large economic and technical potential in the reside ntial sector, although the relative magnitude of the economic savings suggest many m easures are not cost e ffective. Industrial and commercial opportunities are generally more economic.July 2012

18 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 consumpti on (indicated on Fig. 2), indicates the modelers’ assessment of that sector’s current state of en ergy efficiency. McK09 and EPRI indicate the residential sector to be the least efficient with ~30-60% reductions tech nically feasible; ~20-25% of industrial consumption are tec hnically feasible. While McK09 s uggests the industrial sector to be the most efficient, EPRI suggests the commer cial sector to be mo re energy efficient. Fig. 3 shows how electricity savings vary by region. Almost uniformly, each study suggests significantly more electricity savi ngs opportunities in the South, rela tive 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 09Energy savings (% of AEO baseline consumption) Residential Commercial Industrial Electricit y Nat gas

Baseline split (resi, comm, ind): Elec: ~35, 40, 25% Nat gas: ~35, 20, 45%July 2012

19 achievable potential, which does not indicate grea ter savings in the Sout h, relative to the other regions. Note that the EPRI report does indicate larger absolute realisti c savings in the South, relative to other regions. However, the discre pancy 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 signifi cantly larger projected energy consumption in the South and Northeast for 2020. We observed negligible diffe rences in the region al breakdown of energy savings (on a normalized basis) fo r the economic, maximum achievable a nd 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 WestElectricity 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)July 2012

20 While the absolute EPRI economic potential is greatest in the South, th e relative portion of the technical potential that is economic is lower compared to other regions. While there are significant cost-effective opportuniti es in the South, some combination of high technology costs or low financial savings from en ergy efficiency due to low energy prices may prevent a greater portion of the technical opport unities 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 implemen tation of energy efficiency. These limits are reflected in the assessment of realistic potential and are, based on the obs erved disparity between the RAP and MAP, assumed to be larger in the South than in other areas. Fig. 3 also indicates the regi onal breakdown of baseline elec tricity consumption. When the regional efficiency potentials ar e normalized by its regional consump tion, there is little disparity in the efficiency potentials across the studies , although the South does exhibit somewhat lower relative maximum achievable and re alistic achievable savings. Overall, the national studies are consistent with recent reports th at estimate large efficiency potential in the Southeast — Chandler and Brown (2009) a nd Chandler (2010), which are meta-level analysis of energy efficiency pot ential studies and Brow n et al. (2010), a study employing NEMS.

3.4 Reconciling differences between EPRI and McKinsey studies Fig. 4 reproduces (with permission) the re conciliation between the EPRI and McK09 economic potentials (McKinsey 2009b). The diffe rences between the economic potentials are explained by differences in the scope, type and costs of technol ogies considered, and assumptions on technology innovation. With the ex ception of the categories of heat pumps andJuly 2012

21 commercial lighting, the technology assumptions app lied by EPRI are less a ggressive than those assumed by McKinsey.

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

The difference between the EPRI and McK09 ec onomic 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 a dditional electrical devices by Mc Kinsey (~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 ac celerated equipment replacement by McKinsey (~180 TWh or ~30% of the differe nce). Accelerated equipment replacement occurred when the full equipment cost (not increm ental) could be offset by ener gy 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 EPElectricity 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 “a dditional electrical devices ” include items such as more energy efficient televi sions and other products. To better understand how economic assumpti ons 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). Th e technical potential ra tio is 1.5 and economic potential ratio is 2.3. While differences in scope and additional technol ogies cause the McK09 technical potential to be ~ 50% larger than the EPRI poten tial, differences in economic assumptions drive the economic pot entials further apart. EPRI a ssumes a 5% discount rate and consumer specific retail rates, while McK09 assu mes a 7% discount rate and industrial rates. Both assumptions would serve to drive the economic potentials closer together. Lower technology costs and accelerated deployment a ssumed by McK09 compensate for the counter effects of the discount rate and electricity rates and drive the ec onomic potentials such that the McK09 economic potential is ~130% larg er 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 inve stments 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 achi evable 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 achi evable 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 sa vings, and excludes progra m administration costs, while the EPRI estimate repres ents annualized implementati on 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 aver age cost of saved ener gy 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. Ot her studies, using econometric methods, estimate costs of energy efficiency at an aver age of $0.05/kWh (5% disco unt rate) (Arimura et al. 2012) and range of $0.05-$0.13/kW h (Auffhammer et al. 2008).

4 Comparison with recent state and regional studies
Table 5 lists the results of recent state and regional studies of en ergy efficiency. With different baselines, timelines, data, and assumpti ons on technology, it is challenging to directly compare these with the national re sults; but they still provide us eful policy input. Table 5 reports the normalized annual savings (total savings divided by total year s) and compound annual savings. While it is hard to draw general conclu sions from the numbers because of variabilityJuly 2012

24 among the studies, achievable poten tials average at ~ 1%/y (bas ed on either a normalized or compound average savings rate); economic potentia ls are roughly double achievable savings and technical potentials are roughly double the economic potentials.July 201225Table 5 Energy demand reductions from recent state and regional studiesaRegion Source Years Total savings (%) Normalized savings (%/y)bCompound savings rate (%/y)cTPdEPdMPdTP EP MP TP EP MPElectricity:California Rufo and Coito 2002 10 19% 14% 10% 1.9% 1.4% 1.0% 1.2% 1.1% 1.1%Georgia ICF 2005 5 29% 20% 6% 5.8% 4.0% 1.1% 1.3% 1.2% 1.0%Iowa Eng Cent Wisc 2009 11 22% 13% 2.0% 1.2% 1.3%Mass RLW 2007 5 19% 3.8% 1.2%New Mex Itron 2006 10 8% 0.8% 1.1%North Carolina GDS 2006 11 33% 20% 14% 3.0% 1.8% 1.3% 1.4% 1.2% 1.1%Texas OEI 2007 15 20% 1.3% 1.2%Utah Tellus 2001 6 9% 1.5% 1.1%Vermont GDS 2006 6 35% 19% 5.8% 3.2% 1.4% 1.2%Wisconsin Eng Cent Wisc 2009 7 18% 11% 2.6% 1.6% 1.2% 1.1%Avg4.1% 2.7% 1.2% 1.3% 1.2% 1.1%Min1.9% 1.4% 0.8% 1.2% 1.1% 1.0%Max5.8% 4.0% 1.6% 1.4% 1.3% 1.2%Natural gas:California ACEEE 2003 20 35% 21% 9% 1.8% 1.1% 0.5% 1.5% 1.3% 1.1%Georgia ICF 2000 5 10% 11% 4% 2.0% 2.2% 0.7% 1.1% 1.1% 1.0%Iowa Eng Cent Wisc 2009 11 22% 20% 2.0% 1.8% 1.3% 1.2%Midwest Quantec 2005 20 10% 25% 0.5% 1.3% 1.1% 1.3%New York OEI/VEIC/ACEEE 20021.7% 1.3% 1.0%Utah GDS/Quantum 2004 10 38% 20% 3.8% 2.0% 1.5% 1.2%Wisconsin Eng Cent Wisc 2009 7 19% 16% 7% 2.3% 1.0% 1.2% 1.1%Avg3.5% 2.6% 1.1% 1.4% 1.2% 1.1%Min0.5% 1.1% 0.3% 1.1% 1.1% 1.0%Max9.3% 5.6% 2.0% 1.7% 1.3% 1.3%aSummary data are drawn from the National Action Plan for Energy Efficiency (2007), Frisch (2008) and Energy Center of Wisconsin and ACEEE (2009).bTotal savings divided by the years to reach the savings.cCalculated by (1-total savings) raised to the exponent(1/years-1).dTP = technical potential, EP = economic potential, MP – maximum achievable potentialJuly 2012

26 The state studies are comparable to the savings estimated by Mck07 and Mck09 but more aggressive than EPRI or AEO-08 side scenario s. There is a rough trend where studies with shorter timelines exhibit larger energy effici ency potentials, in part, possibly because many potential studies do not assume technological innovation over tim e (which makes it harder for estimated energy efficiency gains to offset growth in consumption).

5 Conclusions From a policy perspective, tr ansparent 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 confir med offline with the authors. Potential studies should address issues such as uncertainty of i nput 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 build ing 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 a nd results, the reviewed potential studies inform U.S. national-level energy efficiency potenti al 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 st ate/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 re leased (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 grow th 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 st udies (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 st udies. Lower natural gas prices and slower economic growth is likely to affect each study sim ilarly, thus, not affecting the overall objective. These studies may be useful for informing en ergy efficiency goals th at could be achieved through either voluntary or regulatory programs, such as energy efficiency resource standards, and are already the basis for utility energy effici ency program goals in some states, such as California. By understanding the relative impact of market barriers, which reduce economic to achievable potentials, complementary policy mechan isms can be designed to target specific enduses or consumer classes. Energy efficiency pote ntial studies may be usef ul, also, for informing climate policy design, in which multiple strategies are being explored and w ill likely be needed.
Acknowledgements This work was supported in part by a fellowshi p administered by the American Association for the Advancement of Science (AAAS), in coope ration with the U.S. Environmental Protection Agency (under Cooperative Agreement No. X3 83232801) . The author especially thanks Alan Sanstad of the Lawrence Berkeley National La boratory, Joe Bryson and Je ff Brown of the U.S. Environmental Protection Agency, and C.K. Wo o of Energy and Environmental Economics forJuly 2012

28 their helpful suggestions. The following indivi duals 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 Cymbals ky and John Conti of the Energy Information Administration. I thank Omar Siddiqui for providi ng information from their report in electronic form and Jon Creyts for providing unpublished data a nd the permission to use them in this paper.

References
American Council for an Energy Effi cient Economy (2010). State Energy E fficiency resource standard (EERS) activity. American Council for an Energy-Efficient Economy. Arimura, T., Shanjun, L., Newell, R.G., Palmer, K. (2012) . Cost effectiveness of elect ricity energy efficiency programs. The Energy Journal , 33(2), 63-98. Auffhammer, M., Blumstein, C., Fowlie, M. (2008). Demand side management and energy efficiency revisited. The Energy Journal , 29(3), 91-104. Brown, M.A., Gumerman, E., Sun, X., Baek, Y., Wang, J., Cortes, R., Soumonni , D. (2010). Energy efficiency in the south. Southeast Energy Efficiency Alliance, Atlanta, GA. Chandler, J. (2010). A pr eliminary look at electric efficiency potential. The Electricity Journal , 23(1), 85-91. Chandler, S. (J), Brown, M. (2009). Me ta-Review of efficiency potential studies and their implications for the south. Georgia Institute of Technology, Working Paper #51. Electric Power Research Institute (2009 ). Assessment of achievable poten tial from energy efficiency and demand response programs in the U.S. (2010-2030). Palo Alto, California, Report 1016987. Energy Center of Wisconsin and American Council for an Energy Efficient Economy (200 9). A review and analysis of existing studies of the energy efficiency resource potential in the midwest. A policy white paper in support of the Midwestern governors associatio n energy and climate change platform. Energy Center of Wisconsin, ECW Report Number 247-1. Eyre, N. (1998). A golden age or a false dawn? Energy efficiency in UK competitive energy markets. Energy Policy , 26(12), 963-972. Fisher, A.C., Rothkopf , M.H. (1989). Market failure an d energy policy. A rationale for selective conservation. Energy Policy , 17(4), 397-406.July 2012

29 Frisch, C. (2008). Electric utility demand side management: defining and evaluating achievable potential. Masters project, Duke University. Gellings, C.W., Wikler, G., Ghosh, D. (2006). Assessment of U.S. electric end-use en ergy efficiency potential. The Electricity Journal , 19(9), 55-69. Gillingham, K., Newell, R.G., Palmer, K. (2006). Retrospective review of energy efficiency policies. Annual Review of Environment and Resources , 31, 10.1-10.31. Gillingham, K., Newell, R.G., Palmer, K. (2009) . Energy efficiency economics and policy. Annual Review of Resource Economics , 1, 14.1-14.23. Greening, L., Greene, D. L., Difiglio, C. (2000). Energy efficiency and consumption — the rebound effect — a survey. Energy Policy , 28(6 7), 389-401. Hassett, K., Metcalf, G.E. (1993). Energy conservation investment. Do consumers discount the future correctly? Energy Policy , 21(6), 710-716. Interlaboratory Working Group (2000). Scenarios for a clean energy future. Oak Ri dge, Tennessee and Berkeley, California, Oak Ridge National Laboratory and Lawr ence Berkeley National Laboratory, Report ORNL/CON 476 and LBNL 44029. Jaffe, A.B., Stavins, R.N. (1994). The ener gy efficiency gap. What does it mean? Energy Policy , 22(10), 804-810. Jaffe, A.B., Newell, R.G., Stavins, R.N. (2004). Economics of Energy Efficiency. Encyclopedia of Energy , 2, 79-90. Lutzenhiser, L. (1994). Innovation and organizational networks. Barriers to energy efficiency in the US housing industry. Energy Policy , 22(10), 867-876. McKinsey & Co. (2009b). EPRI and McKinsey reports on energy efficiency: a compar ison, McKinsey and Co. Metcalf, G.E. (1994). Economics and rational conservation policy. Energy Policy , 22(10), 819-825. National Academies (2010). Real prospects for energy effi ciency in the United States. National Academies Press. Washington, D.C. National Action Plan for Energy Effici ency (2007). Guide for conducting energy effici ency potential studies. Prepared by Philip Mosenthal and Jeffrey Loiter, Optimal Energy, Inc. National Action Plan for Energy Efficiency (2008). Understa nding cost-effectiveness of en ergy efficiency programs: best practices, technical methods, and emerging issues for policy-makers. Prepared by Energy and Environmental Economics, Inc and Regulatory Assistance Project. Rufo, M., Coito, F. (2002). California's secret energy surplus: the potential for energy efficiency. The Energy Foundation and The Hewlett Foundation.July 2012

30 Sanstad, A.H., Howarth, R.B. (1994). Normal mark ets, market imperfections and energy efficiency. Energy Policy , 22(10), 811-818. Sanstad, A.H., Blumstein, C., Stoft, S.E. (1995). How high are option values in energy efficiency investments? Energy Policy , 23(9), 739-743. Sorrell, S., Dimitropoulos, J., Sommerville, M. (2009). Empi rical estimates of the direct rebound effect: a review. Energy Policy , 37(4), 1356-1371. United States Environmental Protection Ag ency (2009). EPA preliminary analysis of the American clean energy and security act of 2009 in the 111th Co ngress. (GHG emissions reductions were drawn from IGEM model results.) Wickler, G. (2009). Personal communication.

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)