### LBNL

- 5803E



# The Future of Utility Customer

Funded Energy Efficiency Programs

in the United States : Projected Spending and Savings to 2025

Galen L. Barbose, Charles A. Goldman, Ian M. Hoffman, Megan Billingsley

Environmental Energy Technologies Division

January 2013

This work was supported by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory Contract No. DE

- AC02

- 05CH11231

. 

# E RNEST 

# O RLANDO 

# L AWRENCE


# B ERKELEY 

# N ATIONAL 

# L ABORATORY

### Disclaimer

This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor The Regents of the University of California, nor any of their employees, makes any warranty, express or implied, o r assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific

commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or The Regents of

the Unive rsity of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, or The Regents of the University of California.

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

### LBNL

- 5803E



# The Future of Utility Customer

- Funded Energy Efficiency Programs in the United States : Projected Spending and Savings to 2025

Prepared for the Office of Electricity Delive ry and Energy Reliability National Electricity Delivery Division U.S. Department of Energy

Principal Authors

Galen L. Barbose, Charles A. Goldman, Ian M. Hoffman, Megan Billingsley

Ernest Orlando Lawrence Berkeley National Laboratory 1 Cyclotron Road, MS 90R4000 Berkeley CA 94720

- 8136

January 2013



### The work described in this study was funded by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory Contract No. DE

- AC02

- 05CH11231

.

### 2 Acknowledgements

The work described in this study was funded by the National Electricity Delivery Division of the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Lawrence Berkeley National Laboratory Contract No. DE

- AC02

- 05CH11231. We would particularly like to thank Larry Mansueti of the U.S. Department of Energy for his support of this work. For providing comments on an earlier draft and/or providing information that was used in the analysis , the authors would like to thank Sheryl Carter (NRDC), Rich Sedano (RAP), Lara Ettenson ( NRDC), Anthony Star (Illinois Commerce Commission), Brian Granahan (Illinois Commerce Commission), Sa m Gomberg (formerly with SACE), Jay Zarnikau (Frontier Associates)

, Amy Martin (Frontier Associates)

, Steve Schiller (Schiller & Associates)

, Hale

Powell (Hale Powell Energy)

, Dian Grueneich (Dian Grueneich Consulting, LLC)

, Joe Bryson (EPA)

, David Walker (MI LARA)

, Howard Geller (SWEEP)

, Elizabeth Noll (AGA)

, Mariam Arnaout (AGA)

, Wayne Williams (Pennsylvania PUC)

, Chrissy Godfrey (MD PSC)

, Kara Rodgers (CEE)

, Mandy Mahoney (SEEA)

, Mike Ambrosi o (Applied Energy Group )

, Cheryl Roberto (Ohio PUC)

, Tom Larson (SACE )

, John Wilson (SACE)

, Ken Smith (SACE )

, Jill Tauber (SACE )

, Shawn Ca rraher (La Capra Associates )

, Josh Craft (NEEP)

, Jim O'Reilly ( NEEP)

, Tom Eckman (NPCC)

, Jeff Harris (NEEA)

, Greg Ehrendreich (MEEA)

, Marcus Preston (NiSource)

, Jeff Schlegel (SWEEP)

, Holly Meyer (NW Natural Gas)

, Bill Edmonds (NW Natural Gas)

, Teri Van Sumeren (Consumers Energy)

, Steve Bateson (Questar Gas)

. We

also thank Anthony Ma for assistance with formatting and production. Of course, any remaining omissions or inaccuracies are our own.

### 3 Table of Contents

Abstract

................................ ................................ ................................ ................................ ........... 5

1. Introduction

................................ ................................ ................................ ............................... 6

2. Recent Policy D evelopments and Trends

................................ ................................ ................. 7

2.1. Policy Drivers for Utility Customer

- Funded Energy Efficiency Programs

.................... 8

2.2. Current and Historical Spending on Utility Customer

- Funded Energy Efficiency Programs

................................ ................................ ................................ ....................... 10

3. Analytical Approach

................................ ................................ ................................ ............... 12

3.1. Electric Energy Efficiency Program Spending and Savings Projections

...................... 12

3.2. Gas Energy Efficiency Program Spending Projections

................................ ................ 15

4. Results

................................ ................................ ................................ ................................ ..... 17

4.1. Combined Electric and Gas Energy Efficiency Program Spending Projections

.......... 17

4.2. Electric Energy Efficiency Program Spending Projections

................................ .......... 18

4.3. Electric Energy Efficiency Program Savings Projections

................................ ............ 21

4.4. Gas Energy Efficiency Program Spending Projections

................................ ................ 23

5. Discussion of Key Issues and Uncertainties

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

5.1. Broader Market and Policy Context

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

5.2. Energy Efficiency Program Implementation and Regulatory Oversight

...................... 27

6. Conclusions

................................ ................................ ................................ ............................. 30

7. Reference s

................................ ................................ ................................ ............................... 31

Technical Appendix A: Methodology and Assu m ptions Used to Develop Energy Efficiency Spending and Savings Projection s

................................ ................................ .......................... 33

Technical Appendix B: State

- Level Spending and Savings Projections

................................ ...... 48

### 4 List of Figures and Tables

Figure 1. Projected Electric and Gas Energy Efficiency Program Spending

............................... 18 Figure 2. Policy Drivers for Projected Electric Energy Efficiency Spending in the Medium Case (2025)

................................ ................................ ................................ ................................ ...... 19 Figure 3. Projected Electric Energy Efficiency Spending by Census Region

.............................. 20 Figure 4. Regional Distribution of Electric Energy Efficiency Program Spending (Medium Case)

................................ ................................ ................................ ................................ ................. 21 Figure 5. Electric Energy Efficiency Program Spending as a Percent of Utility Revenues (Medium Case)

................................ ................................ ................................ ........................ 21 Figure 6. Projected Electricity Savings from Utility Customer

- Funded Efficiency Programs

..... 22 Figure 7. Projected Gas Energy Efficiency Program Spending

................................ .................... 24 Figure 8. Projected Gas Energy Efficiency Program Spending as a Percentage of Gas Distribution Utility Revenues

................................ ................................ ................................ . 24

Figure A

- 1. U.S. Census Regions Used for Scenario Development

................................ ........... 36 Figure A

- 2. Generic Program Cost Function

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

Table 1: Policy Drivers for Customer

- Funded Energy Efficiency Program Activity

.................... 9

Table 2. 201 0 Expenditures for Utility Customer

- Funded Energy Efficiency (EE) Programs

.... 11

Table 3. Scenario Assumptions for Electric Energy Efficiency Projections

................................ 14

Table 4. Scenario Assumptions for Gas Energy Efficiency Projections

................................ ...... 15

Table 5. Projected Electric Energy Efficiency Spending

................................ .............................. 19

Table 6. Projected Incremental Annual Electricity Savings from Utility Customer

- Funded Programs (TWh)

................................ ................................ ................................ ..................... 22

Table A

- 1. AEO Projected Growth Rates in Retail Electricity Sales and Prices

....................... 34

Table A

- 2. Estimate Customer

- Funded Energy Efficiency Program Savings Embedded in AEO Forecasts by EMM Region

................................ ................................ ................................ ..... 35

Table A

- 3. Scenario Descriptions for States in the Northeast

................................ .................... 36

Table A

- 4. Scenario Descriptions for States in the South

................................ .......................... 38

Table A

- 5. Scenario Descriptions for States in the Mid west

................................ ...................... 39

Table A

- 6. Scenario Descriptions for States in the West

................................ ............................ 40

Table A

- 7. Energy Efficiency Spending Assumed for “Uncommitted” States

.......................... 42

Table A

- 8. Analysis Framewor k for Natural Gas Energy Efficiency Programs

........................ 44

Table A

- 9. Gas Energy Efficiency Program Scenario Descriptions: Tier I States

..................... 45

Table A

- 10. Gas Energy Efficiency Program Spending: Tier II States

................................ ...... 47

Table B

- 1. Electric Efficiency Program Spending Projections by State ($M, nominal)

............ 4 9

Table B

- 2. Electric Efficiency Program Savings Projections by State (First

- Year GWh)

......... 50

Table B

- 3. Gas Efficiency Program Spending Projections by State ($M, nominal)

.................. 51

### 5 

## Abstract

We develop projections of future spending on
, and savings from

, energy efficiency programs funded by electric and gas utility customers in the United States, under three scenarios through 2025. Our analysis , which updates a previous LBNL study, relies on detailed bottom

- up modeling of current state energy efficiency policies, regulatory deci sions, and demand

- side

management and utility resource plans. The three scenarios are intended to represent a range of potential outcomes under th e current policy environment (i.e., without considering possible major new policy developments )

.

Key findings from the analysis are as follows:

**•** By 2025, s pending on electric and gas efficiency programs (excluding load management programs) is projected to double from 2010 levels to $ 9.5 billion in the medium case, compared to $ 15.6 billion in the high case and $ 6.5 billion in the low case

.

**•** Compliance with statewide legislative or regulatory savings or spending targets is the primary driver for the incre ase in electric program spending through 2025 , though a significant share of the increase is also driven by utility DSM planning activity and integrated resource planning

.

**•** Our analysis suggests that electric efficiency program spending may approach a mor e even geographic distribution over time in terms of absolute dollars spent , with the

Northeast ern and West ern states declining from over 70% of total U.S. spending in 2010 to slightly more than 50% in 2025, and the South and Midwest splitting the remainder roughly evenly

.

**•** Under our medium case scenario, a nnual incremental savings from customer

- funded electric energy efficiency programs increase from 18.4 TWh in 2010 in the U.S. ( which

is a bout 0. 5 % of electric utility retail sales) to 28

. 8 TWh in 2025 (0. 8 % of retail sales)

.

**•** These savings would offset the majority of load growth in the Energy Information

Administration’s most recent reference case forecast of retail electricity sales through

2025

, given specific assumptions about the extent to which future energy efficiency program savings are captured in that forecast

.

**•** T he pathway that customer

- funded efficiency programs ultimate ly take will depend on a

series of key challenges and uncertainties associated both with the broader market and policy context and with the implementation and regulatory oversight of the energy efficiency programs themselves

.

### 6 

## 1. Introduction

Electric and natural gas energy efficiency in the United States is pursued through a diverse mix of policies and programmatic efforts , which support and supplement private investments by

individuals and businesses . These efforts include federal and state minimum efficiency standards for electric and gas end

- use products; state building energy codes; a national efficiency labeling program (ENERGY STAR®); tax credits; and a broad array of largely

incentive

- based programs for consumers, fun ded primarily by electric and natural gas utility

customers (Dixon et al

. 201 0 ). 1 

### Over the past four decades, policy support and utility customer funding of energy efficiency programs, in particular, has ebbed and flowed. 2 

### Utilities first

launched substant ial programs in the wake of the 1973 energy crisis, and those programs grew and matured with the expansion of integrated resource planning and demand

- side management during the 1980s and 1990s (Nadel 1992). Spending on energy efficiency by utilities then declined sharply in many states in the late 1990s, with the restructuring of the electricity industry. However, the western energy crisis of 2000

- 2001 brought renewed attention to energy efficiency as an important strategy for managing and containing costs for electric utility

customers

. 3



### Since then, many state regulatory agencies and legislatures have sought to prioritize energy efficiency, in some cases strengthening and supp lementing pre

- existing policies by requiring comprehensive electric and gas sy stem resource planning, developing funding mechanisms and energy savings targets, and creating business incentives for program administrators to deliver energy efficiency to customers. In some states, regulators have also extended demand

- side planning, sav ings targets, or business incentive mechanisms from the electricity sector to large regulated natural gas utilities.

A variety of organizations and analysts have examined trends in utility customer

- funded energy efficiency programs in the United States. These include efforts to document historical and recent trends in spending

, savings or both (Nadel 1992; Sciortino et al. 2011; Cooper and Wood 2012; CEE 2012), as well as estimates of the projected impact of individual policies related to utility custo mer

- funded energy efficiency (Nowak et al. 2011) or in particular regions (Hopper et al. 2008). Yet other studies have sought to estimate the potential savings that could be obtained through customer

- funded efficiency programs, including an innumerable nu mber of such studies conducted for individual utilities or states, as well as several national studies (EPRI 2009). The present study builds upon the body of existing literature by comprehensively assessing the potential impact of the full suite of polici es and market conditions relevant to the future of utility customer

- funded energy efficiency programs in the United States, updating an earlier LBNL analysis (Barbose et al. 2009).

1 The American Recovery and Reinvestment Act (ARRA) provided a massive but temporary infusion of federal funding for energy efficiency (~$15

- 20B in programs administered by federal, state and local governments to be spent over three years) (Goldman et al. 2 011).

2 Geller et al. (2006) provide an overview of the efficiency policy landscape among nations in the Organization of Economic Cooperation and Development, including the U.S., while Gillingham et al. (2006) provide a comprehensive review for the United States.

3 Energy efficiency programs administered by U.S. gas distribution utilities have also increased over time but are much smaller in size than electric efficiency programs (York et al. 2012).

### 7 Specifically, we project future spending on

, and savings from

, U.S. elec tric and gas efficiency programs to 2025 under low, medium, and high scenarios. The projections are based on a

detailed

, bottom

- up review and modeling of all relevant state policies and legislation, regulatory decisions, and utility integrated resource an d demand

- side management plans. The three

scenarios are intended to represent a range of potential outcomes under the current policy environment, given uncertainties in policy implementation and in the broader economic and policy environment (e.g., utilit y business models, the extent to which energy efficiency is a policy priority, and concerns about rate impacts ). The three scenarios are not intended to

encompass major new federal policy developments, which could naturally expand the range of potential outcomes beyond those modeled here. 4 

### Scenario definitions and assumptions were also informed by interviews with regional and national energy efficiency experts, program administrators, regulatory staff and other industry stakeholders. Based on the quantit ative analysis of projected spending and savings under varying policy implementation scenarios, we identify and discuss the broader themes and issues that will influence which of the potential projections are most likely to transpire.

The study has relev ance to a broad range of audiences: utilities and other entities responsible for administering customer

- funded efficiency programs and the state regulatory agencies responsible for overseeing their implementation; policymakers, planners, and industry analy sts seeking to understand the potential impact of these programs on the broader electricity market or their implications for other policies; and the energy services industry seeking to understand market trends and opportunities. While this study focuses o n the United States, the analysis also has relevance to policymakers abroad where energy and environmental policies may require the development of specific long

- term energy savings goals and/or funding mechanisms for voluntary incentive

- based programs, suc h as those that are prevalent in the United States. For example, the set of potential trajectories of U.S. efforts potentially offers a window on the prospects and issues raised by the 2012 European Union Energy Efficiency Directive ( Directive 2012/27/EU)

, in which the European Parliament and Council committed member states to adopting efficiency targets and submitting implementation plans consistent with a EU

wide target of saving 20% of the projected primary energy consumption in 2020 (Boonekamp 2011)

.

The remainder of the paper is organized as follows. Section 2 provides an overview of the key policy drivers that influence future efficiency program spending and savings, and summarizes current trends in spending on energy efficiency programs across stat es. Our modeling approach for capturing policy and market influences on future spending and savings for electric and gas efficiency programs is described in Section 3. The results of our analysis are presented in

Section 4 . I n Section 5, we identify key challenges and discuss significant uncertainties in market and policy drivers that may influence the path forward for customer

- funded efficiency

programs.



## 2. Recent Policy Developments and Trends

4 By virtue of limiting the analysis to current energy e fficiency policies, we do not consider the potential impact of major new federal (or state) policy initiatives (e.g., a national energy efficiency resource standard, clean energy standard, or carbon policy) that could result in customer

- funded energy effic iency program spending and savings that exceed the values in our High Case.

### 8 Over the past decade, an increasing number of states have ad opted policies that encourage or require utility customer

- funded energy efficiency programs. In this section, we summarize recent trends in the development of these policies and the current and historical spending levels

across states.

2.1. Policy Drivers for Utility Customer

- Funded Energy Efficiency Programs

In the utility sector, policies that drive investment in energy efficiency include: system benefit charges ; energ y efficiency resource standards; renewable portfolio standards under which energy effic ien cy is a qualifying resource; requirements that utilities obtain “all cost

- effecti ve energy efficiency” resources; long

- term integrated resource planning requirements; and multi

year demand

- side management planning requirements (see Table 1 ). Naturally, the scope and level of aggressiveness of each type of policy can vary substantially across states, and many states have adopted multiple policies in tandem.

A number of these policy drivers are relatively recent, most notably energy efficienc y resource standards (EERS), which have thus far been adopted in 15 states and require utilities to achieve minimum energy efficiency savings targets over a lengthy period of time. 5 

### Similarly, several others states have adopted broader renewable portfolio standards (RPS) or alternative energy

standards under which en ergy efficiency is a qualifying resource. Many of these EERS policies and RPS policies with energy efficiency allowances have been enacted in states that previously had not aggressively pursued customer

- funded energy efficiency and have therefore required rapid development of the regulatory and administrative structures necessary to implement and oversee sizable energy efficiency program portfolios. Anothe r recent policy development in a

number of states, all of which have offered large

- scale energy efficiency programs for more than a decade, is the development of statutory or regulatory requirements that utilities acquire “all cost effective” energy efficiency. In these states, program adminis trators or regulatory staff may then conduct studies that estimate the long

- term, cost

- effective savings potential and then propose annual or multi

- year savings targets and budgets in order to capture this potential over a defined time period.

Other fac ets of the energy efficiency policy landscape are less recent. System benefit charges (SBC), which exist in 14 states and were typically established more than a decade ago as part of larger electric industry restructuring processes, serve to set an approxi mate floor on energy efficiency program spending via a non

- bypassable surcharge on customers’ utility bills. Integrated resource planning (IRP) also exists in many states, whereby utilities are required to plan for the long

- term needs of their customers b y considering and assessing a broad range of resource options, including energy efficiency resources. Depending upon the manner and extent to which utilities are required to assess energy efficiency options, the IRP may culminate in a

5 In this study, we define Energy Efficiency Resource or Portfolio Standards as requirements under statute or regulatory order that some or all utilities within a state (e.g., all utilities or investor

- owned utilities only) achieve specified minimum savings levels over a period greater than three years. States with shorter term DSM plans (i.e. one to three years) and/or multi

- year efficiency budgets approved by state regulators are separately listed. Note that other entities (e.g., ACEEE) that track the status of energy efficiency policies in various states may use slightly different criteria for defining an EERS than LBNL; thus their tallies of the number of states with such polic ies may

differ.

### 9 10

- to 20

- year plan with specified levels of energy efficiency resource acquisition. Finally, utilities in many states are required to regularly submit a demand

- side management (DSM) plan to their state regulator , proposing a specific portfolio of programs that meet cost

- eff ectiveness guidelines and other policy ob jectives, typically on a one

- to three

- year cycle.

Although IRP and DSM planning have both been utilized for more than 20 years, their application has expanded somewhat in recent years as a result of policy spill

- o ver or cross

border effects from other states within a given region. For example, Arkansas regulators developed a step

- by

- step energy efficiency program development template that has been cited as a policy influence in other southern states (e.g., Mississ ippi and Alabama). 6 

### Multi

- state utilities also are developing territory

- wide efficiency programs designed to meet one state’s mandates, in effect carrying that state’s energy saving policies de facto into neighboring states. 7 

### Lastly, the move by the Tennes see Valley Authority (TVA) 8 

### to set savings targets through its IRP, and to offer programs and encourage its member distributors to offer programs, is expected to spread the pursuit of energy efficiency across the seven states where it provides wholesale po wer.

Table 1 : Policy Drivers for Customer

- Funded Energy Efficiency Program Activity
Key Policy Drivers for Energy Efficiency Spending and Savings Applicable to Electric Efficiency Programs Applicable to Natural Gas Efficiency Programs Energy Efficiency Resource Standard (EERS) AZ, CA, CO, HI, IL, IN, MD, MI,
MN, MO, NM, NY, OH, PA, TX CA, CO, MI, MN, NY, IL

Energy efficiency eligibility under state

RPS HI, MI, NC, NV

, OH

Statutory requirement that utilities acquire

all cost

- effective energy efficiency CA, CT, MA, RI, VT, WA CA, CT, MA, RI, VT, WA

Systems benefit charges CA 9 CA, DC, ME, MT, NJ, NY,
RI,WI , CT, DC, MA, ME, MT, NH, NJ, NY, OH, OR, RI, VT, WI Integrated resource planning 34 States (primarily in the West and Southeast) and TVA 17 States (primarily in the West and Northeast)

Demand Side Management plan or multi

year energy efficiency budget 28 States 21 States (primarily in the Northeast and Midwest)



### In addition to the energy efficiency policy drivers summarized in Table 1, other broad market and policy dynamics may also play a critical role in shaping the trajectory of future spending and

6 In Arkansas, the process began with a collaborative among regulators, utilities and other stakeholders, then proceeded to “quick start” programs designed to test the viability of utility customer

- funded programs in that jurisdiction and b egin building program infrastructure. In the final step, regulators set modest but increasing savings

targets.

7 For example, Duke Energy Carolinas and Progress Energy Carolinas are subject to an RPS in North Carolina in which energy efficiency is an eligi ble resource, and both submitted a pro rata version of the same efficiency plan from North Carolina for the rest of their service territory in South Carolina. Likewise, West Virginia’s requirement that an American Electric Power subsidiary initiate effici ency programs resulted in submission of similar program plans in neighboring Virginia.

8 The Tennessee Valley Authority (TVA) is the largest U.S. public power utility and serves 155 distributors and 57 industrial customers in TN, KY, AL, MS, GA, NC and VA.

9 The systems benefit charge in California expired at the end of 2011, although legislative efforts are underway to

renew it.

### 10 savings from customer

- funded energy efficiency programs. We discuss several of these factors in Section 5.1, including the timing and pace of the economic recovery, the long

- term trend in natural gas prices, the stringency of future federal and state minimum efficiency standards for appliances and building codes, a nd the outcome of federal air emissions regulations.

2.2. Current and Historical Spending on Utility Customer

- Funded Energy Efficiency Programs

Over the latter half of the past decade, spending on electric and gas utility customer

- funded energy efficiency p rogram s (excluding load management ) 10



### Table 2 more than doubled, from roughly $2 billion in 2006 to $4.8 billion in 2010, consisting of $3.9 billion for electric energy efficiency programs and $0.8 billion for natural gas programs (CEE 2008; CEE 2012). Approved bu dgets

for 2011

– which may diverge from actual expenditures

– were significantly higher than 2010 spending, totaling $6.7 billion, consisting of roughly $5.6 billion for electric efficiency programs and $1.2 billion for gas efficiency programs (CEE 2012). With the steady increase in spending on utility customer

- funded efficiency programs in recent years, relative spending as a percentage of utility revenues has also risen, with electric program expenditures in 2010 equivalent to roughly 1.1% of total U.S. electric utility revenues in that year, while gas program expenditures were equivalent to roughly 0.7% of total U.S. gas distribution utility revenues. The geographical distribution of both electric and gas spending has spread over time, as

numerous state s with recently adopted policies have ramped up their efforts. That said, total spending on utility customer

- funded energy efficiency programs , in absolute dollar terms, still remains highly concentrated within a relatively small number of states (see ). 11



### In particular, the majority of funding for electric efficiency programs is concentrated in California, the Pacific Northwest (OR, WA), and t he Northeast (MA, NJ, NY, CT), all states with a long history of commitment to energy efficiency. O ther states, many located in the Midwest (e.g., OH, PA, IL, IN, and MI), are in the process of ramping up program spending, often driven by long

- term electricity savings targets. The top 10 states, in terms of absolute dollar expenditures account for abou t 70% of 2010 spending on electric energy efficiency programs. Program administrators in the leading states with the highest per capita energy efficiency spending typically offer a comprehensive portfolio of programs tailored to residential, commercial, an d industrial customers that utilize a variety of designs and intervention strategies (e.g., technical assistance to end users and trade allies, incentives to customers to buy down the cost of high

- efficiency equipment, and incentives to upstream manufactur ers and retailers to stock and distribute high

- efficiency products).

Gas efficiency programs are less widespread than electric programs, and thus funding is even more highly concentrated in a small number of states, where the top

- 10 states account for a lmost 80% of the national budget for gas efficiency programs. Specifically, gas efficiency spending is

10 Electric utility expenditures on load management programs in 2010 represented an additional $0.9 billion (CEE 2012).

11 Metrics based on total budget for energy efficiency tend to favor states with large populations. It is important to note that program administrators in several small states (e.g. VT, RI, and IA) have significant energy efficiency budgets, if metrics are b ased on efficiency spending per capita. In the U.S., the 10 leading states spend more than $25 per capita on utility customer

- funded electric efficiency programs, while the average spending on these programs among the 50 states is about $12 per capita.

### 11 concentrated in about a dozen states in various regions: NY, MA, and NJ in the Northeast; IL, MI, IA, MN and WI in the Midwest; and CA, OR and UT in the West. Most southern utilities have modest retail gas sales or function largely as distribution entities that convey “transportation gas”, 12 

### and they consequently spend little on gas efficiency programs. Nationally, gas efficiency program budgets are domi nated by residential and low income programs, together comprising 68% of total program expenditures in 2010 (CEE 2012), which is quite different from the program mix for electric efficiency programs. 13



### Table 2 . 2010 Expenditures fo r Utility Customer

- Funded Energy Efficiency (EE) Programs

Rank Combined Electric & Gas EE Spending ($M) Electric EE Spending ($M) Gas EE Spending ($M)

1 CA $1,139 CA $938 CA $201

2 NY $521 NY $482 NJ $126

3 NJ $317 MA $245 MA $72

4 MA $317 WA $218 MI $41

5 WA $247 NJ $191 IA $40

6 FL $176 FL $165 NY $39

7 OR $158 OR $135 MN $36

8 MN $144 TX $114 UT $36

9 CT $119 CT $108 OH $32

10 MI $116 MN $107 WA $29
Top 10

States $M $3,255 $2,702 $653 % of U.S. 68% 68% 78%

Remainder

of U.S. $M $1,531 $1,246 $185 % of U.S. 32% 32% 22%

Total U.S. $M $4,786 $3,948 $838 Source: CEE (2012), with several modifications

12 See approach section on modeling of gas programs for more details.

13 On a national basis, electric energy efficiency spending in 2010 was allocated among market sectors as follows: commercial and industrial (47%), residential (28%), low

- income residential (8%) and other programs or expenditure categories not directly attributable to a sector (16%).

### 12 

## 3. Analytical Approach

We developed low, medium, and high case projections of electric and natural gas efficiency program spending to 2025, as well as accompanying projections of electric program energy savings. 14 

### These projections are based on a state

- by

- state review of current policies, regulatory decisions, utility IRPs and DSM pl ans, and other key regulatory and planning documents, further supported through interviews with state PUC and utility staff and regional energy efficiency experts. The projections are intended to represent alternative pathways for the future

evolution of energy efficiency programs, given the current set of policies in place and the larger market and policy environment in which programs operate

. As explained further below, we took different approaches to developing projections for electric and gas energy ef ficiency program spending. These methodological differences reflect both that enabling efficiency policies are more prevalent among electric utilities compared to gas utilities, and that the level of development and experience with administering electric efficiency programs is much greater than for gas programs.

3.1. Electric Energy Efficiency Program Spending and Savings Projections

The projections of electric program spending and savings are based primarily on state

- specific assumptions about how effectively and aggressively current energy efficiency poli cies are implemented and about the impact of broader market conditions. The scenario assumptions are summarized by census region in Table 3

. T he projections for these states typically begin with assumptions about either future spending or savings (depending on the state and scenario), and then future spending or savings are deriv ed from the other based on assumptions about the cost of savings. For a group of seven “uncommitted” states that currently have little efficiency program activity and no established policy framework, we instead employ a standardized approach by which spen ding increases above current levels by a stipulated amount under each scenario, also described in Table 3. 15 

### Additional methodological details, including state

- by

- state descriptions of scenario definitions, are provided in A ppendix A

.

Although the scenari o definitions were developed on a state

- by

- state basis, with consideration of the specific policy and market context in that state, the low, medium, and high scenarios can be characterized in broad terms. At a conceptual level, the low scenario represents a less prominent role for energy efficiency as a resource in many states, as program spending remains at current levels or increases very modestly (or decreases in a few states) in subsequent years. The

medium scenario reflects a future in which states t hat historically have been leaders in energy efficiency continue down that path and in some cases expand the role of energy efficiency as a

14 In the context of this report, “spending” refers to the flow of money from the energy efficiency program administrator into the market, including all program administration costs but excluding performance incentives. To the extent possible, electric spending projections are intended to reflect “gross” savings, prior to accounting for free

riders or free

- drivers. This approach was taken in order to abstract fr om potential inconsistencies across states in methods for estimating net

- to

- gross ratios. However, the underlying data used to derive the cost of savings for some states were not explicit about whether savings are reported in “net” or “gross” terms; thus, some ambiguity exists in whether the spending projections for a number of states reflect net or gross savings. Gas efficiency program savings projections were not included for several reasons (e.g., relative paucity of mature, multi

- year gas efficiency

pr ograms from which to draw reliable data).

15 These seven uncommitted states include: AK, KS, LA, ND, NE, SD, and WV.

### 13 resource, while other states are fairly successful in ramping up their energy efficiency programs to meet legislativ e saving targets. Note that in the medium scenario, our estimates of future savings account for constraints that may limit the ability of program administrators to achieve savings targets

– e.g.
, ability for energy efficiency services infrastructure to ram p up quickly in early years and rate or spending caps that limit program spending increases in later years. The high scenario reflects a future in which many states establish a very prominent role for energy efficiency as a resource: states with EERS statutes are assumed to meet savings targe ts (and overcome constraints), states in each region are inclined to follow the example (and goals) established by leading states in that region, and those states that are currently “uncommitted” are

assumed to ado pt policies that lead to savings in 2025 of roughly the national average savings targets achieved by utilities currently.

### 14 Table 3 . Scenario Assumptions for Electric Energy Efficiency Projections

Region Scenario Representative As sumptions

South Low TX IOUs meet minimum EERS targets. TVA savings based on 2010 IRP “ Baseline Portfolio .” NC IOUs achieve only as much savings as can be applied towards their RPS. Utilities maintain spending at the level in the last year of recent DSM plans (in terms of percentage of revenues).

Medium TX IOUs maintain savings at current levels (0.2% of sales), exceeding EERS targets. FL IOUs

ramp up savings to 0. 3 % of sales. IRP savings targets are achieved (TVA, KY, GA). Otherwise

similar to low case.

High TX utilities ramp up savings to 0. 3 % of sales , offsetting 50% of demand growth

. FL IOUs ramp up to long

- term savings targets established by regulators (0.48% of sales in 2019) and to 0.75% by 2025. TVA and IOUs in GA, NC, and SC ramp up savings to roughly 1% of retail sales. MD utilities meet state EERS goals.

Midwest Low IL, IN, and OH utilities fall short of EERS targets (e.g., due to cost caps or opt

- out), but MI and MN utilities fully meet their more

- modest EERS targets. IA utilit ies maintain current spending levels, and WI spending is equal to current legislative cap of 1.2% of revenues.

Medium EERS targets are achieved in most cases, one exception being IL, where cost cap is eased but not to the extent required to meet ultimate targets. IA spending continues at the level in the last year of the most recent DSM plans

. WI spending rebounds to 1.7% of revenues , half way between current legislative cap and historical peak

. High All states reach savings of roughly 1.5% to 2% of retail sales, meeting or exceeding EERS targets by varying degrees.

West Low CA IOU savings are based on 90% of market potential, as estimated in Navigant (2012)

, which decline from current levels

. AZ and NM utilities achieve EERS targets. Many utilities in the Northwest achieve savings equal to 60% of NPCC’s 6 th Power Plan conservation targets, with remainder achieved through codes and standards. Utilities in other states achieve savings

based on most recent IRP or maintain constant savings based on the final year of their most recent DSM program plan, whichever is less.

Medium CA IOU savings are based on 110% of market potential in Navigant (2012) . CO utilities achieve EERS targets. In the Northwest, utility savings equal 75% of NPCC conservation targets, with remainder achieved through codes/standards. Utilities in most other states achieve savings based on most recent IRP or maintain constant savings based on the last year of their

most re cent DSM program plan, whichever is greater.

High CA IOU savings are based on 130% of market potential in Navigant (2012) . AZ IOUs meet EERS targets without reliance on retroactive credit for historical programs, and SRP achieves similar savings levels. CO is same as medium case. In the Northwest, utility savings equal 85% of NPCC conservation targets, with remainder achieved th rough codes/standards. Utilities in many other states achieve savings of roughly 1.5% of retail sales.

Northeast Low Spending levels in most states remain flat at roughly the statutory minimum (constituting a decline from current spending in some states) and/or continue at current funding levels. In NJ, spending declines by more than 50 % from current levels, as reliance shifts to revolving loan

funds

, with program spending equal to roughly 70% of the level s specified in the recent RFP for program adminis tration

.

Medium Spending in most states, as a percentage of revenues, remains flat at the level in the final year

of the most recent energy efficiency program plan. NY meets its EERS target for 2015, but spending thereafter reverts to the 2010

- 2015 average. PA spending rises to current cap. NJ programs shift to revolving loan funds, but spending levels declines less severely than Low

Case to reflect a more successful transition to financing model.

High New England IOUs capture all cost

- effective energy efficiency , up to a stipulated spending cap (10% of revenues for MA, RI, and VT; 6.5% for CT). NY meets its EERS target for 2015, and spending thereafter continues at 2015 levels. Savings in other states rises to 1

- 2% of retail

sales

.

Uncommitted Low Spending increases to 0.3% of revenues above current levels

Medium Spending increases to 0.5% of revenues above current levels High Spending increases to 0.8% of revenues above current levels

### 15 3.2. Gas Energy Efficiency Program Spending Projections

For the purpose of developing projections of utility customer funding of gas efficiency programs, we first grouped states into three categor ies: Tier I consists of the 13 states that comprise more than 80% of current national funding for gas efficiency programs, Tier II consists of another 15 states where 2010 spending on gas efficiency programs exceeded $0.50 per capita, and Tier III consists of the remaining 23 states. (See T echnical A ppendix A for the set of states included within each tier).

The process for developing scenario definitions for each state differed according to its tier (see

Table 4 ). For Tier I states, gas efficiency program spending projections are based on state

specific policies, gas DSM program plans, and regulatory decisions that set savings targets for gas utilit ies, and were further informed by interviews with program administrators, regulators and other experts in the field. For most Tier I states, the low and medium case spending projections track the most recent multi

- year gas DSM program plans to their termi nal year (typically 2012 to 2 014). In the low case, we assume that spending on residential gas efficiency programs in most Tier 1 states will decline to 25% of the level in the terminal year of the most recent DSM plan, while spending on commercial and in dustrial (C&I) programs will decline to roughly 80% of the level in the terminal year of the DSM plan. This decline in spending is due to the combination of sustained low natural gas prices, which reduce the cost effective ness of gas efficiency programs, and tightening federal minimum efficiency standards for gas furnaces, which reduce the savings for voluntary programs

– both of which are discussed further in Section 5. In the medium case, we assume a more modest drop

- off in residential program spending, typically to 50% of the level from the terminal year of the most recent gas DSM Plan, but that C&I program spending increases slightly, as program managers shift budgets towards markets with greater savings opportunities. In both the low and medium scena rio, we assume

that spending on gas low

- income programs remain s constant at the level from the last year of the DSM plan, as these programs meet broader policy objectives (e.g. equity, reductions in bill arrearages) and therefore are less susceptible to th e dynamics putting downward pressure on gas program spending for the other sectors. Finally, in the high case, we assume that many Tier I states achieve gas savings levels on par with the gas EERS targets recently adopted in several states (i.e., generall y 1.0

- 1.5% of total gas distribution utility retail sales).

Table 4 . Scenario Assumptions for Gas Energy Efficiency Projections

Category Scenario Representative Assumptions (specific assumptions vary by state)

Tier I States Low Assume new furnace equipment standards and moderate gas prices cause a reduction in residential program spending to 25% of the level from the most recent gas DSM plan, and to 80% for commercial & industrial programs. No change in low

- income program spendi ng.

Medium Assume new furnace equipment standards and moderate gas prices cause a reduction in residential program spending to 50% of the level from the most recent gas DSM plan, but a slight increase in C& I program spending. No change in low

- income program spending.

High States reach stipulated benchmarks for gas program savings ranging from 1.0% to 1.5% of total gas distribution utility sales.

Tier II States All Regional benchmark (average) based on low, medium and high scenarios in Tier I

Tier III States Low Spending remains at 2010 levels in absolute nominal dollar terms

Medium Spending remains at 2010 levels as a percentage of gas distribution utility revenues High Spending percentage increases above current levels by 0.25% of gas distribution utility revenue s

### 16 The 15 Tier II states have relatively aggressive spending levels on a per capita basis, but small populations and therefore small spending levels in absolute terms. Thus, for s implicity, the spending projections for these states were developed based on regional benchmark trajectories that were developed from the projections for Tier I states in the corresponding region. These regional benchmark trajectories were developed by av eraging the change in spending as a percentage of gas distribution utility revenues per year by region for the Tier I states in each census region. Those growth curves were then applied to the 2010 spending for each Tier II state. As an example, in the m edium case, spending for t he three Tier I states in the N ortheast (MA, NY, and NJ) is projected to increase by, on average, 0.6% of revenues; thus, the same 0.6% increase in spending as a percent of revenues was stipulated for the Tier II northeastern

stat es in the medium case. For further details, please refer to T echnical A ppendix A.

For the remaining 23 Tier III states that currently have little or no customer

- funded gas program activity, we assumed that future gas efficiency spending will, in the low case, remain at 2010 levels in absolute nominal dollar terms (thus declining as a percentage of gas distribution utility revenues, as revenues grow). In the medium case, we assume that program administrators maintain gas efficiency spending at their prese nt level, in terms of the percentage of utility revenues. The high case posits that program administrators will increase program spending to approximately 0.25% of revenues above 2010 levels by 2025.

### 17 

## 4. Results

In this section, we present our projections of spending on utility customer

- funded energy efficiency programs through 2025 . We first present total projected spending for electric and gas efficiency programs, combined, before turning to the projections for each fuel individually. We

als o present projections of electric energy savings associated with the three spending trajectories

f

or electric efficiency programs and consider the potential significance of these savings projections in relation to current expectation s about future load gro wth in the electric sector. 16


### The results presented throughout this section focus primarily on national and regional trends ; the corresponding state

- level projections are provided in Technical Appendix B.

4.1. Combined Electric and Gas Energy Efficiency Program Spending Projections

Total spending on electric and gas energy efficiency p rograms is expected to increase in all

scenarios across the study period. By 2025, we project that total electric and gas efficiency program spending, in nomina l dollars, will rise from $4.8 billion in 2010 to $ 6.5 billion in the low case, $ 9. 5 billion in the medium case, and $ 15.6 billion in the high case (see Figure 1 ). These projections correspond to compound growth rates of approximately 2 % per year (low case), 5 % per year (medium case), and 8 % per year (high case). Although the projected increase in spending in both the medium and high cases is sizable in absolute dollar terms, the associated growth rates in all cases are substantially lower than that witnessed over the past half

- decade,

when total electric and gas efficiency program rapidly accelerated at an average rate of 26% per year from 2006 to 2010 (Eldridge et al. 2008, CEE 2012). In the decade preceding this recent and rapid expansion of energy efficiency program activity, however, e lectric program spending grew by less than 5% per year from 1997 to 2 006, which is on par with the projected growth in spending under the medium case.

As discussed further in the following sections, projected growth rates for electric efficiency

program s pending are somewhat higher than for gas program spending in both the low and medium cases, with projected electric program spending growth of 2.3 % and 4.9 % per year in the low and medium cases, versus less than 1 .1 % and 3.8 % per year for gas programs. In the high case, however, gas efficiency spending grow s faster than electric spending (9.7% vs. 7.8%) . These differing trends reflect, in large part, the broader base of underlying policy support for, and historical experience with, electric efficiency prog rams, leading to stronger growth in the low and medium cases for electric programs, while leaving a large upside potential for growth in gas program spending under the high

- case conditions.

16 We do not present projections of natural gas program savings, as insufficient data exists to link the projected spending amounts to specif ic savings trajectories.

### 18 Source: 2010 spending based on CEE (2012) 

### Figure 1

. Projected Electric and Gas Energy Efficiency Program Spending



### 4.2. Electric Energy Efficiency Program Spending Projections

Spending on electric utility customer

- funded energy efficiency programs is expected to increase,

in nominal do llar terms, across all scenarios (see Table 5 ). Relative to 2010 spending of $3.9 billion (1.1% of total electric utility retail revenues), spending i s expected to more than double to $ 8. 1 billion by 2025 in the medium case ( 1.7 % of revenues). In comparison, spending in the

low case is projected to increase more slowly, reaching $ 5.5 billion by 2025 (1. 1 % of revenues). As described in Section 3, this slower pace of spending growth reflects a future scenario in which regulators and administrators “stay the course” at current funding levels, and many states with aggressive savings targets fall short of those goals. In the high case, spending more than

t riples from 2010 levels, reaching $ 12.2 billion (2. 7 % of revenues), due to the impact of “all cost effective efficiency” policies in leading states, successful achievement of EERS targets, and an increase in program savings in a number of states to the lev els projected for regional peers.

Importantly, the projected growth in electric program spending across all cases does not occur smoothly over the forecast period, but rather is “front

- loaded”, with much faster growth projected through 2015 ( Table 5 ). In the medium case, for example, spending grows by 1 1 % per year through 2015 but by only 2% per year from 2020 to 2025. This dynamic is partly due

to t he fact that, in many states, recent multi

- year DSM plans entail significant spending increases over the next several years, but no longer

- term targets or resource planning process currently exists to guide program activity beyond the time horizon of the D SM plan. The front

loaded spending projections also reflect the trajectory of EERS schedules, which typically reach their terminal targets by 2020 or sooner. From 2020 onward, we assume that spending growth in many states tapers off and grows roughly in proportion with projected revenues, reflecting both a lack of strong policy drivers for continued spending growth after 2020, as well as the assumption that savings potential within the 2020

- 2025 period will be diminished due to the

### 19 success of programs imp lemented over the prior decade and tightening federal efficiency

standards.

Table 5

. Projected Electric Energy Efficiency Spending

Scenario Projected Spending ($B, nominal) Projected Spending (% of Revenues) Average Annual Spending Growth

2015 2020 2025 2015 2020 2025 2010

- 2015 2015

- 2020 2020

- 2025

Low 4.8 5.2 5.5 1.2% 1.2% 1.1% 4% 2% 1%

Medium 6.5 7.4 8.1 1.7% 1.8% 1.7% 11% 3% 2%

High 8.3 10.8 12.2 2.2% 2.6% 2.7% 16% 5% 3%



### Not surprisingly, total U.S. electric program spending across all scenarios are driven, in large measure, by EERS policies, energy efficiency eligibility under RPS policies and legislative mandates requiring utilities to acquire all cost

- effective energy efficiency. In the medium case, for example, the 15 states with an electri c EERS, plus the additional five states with legislative

“all cost

- effective energy efficiency ” mandates (and no associated EERS) and the two states that qualify energy efficiency as an eligible resource under a renewable portfolio standard (again,

with out an associated EERS) together account for 72 % of the total projected electric efficiency program spending in the U.S. in 2025 (se e Figure 2 ) . The remaining spending is associated primarily with the additional 18 states that rely primarily on DSM planning and/or IRP (without

an associated EERS or “all cost

- effective energy efficiency ” mandate) to establish their electric efficiency budgets and targets, together comprising 28 % of total projected spending on electric efficiency programs.

Figure 2

. Policy Drivers for Projected Electric Energy Efficiency Spending in the Medium Case (2025)



### Projected trends in total U.S. spending are, to some extent, an overlay of distinct quasi

- regional trends (see Figure 3 ). In the medium scenario, overall growth of national efficiency program spending is driven chiefly by projected growth in the Midwest and South , which together represent 70% of projected total U .S. electric program spending growth over the 2010

- 2025 period. I n the Midwest, spending growth is associated with a contingent of populous states (IL, IN, MI, OH) that are currently ramping up to meet statutory EERS targets, while in the South,

### 20 increases in efficiency program spending are associated with a collection of relatively modest EERS policies and nascent IRP/DSM planning processes in states with a large base of energy consumption (TX, FL, NC, MD, KY). The same underlying policy drivers propel sp ending growth in these two regions in the low and high scenarios as well, though to differing degrees.

In the West and Northeast

– the traditional bastions of energy efficiency activity

– electric program spending is also projected to increase in the me dium case, though by lesser amounts than the other two regions, reflecting the more mature state of those market s

. In the Northeast, efficiency program spending is projected to increase under all three scenarios , where d ifferences in spending levels betwe en the medium and high cases are largely driven by assumptions about how utility program administrators and state regulators translate statutes requir ing acquisition of

all cost

- effective efficiency into multi

- year savings goals. For the West , the regiona l trends are dominated by California, where electric program spending in both the medium and low cases is projected to decline over the long term , as saturation within key end

- use markets occurs and as the state leans more heavily on other energy efficienc y policies (Navigant 2012). In the medium case, those declines are offset by spending growth in other western states, leading to net spending growth for the region as a whole, while in the low case, total electric program spending in the West is projected to decline slightly.

Figure 3

. Projected Electric Energy Efficiency Spending by Census Region



### The differing regional trends imply a continued shifting of the energy efficiency map over the coming decade and beyond (see Figure 4 ). While states in the West and Northeast accounted for more than 70% of efficiency program spending in 2010, that percentage declines to just over 50% by 2025 in the medium case, with the South and Midwest splitting the remaining spending about evenly. Notwithstanding the greater regional balance in absolute dollar spending on electric efficiency programs, the South is still projected to lag well behind other regions in terms

of relative spending levels as a percentage of electric utility revenues. As shown in Figure 5

, spending as a perc entage of revenues in the medium case is projected to rise from 1.8% and

2.8 % in the Northeast over the 2010 to 2025 timeframe, and declin e slightly from 2.4% to 2.1% in the West. In the Midwest, efficiency spending is expected to increase quite dramatica lly

### 21 ( from 0.7% to 2. 2 % of revenues )

. However, i n the South, while spending as a percentage of total electric utility revenues ris es from 0.4% of revenues in 2010 to 0.9 % in 2025

, this is one

third to one

- half the spending levels projected in the other three regions

.

Figure 4

. Regional Distribution of Electric Energy Efficiency Program Spending (Medium Case) Figure 5

. Electric Energy Efficiency Program Spending as a Percent of Utility Revenues (Medium Case)



### 4.3. Electric Energy Efficiency Program Savings Projections

In 20 10 , electric energy efficiency programs in the U.S. achieved incremental energy savings of

18.4 TWh , equivalent to 0.4 9 % of electric utility retail sales nationally ( Foster et al. 201 2 ). 17 

### In comparison, leading states, where program administrators typically have a decade or more of experience in delivering energy efficiency programs, have achieved annual savings of more than

1.0% of retail sales (e.g., CA, CT, MA, OR, VT, NV, HI, RI, and MN), and a sizeable contingent of other states has consistently achieved savings in excess of 0.50% of retail sales.

As explained previously in Section 3 (and in greater detail in A ppendix A ), the electric efficiency program spending projections are link ed to a corresponding set of savings projections ( see Table 6 and Figure 6 ), where in some cases savings estimates are derived from spending,

and in other cases vice

- versa. 18

17 Note that energy savings number cited here represents first

- year savings from programs implemented in 2009, and should not be confused or compared with other estimates (e.g., CEE 201 2 ) that refer to the combined impact in any given year from both programs implemented in that year and from programs implemented in prior years. 

### In the medium case, incremental annual energy savings from electric efficiency programs are projected to increase to 2 8 .8 TWh and 0.7 6 % of retail sales in 2025. This represents roughly a 50% increase over the impact of electric efficiency programs in 2010. As was the case for the spending projection, much of the projected increase in annual

incremen tal savings is concentrated in the initial years of the forecast period, as the projection

18 To the extent possible, spending projections are intended to reflect “gross” savings (i.e., prior to accounting for

free

- riders or spillover effects). T his approach was taken in order to abstract from potential inconsistencies across states in methods for estimating net

- to

- gross ratios.

### 22 follows the trajectory of the most recent batch of utility energy efficiency plans (which typically

terminate in the 2012

- 2014 period ) and EERS targets (which typically reach their final percentage targets by 2020 or sooner). 19 

### In the low case, incremental annual savings rise moderately by 2015 b efore largely flattening out over the remainder of the forecast period, reaching 20 .6 TWh or 0.5 3 % of retail sales by 2025. In the high case, annual incremental savings rise to 41.6 TWh by 2025, more than double the level achieved in 2010, equivalent to

1

. 13 % of total electric utility retail sales. Thus, in effect, the high case represents a scenario in

which th e national average savings rise to the level currently being attained by the top tier of

states. In both the medium case and the high case, savin gs levels nationally are within the bounds of most studies of “achievable” energy efficiency potential. This suggests, among other things, that the level of savings projected in these two cases could potentially be reached through accelerated deployment o f current technologies, without significant reliance on new efficiency technologies.

Table 6

. Projected Incremental Annual Electricity Savings from Utility Customer

- Funded Programs (TWh)

Scenario 2010 2015 2020 2025

Low 18.4 20.4 21.1 20.6

Medium 26.6 28.6 28.8
High 33.1 39.8 41.6



### Figure 6

. Projected Electricity Savings from Utility Customer

- Funded Efficiency Programs



### To place these savings projections in perspective, the Energy Information Administration (EIA)’s most recent reference case forecast (EIA 2012) projects that total U.S. retail electricity sales will grow at a compound annual growth rate (CAGR) of 0.58% ove r the 2010 to 2025 period, which is substantially lower than the average U.S. load growth of 1.6% per year over the past two decades. The EIA’s modeling framework (the National Energy Modeling System, or

19 For many states, our analysis assumes constant savings percentages fr om 2020 to 2025; those assumptions are

reflected in the national totals in Figure 7, which similarly shows a flat or slight decline i n savings percentages from

2020 to 2025.

### 23 NEMS) does not explicitly account for the impacts o f future utility customer

- supported efficiency programs; however, the electricity sales forecast generated by NEMS implicitly

assumes that historical trends in utility customer

- funded efficiency programs will continue over the forecast period. For the per iod 2000 to 2 010, we estimate that utility customer

- funded energy efficiency programs nationally achieved incremental savings of roughly 0.18% per year,

on average. 20 

### Thus, if one were to assume that the EIA reference case forecast implicitly

assumes that savings from customer

- funded electric efficiency programs continue to accrue at

this historic rate

, then a hypothetical reference case forecast with no future customer

- funded energy efficiency activity would correspond to a CAGR of 0.76% (i.e., 0.58% plus 0.18%).

Our medium case projection corresponds to average annual incremental electricity savings of

0. 72 % of retail electric sales per year between 2010 and 2025. This, in turn, implies that if electric utility customer

- funded efficiency programs achieve savings at the level projected under our medium case, they would reduce growth in U.S. retail electricity sales to just 0. 04 % per year through 2025 (i.e., 0. 76 % annual growth with no future efficiency program activity minus projected annual incremental savings of 0.7 2 % of retail sales per year under the median case),

offset ting almost all projected load growth under EIA’s 2012 reference case forecast. 21



### Follow ing the same logic, our low case and high case savings projections would offset roughly 70% and 120% of load growth, respectively, yield ing average annual growth rates for retail electricity sales of 0.2 1 % and

- 0.1 8 % from 2010 to 2 025. To be sure, these benchmarks should

be considered no more than a “back

- of

- the

- envelope” estimate of the impact of projected

customer

- funded efficiency program savings on load growth in the United States. Nevertheless,

t hey suggest that rising savings leve ls, in combination with modest underlying drivers for load growth, could potentially lead to flat, or even negative, load growth over the next 10 to 15 years.

4.4. Gas Energy Efficiency Program Spending Projections

Our analysis suggests a very different set of trajectories for gas efficiency programs compared to electric efficiency programs (see Figure 7 and Figure 8 ). While the low and medium scenarios both show gas efficiency spending i ncreasing from 2010 to 2015, associated primarily with

increases that have already been planned or approved in recent multi

- year gas DSM plans, we currently see little evidence to expect significant further spending growth at a national level beyond 2015. Thus, in the low case, spending on gas efficiency programs recedes from its

elevated level in 2015 to below $ 1 b illion in 2025 (0.5% of revenues), which is slightly higher

than 2010 spending i n absolute nominal dollar terms but lower as a percentage of ga s

20 EIA’s National Energy Modeling System (NEMS) is calibrated to historical data on end

- use stock efficiency and shipments, and th e customer adoption simulation assumes, in essence, that consumers will continue purchasing equipment that exceeds minimum efficiency standards to the same extent as has historically occurred. This estimate of incremental savings from efficiency programs i s based on ACEEE data for national electric efficiency program savings for 2006

- 2010, and savings for 2000

- 2005 are estimated based electric efficiency program spending for those years.

21 One must interpret this finding with a certain degree of caution giv en that: (a) EIA’s 2012 reference case load forecast projects much slower growth in electricity demand and in economic activity than has historically occurred, and (b) uncertainty regarding the precise extent to which EIA’s load forecast accounts for the i mpacts of future electric utility customer

- funded efficiency programs. Our results suggest that additional analysis of the amount of future energy efficiency program savings that is implicit in EIA’s reference case forecast and more consistent accounting of free rider and spillover effects is warranted, although beyond the scope of this study.

### 24 distribution utility revenues. In the medium case, spending remains roughly flat at projected 2015 levels, reaching almost $1. 5 billion in 2025, equivalent to 0. 8 % of revenues, a slight

increase over the 2010 level . As discussed in Section 3 , the low and medium case projections are driven largely by scheduled increases in federal minimum efficiency standards for furnaces, with differing assumptions between the low and medium case s about the extent of the impact on residential gas efficiency spending an d the degree to which declines in residential program spending may be offset by increased spending on programs that target commercial/industrial customers. In the high case, however, where gas program savings in the leading states are

assumed rise to leve ls on par with current leading states for electric efficiency, spending on gas programs roughly triples from 2010 levels, reaching $3. 3 billion in 2025 (1. 8 % of revenues).

Figure 7

. Projected Gas Energy Efficiency Program Spendi ng

Figure 8

. Projected Gas Energy Efficiency Program Spending as a Percentage of Gas Distribution Utility Revenues

### 25 

## 5. Discussion of Key Issues and Uncertainties

The preceding set of projections suggest a wide range of potential trajectories for utility

customer

- funded energy efficiency program spending and savings in the United States

– even without considering the possibility of fundamentally new policy developme nts. In this section, we identify some of the significant issues and uncertainties that may influence the spending course and impact of these programs over the next 10 to 15 years and which we attempted to

account for

– either directly or indirectly

– wit hin the projections. These interrelated issues and

uncertainties include both external factors , such as the broader policy and market context within which utility customer

- funded programs operate, and internal factors related to the implementation and regu latory oversight of these programs.

5.1. Broader Market and Policy Context

Utility customer

- funded energy efficiency programs and their enabling policies function within a broader context, influenced by a variety of market forces and conditions, as well as by interactions with other policies. We briefly highlight four key elements of this broader market and policy context that may be particularly critical to the future trajectory of customer

- funded efficiency programs: the state of the economy, natural gas pr ices, federal minimum efficiency standards, and environmental regulations affecting the electric power sector. 22



### The Economy

The timing and extent of the economic recovery m ay complicate and restrain efforts to scale

- up

ener g y efficiency spending and savings over the near to medium term , for several reasons. First, utility customer

- funded energy efficiency progra m s t ypically requires custo m ers to pay a portion of the capital outlay for energy efficiency m easure s; as households and businesses struggle t o

m anage their day

- to

- day expenses, and as declining home values reduce the equity available for financing efficiency improvements, m any customers may be reluctant to m ake new invest m ents,
even those with short p ayback periods. As a result, program partic ipation may be suppressed , or program costs may rise if program administrators are required to increase financial incentives or expend greater sums on marketing efforts. Second, a stagnant economy is likely to reduce the

rate of stock turnover and new hous i ng starts, thereby reduci n g the a m ount of energy savings that could be captur e d through utility customer

- funded progra m s targeting these m arket opportunities. Third, a slow economy may indirectly constrain energy efficiency program

efforts in at leas t three ways: h eighten ed sensitivity to potential near

- term rate impacts associated with efficiency program spending, 23

22 Other aspects of the broader market and policy context that may impact future customer

- funded energy efficiency program activity include the development of altern ative utility business models, increasing capital costs for conventional generation technologies, greenhouse gas mitigation policies, and the lasting effects of ARRA

funding on energy efficiency program delivery infrastructure and energy efficiency potenti al. 

### increas ed risk that policymakers will re

- direct

23 Lawmakers in Wisconsin and regulators in Florida, for example, have both cited rate impacts in repealing or lowering energy savings targets.

### 26 dedicated funding for energy efficiency to shore

- up state budgets 24 

### or other non

- efficienc y

purposes , and slowed load growth, thereby reducing the avoided capacity costs and cost
effective ness of energy efficiency programs.

Moderate Natural Gas Prices

As of April 2012, natural gas was trading at wellhead prices of less than $2 per million British thermal units ( MM B tu ), the lowest level in 10 years and nearing a record low. Although natural gas prices are projected to rise over the next 20 years (EIA 2012), they are nevertheless expected to remain lower, in real terms, than the prices that characterized most of the past decade, when most state energy savings targets were set. 25 

### For electric and gas energy efficiency programs, lower gas prices translate into reduced program benefits, which in turn constrains total efficiency spending and flex ibility in program design as benefit

- cost ratios decrease. More aggressive efficiency portfolios and comprehensive, multi

- measure programs may be especially at risk, because costlier measures will result in longer payback periods for customers and will

not be as cost effective from a total resource cost perspective. The effects of moderate gas prices will be especially pronounced for natural gas efficiency programs because lower gas commodity

costs means lower avoided energy costs to gas utilities , which af fects program cost

effectiveness . Lower gas prices also mean that customers will have incentive to increase consumption or convert to gas heating from other fuels and will have less direct financial incentive to invest in energy efficiency

.

State and Fede ral End

- Use Codes and Standards

In recent years, state adoptions of building energy codes have increased, and federal minimum efficiency standards for appliances and end

- use equipment have been tightened . These policies affect utility customer

- funded pro grams by essentially raising the baseline against which savings are measured, thereby influencing both the size of the remaining potential that can be harvested through those programs and the mix of technologies targeted. Two specific federal efficiency

st andards that are planned to go into effect over the near

- term

– for lighting in 2012 to 2 014, then again in 2020, and for non

- weatherized natural gas furnaces in 2013

– may have potentially significant impacts on customer

- funded efficiency programs. The i mpact of the federal lighting standards is somewhat less certain, because program administrators have other lighting technologies that are likely to remain cost effective after the standards come into effect. Gas program administrators, however, may have fewer options. Starting in 2013, the new furnace

standards would raise the minimum heat

- to

- fuel efficiency of furnaces from 78% to 90%

AFUE 26

24 Actual diversions of SBC funds to state general funds have been considered by state legislators in a numbe r of states and have actually occurred in several states. For example, nearly a third of program revenues in Connecticut were redirected to the state general fund and California lawmakers considered diverting $161 million in gas SBC funds to the state gen eral fund but a state court denied the transfer. 

### in northern states (generally the states with the nation’s most substantial spending and

25 The trajectory for gas prices, and the implications for spending and performance of gas energy efficiency programs, could change in response to, for example, tighter regulation of hydraulic fracturing , an acceleration in the expected increase of demand among gas

- fired generators or a rapid increase in export s of liquified natural gas.

26 Annual fuel use efficiency (AFUE) is an equipment rating intended to measure the season

- long, average efficiency of equi pment as a ratio of thermal energy output to fuel energy input. An AFUE of 78%, the current U.S. standard for furnaces, represents an average of 78 Btus of heat for every 100 Btus of energy in the combusted fuel.

### 27 savings targets). Programs can continue to provide incentives for higher efficiency gas furnaces, but with a technological efficiency limit of about 98% AFUE, the incremental savings will be lower, and residential gas furnace programs are therefore less likely to continue as the ma instay of gas efficiency program portfolios.

Emissions Regulations

Proposed or final air emissions regulations that are being considered or adopted by state and federal environmental agencies 27



### - in combination with low

- priced, abundant gas

– have become

i mportant driver s for utility customer

- funded energy efficiency programs, as part of utilities’

multi

- faceted strategies for managing the retirement of older coal

- fired generators. 28 

### For example, many utility resource plans have discussed the potential role of demand

- side resources as part of a strategy for complying with emissions requirements (e.g., Tennessee Valley Authority), as a prerequisite for utility customer funding of low carbon replacement generation (American Electric Power in West Virginia, Flo rida Power & Light in Florida), or as a means of deferring retirement and replacement decisions (Duke Energy Carolinas). The ultimate import of these regulations for future energy efficiency program budgets, however, depends on several

factors . These fact ors include : the timing and stringency of the final rules; the price of natural

gas ( as gas

- fired generation is expected to offset the majority of the retired coal

- fired generation ) ; the capital cost profile of clean energy generation alternatives (e.g., renewable energy, nuclear power , coal with carbon capture and sequestration ); the regulatory and business models in place that govern the balance and relative attractiveness of supply

- and demand

- side investments; and the degree to which utilities and util ity regulators integrate state and tribal Clean Air Act implementation plans with utility resource plans.

5.2. Energy Efficiency Program Implementation and Regulatory Oversight

T here are also a variety of other critical issues and uncertainties specific to t he regulatory and administrative institutions within which utility customer

- funded efficiency programs operate and that may strongly influence the spending and savings trajectories of those programs. Here, we highlight several: general aversion to rate im pacts, challenges associated with developing innovative program designs to reach deeper and broader savings, and the limited ability in some states to extend gas efficiency programs to transportation gas customers.

Aversion to Rate Impacts

In m ost stat e s , utilities t y p i cally expense program costs for e n ergy efficie n cy as they are incurred. As a result, energy efficiency program cost recovery is relatively front

- loaded

co m p ared to cost recovery for m ost utility supply

- side resource alternative s

. As a result, the rate

27 Efforts to limit these emissions span multiple sets of regulations

– for air toxics, for nitrogen and sulfur oxides, for greenhouse gases, for managing coal ash and for limiting once

- through cooling for generators

– and each of these regulations has its o wn timeline and likelihood of coming into effect.

28 Coal

- fired generators are the nation’s largest single source of acid gases, carbon dioxide and air toxics such as mercury. The oldest coal

- fired generators in the U.S., generally those of 1960s vintage or earlier would be most affected by these environmental regulations. See CRS (2011), Brattle Group (2010) and Bipartisan Policy Commission (2011) for a more detailed discussion of these regulations, their timing and the projected impacts on the

electric power industry.

### 28 i m pac t s from energy efficiency tend to occur sooner (even if t h e rate i m pacts are less o ver the long

- ter m , and even if avera g e utility b i lls are reduced co m pared to supply

- side alternatives).

The short

- term rate i m pacts associated wi th attai n i n g very aggressive levels of savings (or even relatively modest levels of savings in states that are higher than has historically occurred) could pose a political challenge for state regulators, particularly in states that have seen significant r ate hikes in recent years or whose rates are well

- ab o ve natio n al averages. Across all states, these challenges are further heightened during periods of economic hardship. Concerns about rate impacts from energy efficiency programs have been institutional ized in a number of states, either through explicit caps on spending or rate impacts, or by the application of the ratepayer impact measure (RIM) test. 29 

### Meeting aggressive EERS targets in some states will likely require exceeding these caps or otherwise justifying rate increases, which may be feasible only in a robust, growing economy.

Developing Innovative P rogram Designs to Reach Deeper and Broader Sav ings

A nu m ber of states have established aggressive e n ergy efficie n cy savings g oals for future years that are well beyond current experience and pract i ce in m ost leading states (e.g., annual incremental electric savings on the order of 1.5% to 2% or m ore of r etail sales). The challenge

f

or these program ad m i nistrators will b e to design and i m ple m ent progra m s that can achieve both deeper savings, on average, at custo m er facilities and h ave a broader reach in ter m s of

m arket penetration ov e r a sustained period of ti m e. Service providers will have to achieve savings lev e ls of 25

- 40% of existing us a ge at cu s t omer f acilities co m pared to current practice in utility customer

- funded progra m s, which is typ i cally in the 5

- 20% range. Achieving higher

m arket penetrat i on rates will require progra m s to target a nd reach traditionally under

- served

m arkets (e.g., s m all commercial, mult i

- fa m ily, rental housing, moderate income households,

non

- owner occupied commercial facilities) in far greater nu m bers t h an current practice (MEEAC 2009). We are also likely to see increased attention to integrated d e livery of electric and gas efficiency progra m s as well as coordinated delivery of energy efficiency, on

- site

renewables and co m bined heat and power, in order to reduce tra nsaction costs and provide

custo m ers with tailored, custo m ized service offerings.

Extending Programs to Transportation Gas Customers

In many states, energy savings in the large commercial and industrial markets are, in effect, beyond the reach of program administrators. This is especially true for gas efficiency programs, as large commercial and industrial customers often purchase natural gas on the competitive market through alternative retailers, and may not pay into or be able to participate in gas uti lity

customer

- funded energy efficiency programs. 30

29 For example, Michigan and Illinois have spending caps in their EERS legislation. In Wisconsin, lawmakers rescinded regulatory discretion over program spending and capped spending at about half the levels anticipated to meet original s avings targets. In Florida, the PSC continues to rely heavily on the RIM test to screen energy efficiency programs; the RIM test highlights potential rate impacts on non

- participants rather than reductions in average customer bills from cost

- effective effi ciency investments. 

### This “transportation gas” accounts for 46% of total U.S. gas sales and 79% of all commercial and industrial sales. The ability for many states

30 Related, large electricity customers in many utility service areas may either “opt out” of paying charges for energy efficiency programs or direct most or all of their share of those charges into their own, “self

- direct” energy

effi ciency investments.

### 29 to significantly increase gas efficiency program savings and spending may therefore hinge, to a large degree, on whether mechanisms can be developed (e.g., non

- bypassable charges for program funding) to bring these customers and savings opportunities into the program fold.

### 30 

## 6. Conclusions

Energy efficiency programs f unded by utility customers are poised for dramatic growth over the

course of the next 10 to 15 years, especially in the Midwest and South

– w ith a contingent of populous Midwest states ramping up to meet statutory EERS targets, and in the South, the

expect ation that a collection of relatively modest EERS policies and nascent IRP/DSM planning processes in states with a large base of energy consumption will push spending upward from currently low levels. As a result, program spending is expected to become mo re evenly distributed nationwide by 2025.

Program spending is projected to roughly double to $ 9

. 5 billion in 2025 and could reach $1 5

. 6

billion under aggressive assumptions about the policy support, implementation and effectiveness of current policies

. Program administrators in many states are projected to achieve annual electricity savings of between 1.5% and 2%, surpassing the achievements of most leading states today.

Given forecasts for a slow economy recovery and modest load growth, the projecte d growth in electricity program spending and savings under our medium case scenario would offset most aggregate annual U.S. electric load growth through 2025, based on the load forecast in EIA’s

most recent reference case (and given assumptions about the e xtent to which EIA’s forecast captures the impact of future efficiency programs)

.

However, program administrators and state regulators face emerging challenges and

uncertainties. The combined effects of economic torpor, moderate gas prices, and tighten ing energy codes and minimum efficiency standards pose challenges for continued growth in electric and, especially, gas efficiency programs. The degree to which leading states and a new vanguard of fast

- rising states can overcome these challenges and offs et reduced efforts elsewhere is likely to govern the longer term path for national

- level spending and savings on efficiency programs

.

### 31 

## 7. Reference s

Barbose, G., C. Goldman, and J. Schlegel. 2009. The Shifting Landscape of Ratepayer

- Funded Energy Efficiency in the U.S. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL

2258E. http://eetd.lbl.gov/ea/emp/reports/lbnl

- 2258e.pdf

Bip artisan Policy Center. 2011. Environmental Regulation and Electric System Reliability. http://www.bipartisanpolicy.org/library/report/envi ronmental

- regulation

- and

- electric

- system

reliability

Boonekamp, Piet G. M. 2011. How much will the energy service directive contribute to the 20% EU energy savings goal? Energy Efficiency 4 :85

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, DOI: 10.1007/s12053

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Brattle Group. 2010. Potential Coal Plant Retirements Under Emerging Environmental Regulations. http://www.brattle.com/_documents/uploadlibrary/upload898.pdf

Congressional Researc h Service. 2011. EPA’s Regulation of Coal

- Fired Power: Is a `Train Wreck’ Coming? CRS

- R41914

Consortium for Energy Efficiency (CEE). 2012. State of the Efficiency Program Industry: Budgets, Expenditures and Impacts 2011. http://www.cee1.org/files/2011%20CEE%20Annual%20Industry%20Report.pdf

Consortium for Energy Efficiency (CEE). 2008. Energy Efficiency Programs: A $3.7Billion U.S. and Canadian Industry. 2007 Report. ( http://www.cee1.org/ee

pe/2007/2007EEPReport.pdf )

Cooper, A. and L. Wood. 2012. Summary of Ratepayer

- Funded Electric Efficiency Impacts, Budgets, and Expenditures (2010

- 2011). Washington, D.C.: Institute for Electric Efficiency. http://www.edisonfoundation.net/iee/Documents/IEE_CEE2011_FINAL_update.pdf

Dixon, R.K., E. McGowan, G. Onysko,, R.M. Scheer.US energy conservation and efficiency policies: Challenges and opportunities, Energy Policy, Volume 38, Issue 11, November 2010, Pages 6398

- 6408, ISSN 0301

- 4215, 10.1016/j.enpol.2010.01.038. ( http://www.sciencedirect.com/science/article/pii/S0301421510000637 )

Eldridge, M., M. Neubauer, D

. York, S

. Vaidyanathan, A

. Chittum, and S

. Nadel. 2008. The 2008 State Energy Efficiency Scorecard. Washington, D.C.: American C ouncil for an Energy Efficient Economy. http://www.aceee.org/pubs/e086.htm

.

Energy Information Administration (EIA). 2012. Annual Energy Outlook, 2012. (Accessed June 2012)

EPRI. 2009. Assessment of Achie vable Potential from Energy Efficiency and Demand Response Programs in the U.S.: (2010

– 2030) . EPRI, Palo Alto, CA: 2009. 1016987.

### 32 Foster, B., A. Chittum, S. Hayes, M. Neubauer, S. Nowak, S. Vaidyanathan, K. Farley, K. Schultz, and T. Sullivan. 2012. The 2012 State Energy Efficiency Scorecard . ACEEE Research Report E C12

. http://aceee.org/research

- report/e12c

Geller, H., P. Harrington, M.D. Levine, A.H. Rosenfeld and S. Tanishima. 2006. Policies for

In creasing Energy Efficiency: Thirty Years of Experience in OECD Countries. Energy Policy 34(5): 556

– 573

.

Gillingham, K., R. Newell, and K. Palmer. June 2004, revised September 2004. Retrospective

Examination of Demand

- Side Energy Efficiency Policies. RFF DP 04

- 19 REV

. http://www.rff.org/RFF/Documents/RFF

- DP

- 04

- 19REV.pdf

.

Goldman, C., E. Stuart, I. Hoffman, M. Fuller and M. Billingsley. 2011. Interactions Between Energy Efficiency Progr ams Funded Under the Recovery Act and Utility Customer

- Funded Energy Efficiency Programs. Lawrence Berkeley National Laboratory, LBNL

- 4322E. http://eetd.lbl.gov/EA/EMP/reports/lbnl

- 4322e.pdf

.

Hopper, Nicole, Galen Barbose, Charles Goldman, and Jeff Schlegel. 2008. “Energy Efficiency as a Preferred Resource: Evidence from Utility Resource Plans in the Western United States and

Canada.” Energy Efficiency , 14(2):126

- 34, October 2008. http://www.springerlink.com/content/6k46076226625763

.

MEEAC (Massachusetts Energy Efficiency Advisory Council). 2009. The Context for Energy Efficiency S avings: E l ectric S a vings Update, Elec tric Net Benefits and Gas Saving s

. Presentation by Council Consultants at MEEAC m eeting. March 24, 2009. http://www

. m a

eeac.or g

.

Nadel, S.. 1992. Utility Demand

- Side Management Experience and Potential

- A Critical

Review. Annual Review of Energy and the Environment 17:507

- 535. http://www.aceee.org/sites/default/files/publications/researchreports/U921.pdf

Navigant Consulting Inc. 2012. Analysis To Update Energy Efficiency Potential, Goals, And Targets For 2013 And Beyond. Prepared for the California Public Utilities Commission. http://www.cpuc.ca.gov/NR/rdonlyres/5A1B455F

- CC46

- 4B8D

- A1AF

34FAAF93095A/0/2011IOUServiceTerritoryEEPotentialStu dyFinalReport.pdf

Nowak, S., M. Kushler, M. Sciortino, D. York, and P. Witte. 2011. Energy Efficiency resource Standards: State and Utility Strategies for Higher Energy Svaings. ACEEE Research Report

U113. http://www.aceee.org/research

- report/u113

Sciortino, M., M. Neubauer, S. Vaidyanathan, A. Chittum, S. Hayes, S. Nowak, and M. Molina. 2011. The 2011 State Energy Efficiency Scorecard. ACEEE Research Report E115. http://www.aceee.org/research

- report/e115

York, D., P. Witte, K. Friedrich, M. Kushler. 2012. A National Review of Natural Gas Energy Efficiency Programs. ACEEE Report Number U121. ( http://www.aceee.org/sites/default/files/publications/researchreports/u121.pdf )

### 33 

## Technical Appendix A : Methodology and Assu m ptions Used to Develop Energy Efficiency Spending and Savings Projection s

This technical appendix describes the m ethods and assumptions used to develop projections of energy efficiency program spending by U.S. electric and natural gas customers and savings (electric only) through 2025.

A1. Electric Energy Efficiency Spending & Savings Projecti o ns

Low, m edium, and high projections of future electric energy efficiency program savings and spending were developed on a state

- by

- state bas i s. Although m any of the specific assu m ptions and the approach to defining scenarios varied by st ate, the basic m ethodology used in all states

consisted of several com m on co m ponents, including:

**•** Developing projections of retail electricity sales and revenues fr o m retail electricity sales;

**•** Defining low, m ediu m , and high scenarios of future utility customer

- funded energy ; efficiency program savings and spending for t h e electricity sector; and

**•** Estimating the a m ount of s pending required to achieve different levels of savings.

Each of these ele m ents is described further belo w

.

A1.1 Retail Sales and Revenue Projections

Projections of annual retail electricity sales and revenues were used as an input to develop energy efficiency program savings and spending projections; for example, in those states that establish EERS targets as a percent of retail sa les, savings projections were calculated based on projected retail sales. Sales and revenue projections were also used to develop m etrics that allow

for co m p a rison of spending and savings lev e ls across s t ates of differing sizes (e.g., savings as a

percent of retail sales and spending as a percent of revenues).

Baseline Retail Sales and Revenue P rojections

An initial set of baseli n e retail s ales and retail p rice projecti o ns for each s t ate was d e v eloped by applying annual growth rate projections from the E nergy Infor m ation Ad m inistration (EIA)’s 2012 Annual Energy Out l ook (AEO2012) reference case forecast to actual 2010 retail sales and price d ata for each state, as reported on EIA’s For m

- 860. The electricity retail sales and price projections in AEO2012 a r e specified at the E l ectricity Market Module (EMM) level, the regions

u s ed in EIA’s National Energy Modeling S ystem (NEMS) ; the state

- level retail electricity sales and average retail electricity price projections were developed by applying the EMM

- level

g r ow th rates to historical retail sales and revenues for each state in the res p ective re g i on. Table

A

- 1 summarizes the annual average growth rates (2011 to 2 025) of ret a il electricity sales and electricity prices in each EMM region from the AEO2012 reference case forecast. R e venue proj e ctions were calc u lated b y m ultipl y i n g proje c ted retail electri c ity prices by projected retail electricity s a les.

### 34 Table A

- 1

. AEO Projected Growth Rates in Retail Electricity Sales and Pri ces

Electricity Market Model (EMM ) Re g ion S tates AEO2012 Average Annual

Gr o w th Rate (2011

- 20 2 5)

Retail

E lec t r i ci t y Sales Retail E lec t r i ci t y Prices

Pr i ce (nominal) Electric Reliability Council of Texas TX 0.7% 2.0% Florida Reliability Coordinating Council FL 0.9% 1.6% Midwest Reliability Council / East WI 0.1% 1.5% Midwest Reliability Council / West IA, MN, ND, NE, SD 0.2% 1.6%

Northeast Power Coordinating Council / Northeast CT, MA, ME, NH,
RI, VT 0.2% 1.2% Northeast Power Coordinating Council / NYC

- Westchester NY 0.3% 1.1% Northeast Power Coordinating Council / Long Island NY 0.1% 1.4% Northeast Power Coordinating Council / Upstate New

York NY 0.0% 1.4% Reliability First Corporation / East DC, DE, MD, NJ, PA 0.3% 1.4% Reliability First Corporation / Michigan MI 0.1% 1.5% Reliability First Corporation / West IL, IN, OH, WV 0.2% 2.4% SERC Reliability Corporation / Delta AR, LA, MS 0.7% 2.2% SERC Reliability Corporation / Gateway MO 0.2% 2.2% SERC Reliability Corporation / Southeastern AL, GA 0.8% 1.4% SERC Reliability Corporation / Central KY, TN 0.8% 1.0% SERC Reliability Corporation / Virginia

- Carolina NC, SC, VA 0.9% 1.4%

Southwest Power Pool / North KS 0.3% 2.1%

Southwest Power Pool / South OK 0.7% 2.3% Western Electricity Coordinating Council / Southwest AZ, NM, NV 1.3% 2.0% Western Electricity Coordinating Council / California CA 0.8% 1.9% Western Electricity Coordinating Council / Northwest

Power Pool Area ID, MT, OR, WA,

WY 0.9% 0.4% Western Electricity Coordinating Council / Rockies CO, UT 1.3% 1.8%



### State

- Specific Adjustments for Each Scenario

Future retail sales and revenues in each state will depend, in part, on the amount of savings

achieved from future customer

- funded energy e f ficie n cy progra m s. In order to m aintain internal consistency, we adjusted the retail sales and revenue projections for each scenario in each state, to reflect the energy efficiency sa v i n g s assu m ed for the g i ven scenario. T he adju s t m e nts consisted of decreasing (or increasing) the baselin e sales and revenue in each year, to account for

the cu m ulative difference between the savings a s su m ed for the scenario and the sa v i n gs assu m ed

to be i m plicit in the AEO2012 forecast. To p r ovide an exa m ple: if we project, under one scenario, that future annual incremental sa vi ngs in a gi ve n state will be equal to 0.3% of retail sales in each year, and the energy efficiency savings assu m ed to be i m pli c it in the b aseline r e tail

sales f or e c a st are 0. 1 % of retail sales, then we would reduce t h e forecast in ea ch year to account
for the cu m ulative effect of the additional 0

. 2% of retail sales saved each year ( i. e., reduce t h e retail sales projection b y 0 .2% in year one, by 0.4% in year two, and by 0.6% in year three and so on).

The foregoing adjustment requires an estimate of the savings embedded in the AEO

- derived baseline retail sales forecast for each state. Although NEMS does not explicitly account for the impacts of future utility customer

- supported efficiency programs, the model operates under the

### 35 implicit assumption that historical trends in utility customer

- funded efficiency programs will continue over the forecast period. 31 

### We therefore assumed that the baseline retail sales projections, derived from the AEO2012 forecasted g rowth rates, i m plicitly account for a

continuation of customer

- funded energy efficiency progra m savings equal to the average level

achieved over the 2000 to 2 010 period. Comprehensive and reliable state

- level data on efficiency pro gram savings are available only from 2006 onward, although sufficiently reliable historical spending data for the U.S. as a whole are available going back to 2000. Thus, the average annual incremental savings in each EMM region over the 2000

- 2 010 period w ere calc ulated via a two

- step process. First, historical state

- level savings from 2006 to 2 009 were aggregated up to each EMM region to calculate the average annual incremental savings in each EMM region over that period. Second, the 2 000

- 2010 average EM M savings were calculated by multiplying the 2006

- 2009 average EMM savings (from the previous step) by the ratio of average U.S. spending over 2000 to 2 010 to average U.S. spending over 2006 to 2 009. The

result of this two

- step calculation yielded the ann ual incremental energy efficiency program savings assumed to be embedded in the AEO

- derived baseline retail sales forecast for each EMM region (see Table A

- 2).

Table A

- 2

. Estimate Customer

- Funded Energy Efficiency Program Savings Embedded in AEO

Forecasts by EMM Region

Electricity Market Model (EMM ) Re g ion Annual Incremental Savings (% of Retail Sales) Electric Reliability Council of Texas 0.1% Florida Reliability Coordinating Council 0.1% Midwest Reliability Council / East 0.4% Midwest Reliability Council / West 0.3% Northeast Power Coordinating Council / Northeast 0.6% Northeast Power Coordinating Council / NYC

- Westchester 0.3% Northeast Power Coordinating Council / Long Island 0.3% Northeast Power Coordinating Council / Upstate New York 0.3% Reliability First Corporation / East 0.1% Reliability First Corporation / Michigan 0.1% Reliability First Corporation / West 0.0% SERC Reliability Corporation / Delta 0.0%

SERC Reliability Corporation / Gateway 0.0% SERC Reliability Corporation / Southeastern 0.0% SERC Reliability Corporation / Central 0.0% SERC Reliability Corporation / Virginia

- Carolina 0.1%

Southwest Power Pool / North 0.0%

Southwest Power Pool / South 0.0% Western Electricity Coordinating Council / Southwest 0.3% Western Electricity Coordinating Council / California 0.6% Western Electricity Coordinating Council / Northwest Power Pool Area 0.3% Western Electricity Coordinating Council / Rockies 0.3%

31 NEMS is calibrated to historical data on end

- use stock efficiency and shipments

, and the customer adoption

simulation operates under the assumption that, in essence, consumers will continue purchasing equipment that

exceeds minimum ef ficiency standards to the same extent as has historically occurred.

### 36 A1.2 Scenario Definitions

Scenarios were defined on a state

- by

- state basis, driven primarily by each state’s unique policies, regulatory dynamics, program histories, available reports and approved DSM or IRP plans. In

order to s i m pli f y the scena r io de v elop m ent and characterization process, we grouped states into Census Regions (see Figure A

- 1). Tables A

- 3 through A

- 6 summarize the assumptions used for each state by region. For sc e n a ri o s def i n e d in ter m s of a ssu m ed sav i n g s, s p e n di n g l ev els are

der i ved f r om saving s , and vice

- versa

. For a group of seven “uncommitted” states that currently have little efficiency program activity and no established policy framework, we instead employ ed a standardized approach by which spending as a percentage of revenues was stipulated to increase above current levels by a specified amount under each scenario, as described in Table

A

- 7

. 32 

### U n less o t h e rwise i nd i cate d

, s p e nd i n g a n d sa v i n g s p r oj ectio n s f o r m un ici p al u tiliti e s and

co op e rati v es within the states covered in Tables A

- 3 through A

- 6 are also d e v e l o p ed using the same assumptions as for uncommitted states.

Figure A

- 1

. U.S. Census Regions Used for Scenario Development

Table A

- 3

. Scenario Descriptions for States in the Northeast

State Case Scenario Assumptions

CT Low SBC funding remains at current approved levels, but RGGI and FCM funding are no longer used for utility customer

- funded energy efficiency

.

Medium Spending percentage remains at current levels indefinitely; RGGI and FCM funding remains at current levels over entirety of forecast period. High IOU savings equal to all achievable energy efficiency potential, based on NEEP potential analysis, but

32 These seven uncommitted states include: AK, KS, LA, ND, NE, SD, and WV

### 37 subject to an annual spending ca p equal to 6.5% of revenue

MA Low IOU spending/savings from 2010 to 2 012 based on most recent plans. For subsequent years, utility

customer funding declines to the legislatively mandated SBC plus current RGGI/FCM levels (e.g.,

consistent with a future in which very aggressive codes and standards exhaust much of the cost

effective energy efficiency potential). POU spending/savings based on generic assumptions in all

scenarios.

Medium IOU savings levels remain flat at 2012 level, equal to 2.3% percent of retail sales.

High IOUs meet state policy of acquiring all cost

- effective energy efficiency , with savings estimated based

on the energy efficiency potential study conducted by NEEP, subject to an assumed annual spending cap equal to 10% of revenue from retail sales.

ME Low Spending remains constant at 2011 level of 1.6% of revenues from retail sales

Medium Spending from 2011 to 2 013 based on Efficiency Maine's projected budget, and remains at 2013 level, equal to 2. 9 % of revenues from retail sales, through 2025.

High Savings rise to achieve all cost

- effective energy efficiency , based on NEEP analysis, but subject to a spendin g cap equal to 3.6% of revenues

.

NH Low Spending levels through 2020 are based on a continuation of planned 2012 SBC & FCM funding

levels for electric energy efficiency , equal to 1.3% of revenues from statewide electricity retail sales.

Medium Savings ramp up from 0.6% of retail sales in 2012 (the projected savings under current approved funding levels) to 1.2% of retai l sales in 2020 , and remain constant at that level thereafter

. High Same as the Medium Case, except savings ramp up to 2.0% of retail sales in 2020.

NJ Low Spending from 2013 to 2 016 is based on RFP (strawman) issued by BPU for a new program administrator, de

- rated to 70% to reflect historical levels of under

- spending. After 2016, spending on

incentive

- based programs remai ns flat at the 2016 level, and spending on financing programs remains at the average of the projected l evel over the 2013

- 2016 period.

Medium Spending from 2013 to 2 016 is based on RFP (strawman) issued by BPU for a new program administrator, with no de

- rating as is applied in the Low Case. After 2016, spending on both

incentive

- based programs and financing programs remains flat at the projected 2016 level.

High Same as Medium Case through 2016, and after 2016, spending on financing programs is also the same as the Medium Case. However, spending on incentive

- based programs after 2016 rises such that total savings from both incentive

- based programs and financing programs reaches 1.5% of retail sales in

2025 .

NY Low Funding remains at 2010 levels at 2. 5 % revenues.

Medium Savings from 2009 to 2 015 are based on the portion of the state's overall EEPS target allocated by the

NYPSC to utility customer

- funded programs. Spending from 2016 to 2 020 remains constant at the average level during 2009 to 2 015, equal to 2.9% of revenues.

High Savings fro m 2009 to 2 015 are based on the portion of the state's overall EEPS target allocated by the

NYPSC to utility customer

- funded programs. Spending from 2016 to 2 020 remains constant at 3. 9 %

of revenues.

PA Low Spending through 2012 is based on approved budget, and decreases to 75% of 2012 spending in 2013, equal to 1.5% of revenue

Medium Spending rises to 2% of 2008 revenue, equal to $268 million (current cap) in 2013 and continues indefinitely

High Same as Low/Medium case through 2013, but then rate cap is removed and IOU savings increase to

1.0% of retail sales in 2025

RI Low Spending through 2025 remains flat at the 2011 level proposed by National Grid in the approved settlement for the 2011 Energy Efficiency Plan, estimated to be 4. 3 % of revenues

.

Medium Savings ramp up from planned 1.3% of retail sales in 2011 to 1.9 % in 2025, as achieved by MA IOUs

under Medium Case.

High IOUs acquire all cost

- effective energy efficiency , with savings estimated based on the energy efficiency potential study conducted by NEEP, subject to an assumed annual spending cap equal to

10% of revenue from retail sales.

VT Low Spending through 201 4 is based on Efficiency Vermont's approved 2012

- 2014 budgets plus Burlington Electric's 2010 spending; spe nding from 2015 onward remains constant at the average of

2012

- 2014 levels, equal to 6.7% of statewide revenues.

Medium Same as Low Case

High Acquire all cost

- effective EE, with savings estimated based on the EE potential study conducted by NEEP, subject to an assumed annual spending cap equal to 10% of revenue from retail sales.

### 38 Table A

- 4

. Scenario Descriptions for States in the South

State Case Scenario Assumptions

AL Low TVA savings based on 2010 IRP “Baseline Portfolio,” rising to 0.3% of retail sales. Non

- TVA load

based on "uncommitted state" low scenario.

Medium TVA savings based on 2010 IRP selected portfolio, rising to 0.7% of retail sales. Non

- TVA load

based on "uncommitted state" medium scenario.

High TVA savings based on 2010 IRP "High EEDR" portfolio , rising to 1.0% of retail sales . Non

- TVA

load based on "uncommitted state" state high scenario.

AR Low Annual savings from IOU utility customer

- funded energy efficiency through 2013 are based on most recently approved DSM plans, and savings remain flat at 2013 levels (0. 8 % of retail sales) thereafter.

Medium Same as Low Case, except IOU savings rise to 1 .0 % annual savings in 2017 and remain flat at that

level thereafter.

High Same as Low Case, except IOU savings rise to 1.25% annual savings in 2021 and remain flat at that

level thereafter.

DC Low The DC City Council terminated all but about $2 million in funding for 2011 programs. Energy efficiency spending drops that year then ramps gradually to 1% of revenues in 2020 and remains flat

to 2025.

Medium Savings ramp up to 1% of retail sales in 2013 (a year later than mandated in the District contract with VEIC) and remains flat to 2025.

High Savings ramp up to 1% in 2013 as in the Medium Case, then to 1.2% of retail sales in 2020, then flat

to 2025

DE Low The Sustainable Energy Utility ceases to exist in 2012. No utility customer funding for energy efficiency until utilities start their own programs in 2020 (next IRP, assumed funding is equal to RGGI funding).

Medium SEU continues to exist funded by the proceeds of the RGGI auctions (assumed constant at 2011 levels).

High SEU contines to exist with RGGI funding, utilities start their own programs in addition to the SEU. Savings achieve 1% of retail sales in 2020.

FL Low IOUs and large munis achieve sa vings in their site plans, which fall to 0.14% of retail sales in 2021, and remain flat at that level to 2025. Remaining utilities based on "uncommitted state" low case assumptions.

Medium Savings for IOUs and large munis in each year are equal to the mid

- point of the low and high cases. Remaining utilities based on "uncommitted state" medium case assumptions through 2019, reaching savings of 0.3% of retail sales and remaining at that level thereafter

.

High Savings for I OUs and large munis ramp from current levels of 0.2% to 0.5% in 2019, per the FPUC's adopted 2010

- 2019 goals, and then ramp up further to 0.75% in 2025. Remaining utilities based on "uncommitted state" high case assumptions.

GA Low GA Power savings through 2013 are based on approved 2011

- 2013 DSM plan, and remain flat at 2013 level thereafter (0.3% of retail sales).

Medium GA Power savings based on 2010 IRP targets, which rise to 0.4% of retail sales by 2020, and are

assumed to remain flat thereaft er. High GA Power savings assumed to ramp up to 1.0% of retail sales by 2025.

KY Low IOUs realize early

- year savings from 2011 DSM plan and 2011 IRP, reaching 0.4% of retail sales in

2013 and flat thereafter. For TVA load, see Alabama (AL).

Medium IOUs realize savings from DSM portfolio and 2010 IRP, reaching 0.6% of retail sales in 2014 and

constant thereafter. For TVA load, see Alabama (AL).

High IOUs realize highest level of savings from 2011 DSM plans and 2011 IRP, reaching 0.8% of retail

sales in 2014 and constant thereafter. For TVA load, see Alabama (AL).

MD Low IOU and SMECO spending through 2015 based on their proposed 2012 energy efficien cy plans, but

with a 1

- year lag. For 2015 to 2 020, spending remains flat at 2015 level, equal to 2. 3 % of revenues.

Medium IOU and SMECO spending through 2014 based on their proposed 2012 energy efficiency plans. For

2015 to 2 020, spending remains flat at the 2015 level, equal to 2. 3 % of revenues.

High IOU and SMECO achieve the EmPower Maryland energy efficiency savings target in 2015. For 2015

to 2 020, savings levels stay at 1.6% of retail sales.

MS Low S ame as Alabama (AL)

Medium S ame as Alabama (AL) High S ame as Alabama (AL)

NC Low IOUs exhaust, but do not exceed, the energy efficiency allowance under the state RPS; POUs first exhaust their allowable use of large hydro, and then meet 50% of remaining RPS needs (after all set

asides are met) wi th energy efficiency

.

### 39 Medium Use the Baseline EE Scenario adopted by the NC. Energy Policy Commission (based upon the La Capra Associates report).

High All utilities ramp up from annual savings levels of 0.3% of retail sales in 2010 (as projected for that year in the Low/Medium Cases) to 1.3% in 2020, then flat through 2025. This is largely consistent with the NC Energy Policy Commission's high energy eff iciency scenario, which is based upon $0.10/kWh avoided costs.

SC Low Majority of savings and spending are SC share of NC REPS

- driven savings and spending by Duke and Progress. Extend IOU savings at 2013 levels (0.5% of retail sales) through 2025.

Medium Assume Duke, Progress and SCEG realize the base case savings projections from their IRPs, rising to 0.7% of retail sales by 2020.

High Assume Duke meets its IRP High Case and other large IOUs take their IRP base case savings projection, with total IOU savings rising to 0.8% of retail sales in 2020 and ramping up further to

1.1% of retail sales in 2025.

TN Low S ame as Alabama (AL)

Medium S ame as Alabama (AL) High S ame as Alabama (AL)

TX Low IOU savings meet current EERS goal (20% of incremental peak demand, ramping to 25% in 2012 and 30% from 2013 on); POU savings/spending are based on generic low case assumptions.

Medium IOU savings assumed to continue at present levels of over

- performance relative to EERS goal. POU savings/spending are based on the generic high case assumptions, with savings ramping up to 0.5% of retail sales by 2025.

High Statewide savings are based on both IOUs and POUs meeting the current EERS savings targets through 2014 (as in the Mid

- Case), w ith savings from 2015 onward equal to 50% of demand growth in each year (as previously proposed).

VA Low Spending as a percent of revenues by Dominion Virginia Power continues at the current rate (0.2% of revenues).

Medium IOUs reach 0.4% annual savings in 2018 and remain flat thereafter, based roughly on the spending trajectory from Dominion Virginia's most recent IRP. High All IOUs meet 10% by 2022 voluntary EERS (relative to 2006 load).



### Table A

- 5 . Scenario Descriptions for States in the Midwest

State Case Scenario Assumptions

IA Low Statewide spending (IOUs & POUs) continues indefinitely at 2010 approved spending levels, 2.9% of revenue, with corresponding savings roughly constant at 1.0 % of retail sales.

Medium For 2009 to 2 013, IOU spending projections are based on the approved budgets in their 2009

- 2013 energy efficiency plans, and POU energy efficiency spending is equal to the projected annual spending

for 2010 to 2 013. For 2014 to 2 020, continue IOU & POU spending at projected 2013 level 3. 4 % of revenues, with corresponding savings roughly constant at 1. 2 % of retail sales.

High All utilities (IOUs & POUs) achieve savings equal to 1.5% of retail sales by 2011 (the target that IOUs were required to evaluate in their most recent energy efficiency plans) and continue at that level until 2020 when they reach 2% savings.

IL Low IOU savings/spending through 2013 are based on approved energy efficiency plans. From 2014 to

2 020, savings equal EEPS targets until statutory cost caps are reached, which limits savings to approximately 0.8% of retail sales.

Medium Same as Low Case, except IOU savings are assumed to rise to 1.3% of retail sales in 2016

High IOU savings/spending through 2013 are based on approved energy efficiency plans. Statutory EEPS spending cap is assumed to be lifted or increased, and EEPS targets are assumed to be fully achieved.

IN Low IURC

- jurisdictional utilities (IOUs and some coops and munis) achieve savings levels equal to 75% of

EERS levels.

Medium IURC

- jurisdictional utilities fully meet EERS savings targets. High Same as Medium Case.

MI Low Statewide savings based on full compliance with statutory EEPS targets, which reach 1.0% of retail

sales in 2012.

Medium Same as Low Case. High Assumes that statewide savings ramp up from 1.0% of retail sales in 2012 to 1. 4 % in 2020.

MN Low All utilities meet the minimum 1.0% EEPS target for conservation improvement programs.

Medium Savings ramp up from 1.0% of retail sales in 2010 to 1.2% in 2020. High Same as Medium Case, except savings ramp up to 2.0% in 2020.

### 40 MO Low IOU annual savings are based on most recently approved IRPs and DSM plans through 2015, and savings thereafter remain flat at that level.

Medium Same as the Low Case through 2015; from 2016 onward, IOU savings as a percentage of retail sales

are the mid

- point between the Low and High Cases.

High IOU savings track approved/submitted plans through 2014, then meet voluntary goals in MO PSC's MEEIA regulations, rising to 1.9% of retail sales in 2020 and remaining at that level thereafter.

OK Low IOU savings and spending through 2015 based on most recent filed plans, and savings thereafter

remain flat at the 2015 level.

Medium Same as Low Case through 2015, after which IOU savings ramp up to 0.75% of retail sales in 2020 (as a proxy for the soft rate cap on residential DSM charges) and remain flat at that level thereafter.

High Same as Medium Case through 2020, but IOU saving s continue to ramp up to 1.0% of retail sales in

2025.

OH Low Most IOUs achieve savings equal to 50% (First Energy meets 25%) of legislated EEPS targets, rising to 1.0% of retail sales by 2019; stipulated savings reflect a possible reduction in EEPS savings targets, and/or high assumed levels of mercantile customer opt

- out, reliance on T&D measures, and non

compliance.

Medium IOUs meet EEPS savings targets in 2022 (delayed four years to ramp to 2022) with the exception of First Energy. First Energy meets 50% of its savings targets relying on high assumed levels of mercantile customer opt

- out, reliance on T&D measures, and non

- compliance.

High IOU savings meet legislated EEPS targets of 2% retail sales in 2019, First Energy meets 75% of EEPS target; stipulated savings reflects lower assumed levels (relative to Low/Medium Case) of mercantile

customer opt

- out, reliance on T&D measures, and non

- compliance.

WI Low Spending declines from 2.1% of revenues in 2011, back to 2008 level of 1.2%, consis tent with legislative rate cap, and remains flat at that level through 2025

Medium Same as Low Case through 2015, then spending rebounds to 1.7% of revenues by 2020 (the mid

- point between the current spending cap and the maximum historical spending level).

High Savings based on lagged 2010 regulatory targets and lagged EEPS policy recommendation in Governor's Task Force on Global Warming, with spending rising to 3.8% of revenues and savings rising to 2.0% of retail sales by 2014.



### Table A

- 6 . Scenario Descriptions for States in the West

State Case Scenario Assumptions

AZ Low IOUs and Coops meet minimum EERS requirements; annual savings decline over the 2016 to 2 020

timeframe because utilities are able to use retroactive credit from pre

- EERS programs during that period, and the savings percentage after 2020 remains flat at the 2020 level. SRP savings remain flat at actual 2010 level. Other utilities (munis, tribes, irrigation districts) based on generic metho d

in each scenario.

Medium IOUs and Coops are same as the Low Case . SRP savings through 2016 based on targets in 2010 Sustainability Report Summary, and remain flat at the 2016 level thereafter.

High IOUs and Coops meet EERS requirements without relying on retroactive credit from historical

programs. SRP savings follow the targets in the Sustainable Portfolio Plan through 2020, which rise to 2% of retail sales, and remain constant thereafter (per

- unit costs for SRP are higher, to reflect a

proport ionally lower reliance on M

- Power program than in other scenarios).

CA Low For IOUs, savings through 2012 are based on level in approved 2010

- 2012 energy efficiency plans, savings from 2013 to 2 014 are based on 90% of levels in the proposed plans, and s avings from 2015 onward are equal to 90% of the maximum achievable potential identified in Navigant's 2012 potential study for the CPUC. POU annual savings remain flat at 2010 levels.

Medium For IOUs, savings through 2012 are based on level in approved 2010

- 2012 energy efficiency plans, savings from 2013 to 2 014 are based on 100% of levels in the proposed plans, and savings from 2015 onward are equal to 110% of the maximum achievable potential identified in the Navigant potential study. POU annual savings equal 100% of the average annual savings from their 2010

- 2020 energy efficiency goals.

High For the IOUs, savings through 2012 are based on level in approved 2010

- 2012 energy efficiency pla ns, savings from 2013 to 2 014 are based on 125% of levels in the proposed plans, and savings from 2015 onward are equal to 130% of the maximum achievable potential identified in the Navigant potential study. POU annual savings equal 125% of the average an nual levels identified in

their 2010

- 2020 energy efficiency goals.

CO Low IOUs maintain savings at levels in approved 2011 DSM plans, equal to 0.9% of retail sales in aggregate; Colorado Springs and Fort Collins continue at 2010 savings levels equal to 0.5% of retail sales; generic assumptions for all other POUs are made in each scenario.

### 41 Medium IOUs meet EEPS targets, which rise to 1. 9 % of retail sales in 2020; Colorado Springs and Fort

Collins achieve and maintain savings of roughly 1% of retail sales by 2015, consistent with CSU's Energy Vision goal. High Same as Medium Case

HI Low Statewide savings exhaust, but do not exceed, the 50% energy efficiency allowance under state RPS.

Medium Same as Low Case

High Same as Low/Medium Cases thr ough 2011, but savings increase from 1.2% of retail sales in 2011 to

2.0% in 2020.

ID Low IOU savings are based on projections from each utility's most

- recent IRP (Idaho Power's projected savings are well below 2010 program results). POU savings based on achieving their pro

- rated share of the NPCC 6th Plan conservation targets, with 60% of th e savings assumed to come from utility customer

- funded energy efficiency (and the remainder from new codes/standards); savings taper off in the latter years as the retrofit potential is exhausted.

Medium Idaho Power savings remain flat at level achieved in 2010, and other IOU savings are based on their IRP projections. POU savings are same as the Low Case through 2020, except that 75% of the conservation target is assumed to come from utility customer

- funded energy efficiency , and after 2020, the savings level remains constant at the 2020 level, rather than tapering off as in the Low

Case.

High IOU savings ramp up from 1.1% of retail sales in 2010 to 1.5% in 2020, and remain flat at that level thereafter. POU savings through 2020 are based on their pro

- rated share of NPCC's 6th Plan conservation targets under NPCC's high ramp rate trajectory, an d 85% of the savings is assumed to

come from utility customer

- funded energy efficiency ; savings after 2020 remain at the 2020 level, rather than tapering off as under NPCC's projection.

MT Low NorthWestern savings based on the targets identified in its 2009 Electric Supply Resource Procurement Plan, which remains flat at approximately 1.0% of retail sales per year. BPA

- served POU savings based on same assumptions as Idaho POUs.

Medium For NorthWestern , same as Low Case. BPA

- served POU savings based on same assumptions as

Idaho POUs.

High NorthWestern savings ramp up to 1.5% of retail sales per year in 2025. BPA

- served POU savings

based on same assumptions as Idaho POUs.

NM Low IOU savings through 2013 based on most recent approved DSM plans, and savings thereafter decline to the minimum level necessary for EERS compliance, which requires average annual savings of approximately 0.7% over the 2014

- 2025 period. Savings/spending projections for POUs

a re based on generic assumptions in each scenario.

Medium IOU savings through 2013 based on most recent approved DSM plans, and remain constant at the 2013 level thereafter, equal to 0.8% of retail sales. High IOU savings ramp up to 1.5% of retail sales in 2020

NV Low IOU savings and spending through 2013 are based on most recent DSM plans, and thereafter decline

to the maximum energy efficiency allowance under the RPS. POU savings/spending projections are based on generic assumptions under each scenario.

Medium IOU savings after 2013 remain flat at 2013 levels, equal to 0.7% of retail sales, which far exceeds

the energy efficiency allowance u nder the RPS High Same as Medium Case, except that savings ramp up to 1.5% of retail sales in 2020

OR Low PGE and PacifiCorp savings are based on Energy Trust 2010

- 2014 Strategic Plan savings projection under "current funding" scenario, and remain at the 2014 level thereafter. Idaho Power savings are

based on most

- recent IRP. POU savings based on same assump tions as in Idaho (ID).

Medium PGE and PacifiCorp savings are based on Energy Trust 2010

- 2014 Strategic Plan sa vings projection

under "IRP

- ach i e vable" scenario, and remain at the 2014 level thereafter. Idaho Power savings are

based on most

- recent IRP. POU savings based on same assumptions as in Idaho (ID).

High Statewide savings through 2020 is based on Oregon's pro

- rated share of the NPCC's 6th Plan conservation targets, under NPCC's "high ramp rate" trajectory, and 85% of the savings is assumed

to come from utility customer

- funded energy efficiency ; savings aft er 2020 remain at the 2020 level, rather than tapering off as under NPCC's projection.

UT Low IOU (PacifiCorp) savings remain constant at 2010 level of 0.9 % of retail sales. POU savings/spending in all scenarios are based on generic assumptions.

Medium PacifiCorp savings follow 2011 IRP schedule, which rises to 1.2% of retail sales in 2021, and stays

flat at that level thereafter High PacifiCorp savings rise to 1.5% of retail sales in 2020, and stay flat at that level thereafter

WA Low Savings based on achieving WA's pro

- rated share of the conservation targets in the NPCC's 6th Plan, with 60% of the savings assumed to come from utility customer

- funded energy efficiency (and

### 42 the remainder from new codes/standards); the savings trajectory tapers off in the latter years as the retrofit potential is exhausted.

Medium Same as the Low Case, except 75% of the conservation target is assumed to come from utility

customer

- funded energy efficiency , and after 2020, the savings level remains constant at the 2020 level, rather than tapering off as in the Low Case.

High Savings through 2020 are based on WA's pro

- rated share of the conservation targets in the NPCC's 6th Plan, under NPCC's "high ramp rate" trajectory, and 85% of the savings is a ssumed to come

from utility customer

- funded energy efficiency (and the remainder from new codes/standards); savings after 2020 remain at the 2020 level, rather than tapering off as under NPCC's projection.

WY Low PacifiCorp 2009

- 2013 savings and spending are based on approved DSM plan settlement, and savings from 2014 to 2 025 are held constant at 2013 level, equal to 0.5% of retail sales, which is roughly in line with the savings projection from PacifiCorp's 2011 IRP. For all other utilities, spending/sav ings projections are based on generic assumptions for each scenario.

Medium Same as Low Case

High Same as the Low Case through 2013, but savings thereafter ramp up to 1.0% of retail sales in 2020

and remain constant at that level thereafter.



### Table A

- 7

. Energy Efficiency Spending Assumed for “Uncommitted” States

Case Increase in Spending as a Percent of Utility Revenue Relative to 2010

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

L o w 0.2% 0.2% 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3% 0.3%

Medium 0.4% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% High 0.4% 0.5% 0.5% 0.6% 0.6% 0.7% 0.7% 0.7% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8% 0.8%



### A1.3 Cost of Savings Assumptions

Depending on the particular state and scenario, t h e spending projection m ay have been esti m ated

from projected first

- year savings or v i ce

- v ersa. In either ca s e, f i rst

- year savings were translated into annual spending (or vice

- versa) using an assu m ed cost of savings. We assu m e that the average cost of savings depends in part on the savings level achieved. To capture this

r el a tio n ship, we developed a generic “cost function” that relates t h e avera g e c o st of first

- year electricity savings to the s avin g s lev e l expr e ssed as a p er centage of the utility (or state)’s r e t a il

s a les (see Figure A

- 2). The y

- axis v alues in the figure are e x pressed on a nor m alized ( dimension less) b a sis, with a cost index of 1.00 at a savings level equal to 1.0% of retail sal e s. The rationale for this cost function is to reflect the fact that, based on our re v i ew of ener g y efficiency program experie n ce, utility costs to acquire savings (on a dollar

- per

- M Wh basis) can be so m ewh a t higher when portfolio savings levels are low (i.e., annual sav i ngs <0.5% of retail sales), due to the effect of fixed program deliv e ry co s ts and be c a u se the u tility is i m ple m e nting pilot progra m s or is ra m ping up its ad m i nistrative and delivery infrastructure. There is also

evi d ence to sug g es t t h at pro g ram costs increase at relatively high savings targets (i.e., annual savings >1. 5 % of retail sales) either because re b ate levels m ay be raised in order to a c hieve hig h er m arket pe n etration o r b ecause the utility incl u des m ore expensive e n ergy efficiency

m easures in its program portfolio.

The cost function was then ap p lied to each s t ate by “scali n g” the generic co s t function shown in Figure A

- 2 using either state

- specific program cost data (if available) or an assu m ed a verage cost
of savings

. 33

33 For e x a m ple, if data for a given state ind i cate that aver a g e progr a m costs are $200 per first

- y r. M Wh s av ed at

sav in g s eq ual t o 1

. 0% of reta i l sa l es, t h e n t h e g e neric c o st f unct io n w ou l d yi eld an a v er ag e cost of $2 5 0 per first

- y r.
M W h at sa v i n g s eq u al to 2

. 0% of retail sales (i.e., 1

. 2 5 t i mes t h e c o st at a sa v i n g s le v el e qu al t o 1

. 0 % of retail sales). 

### S tate

- specific cost of savings data (i.e., cost per first

- year MWh saved) were used

### 43 for 23 states, based on recent program results or recently

- approved DSM program plans. For the remaining 27 states , a generic cost of savings value was used to scale the cost function. T hese

states were first categorized as either a low

- cost state or a high

- c ost state. Low

- c ost states were
then assumed to have average program costs equal to $ 15 0 per first

- year MWh s aved at a savings

level of 1.0 % of retail sales, based on data compiled by ACEEE ( Sciortino et al. 20 11 ). High

c ost states were assumed to have average program costs equal to $ 300 per first

- yr. MWh saved at a savings level of 1.0% of retail sales, which is based roughly on average costs currently observed among some Northeastern states.

Figure A

- 2

. Generic Program Cost Function



### A2. Natural G a s Energy Efficiency Spending Projections

We developed low, m edium, and high projections of spending through 2025 on energy efficiency

progra m s funded by customers of natural gas utilities. Given that spending on natural gas

progra m s represents a relatively s m all portion of tot a l ( e le c t ric plus gas) customer

- funded spending, we use d a si m pler and more st a ndardized approach to project future spending,

co m pared to electric energy efficiency programs.

A2.1 Revenue Projections

Projections of revenue from ret a il n a tur a l gas s a les to r e side n ti a l, com m ercial, and in d ustrial

custo m ers (i.e., excluding sales to electric utilities) were deve l oped in a si m ilar m anner as the baseline projections of revenue from retail ele c t r icity s ales. R etail s a les a n d ret a il p r ice projections were first de veloped for each state b y applying annual growth rate p r ojections from the AEO2012 reference case forecast to actual 2010 retail sales and price data for each state, as reported by EIA. Retail g a s sales in c l u de sales to r eside n ti a l, c o m m ercial, industrial, and transportation sector s, but exclu d e sales to the electric power sector. Average annual retail gas prices were calculated as the a v erage of E I A’s forecast of prices for the residential, com m ercial,

industri a l, and transportation sectors, w e ighted by the quantity of sales to each sector. The 0.50 0.75 1.00 1.25 1.50 1.75 2.00

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%Program Costs per 1st-Yr. MWhNormalized to 1.00 at 1% Savings

Savings as % of Retail Sales

### 44 nat u ral gas retail sales and retail price pr o j ections in A EO2012 are specified at the censu s

- region

level. Thus, the growth rates in each census region were applied to each state in their respective region. Reve n ue projecti o ns were calcula ted by m ulti p lying pr ojected retail gas prices by projected retail gas sales, and were conv e rted to no m inal dollars using the AEO2012 reference

case foreca s t o f the GDP chain

- type price in d ex. Unlike the electricity revenue projections, no adjust m ents were m ade to the n atural gas revenue projections to acco u nt for differing levels of energy efficiency savings acr o ss scenarios.

A2.2 Scenario Definitions

For the purpose of developing projections of utility customer funding of gas efficiency program s, we first grouped states into three categories: Tier I consists of the 13 states that comprise more than 80% of current national funding for gas efficiency programs, Tier II consists of another 15 states where 2010 spending on gas efficiency programs exc eeded $0.50 per capita, and Tier III consists of the remaining 23 states. Table A

- 9 identifies the states in each group.

Table A

- 8

. Analysis Framework for Natural Gas Energy Efficiency Programs

Tier I CA, CO , IA, IL, IN, MA, MI, MN, NJ, NY, OR, UT, WI

Tier II AR, CT, ID, MD, MO, NH, NM, NV, OH, PA, RI, SD, VT, WA, WY

Tier III AK, AL, AZ, DC, DE, FL, GA, HI, KS, KY, LA, ME, MS, MT, NC, ND, NE, OK, SC, TN, TX, VA,

WV



### Tier I Scen a rio Defi n iti o ns

For Tier I states, gas efficiency program spending projections are based on state

- specific policies, gas DSM program plans, and regulatory decisions that set savings targets for gas

utilities. Table A

- 10 describes the specific ass u mptions un d erl y ing the t hree scenarios in each

Tier I s tate. For most Tier I states, the low and medium case spending projections track the most

recent multi

- year gas DSM program plans to their terminal year (typically 2012 to 2 014). In the low case, we assume that spending on residential gas efficiency programs in most Tier I states
will decline by 50% to 7 5% relative to the level in the terminal year of the most recent DSM plan, while spending on commercial and industrial (C&I) programs will decline by roughly 2 0%. This decline in spending is due to the combination of sustained low natural gas prices, which

reduce the cost effective ness of gas efficiency programs and tightening federal minimum efficiency standards for gas furnaces, which reduce the savings for v oluntary programs. In the medium case, we assume a more modest drop

- off in residential program spending, typically to 50% of the level from the terminal year of the most recent gas DSM Plan, but that C&I program spending increases slightly, as program man agers shift spending towards markets with greater savings opportunities. In both the low and medium scenario, we assume that gas efficiency programs for low

- income programs, however, remain constant at the level from the last year of the DSM plan, as thes e programs meet broader policy objectives (e.g. equity, reductions in bill arrearages), and therefore are less susceptible to the dynamics putting downward pressure on gas program spending for the other sectors. Finally, in the high case, we assume that m any Tier I

### 45 states achieve gas savings levels on par with gas EERS targets in a number of states (i.e., 1.0

- 1.5% of total utility retail sales).

Table A

- 9

. Gas Energy Efficiency Program Scenario Descriptions: Tier I States

Sta te Case Scenario Description

CA Low Program savings are assumed to track 60% of the total gross market potential excluding codes and standards, based on the Navigant (2012) potential study; spending is calculated from savings, based on the planned savings and spending in the IOU 2013

- 2014 program plans.

Medium Simila r to Low Case, except savings are assumed to track 80% of the total gross market potential excluding codes and standards. High Spending as a percent of revenues remains flat at 2012 levels

CO Low Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time by 75% and C/I spending declines by 20%, to reflect impact of furnace standards and low gas prices. Low

- i ncome program spending as a percentage of revenues continue s at the same level as in th e DSM plan.

Medium Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time by 50%, while C/I spending

increases 30% , as funds are shifted across market segments

. Low

- i ncome program spending as a percentage of revenues continue s at the same level as in the DSM plan. High Savings r amps up to 1% of retail sales to non

- transportation gas customers

IA Low Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time , beginning in 2017, by 75% and C/I spending declines by 20%, to reflect impact of furnace standards and low gas prices. Low

i ncome program spending as a percentage of r evenues continue s at the same level as in the DSM plan.

Medium Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time by 50%, while C/I spending

increases 30%

, as funds are shifted across market segments

. Low

- i ncome program spending as a percentage of revenues continue s at the same level as in the DSM plan.

High Savings r amps up to 1 .5 % of retail sales to non

- transportation gas customers, on par with the

gas EERS targets in IL and MN

IL Low See Iowa (IA) Low Case

Medium See Iowa (IA) Medium Case

High Savings r amps up to meet Illinois gas EERS savings targets (1.5% of retail sales to non

transportation gas customers)

IN Low See Iowa ( IA ) Low Case

Medium See Iowa ( IA ) Low Case

High Savings r amps up to 1% of retail sales to non

- transportation gas customers

MA Low Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time by 50 % and C/I spending declines by 20%, to reflect impact of furnace standards and low gas prices. Low

- i ncome program spending as a percentage of revenues continue s at the same level as in the DSM plan.

Medium Spending tracks most recent gas DSM plan to terminal year. Thereafter, r esidential program spending as a percentage of revenues declines over time by 20%, while C/I spending

increases 30% , as funds are shifted across market segments

. Low

- i ncome program spending as a percentage of revenues continue s at the same level as in the DSM plan.

High Savings r amps up to 1 .5 % of total statewide gas retail sales, a proxy for achievement of all

cost

- effective gas energy efficiency

MI Low See Iowa (IA) Low Case

Medium See Iowa (IA) Medium Case

High See Iowa (IA) High Case

MN Low See Iowa (IA) Low Case

Medium See Iowa (IA ) Medium Case

### 46 High Savings r amps up to meet Minnesota gas EERS savings targets (1.5% of retail sales to non

transportation gas customers)

NJ Low Similar to Colorado (CO) Low Case, except total spending is de

- rated by 70% to reflect historical gap between actual expenditures and planned budgets.

Medium Similar to Colorado (CO) Medium Case, except total spending is de

- rated by 70% to reflect historical gap between actual expenditures and planned budgets, and C/I spending as a percentage of revenues remains flat, rather than increasing. High Savings ramps up to 1% of total statewide retail sales

NY Low Similar to Massachusetts (MA) Low Case , but projections to 2015 are based on achievement of New York’s gas EERS targets

Medium Similar to Massachusetts (MA) Medium Case , but projections to 2015 are based on

achievement of New York’s gas EERS targets

High Similar to New Jersey (NJ) High Case , but projections to 2015 are based on achievement of New York’s gas EERS targets

OR Low See Iowa (IA) Low Case

Medium See Iowa (IA) Medium Case

High See Iowa ( IA ) High Case

UT Low See Iowa (IA) Low Case

Medium Similar to Colorado (CO) Medium Case, except residential program spending declines by 70% rather than 50%, based on information provided by program managers. High See Iowa ( IA ) High Case

WI Low Spending is based on the current legislative cap (1.2% of revenues)

Medium Same as Low Case

High See Iowa ( IA ) High Case



### Tier II Scen a rio Defi n iti o ns

The 15 Tier II states have relatively aggressive spending levels on a per capita basis, but small populations and therefore small spending levels in absolute terms. Thus, for simplicity, the spending projections for these states were developed based on regional benchmark trajectories that were developed from the projections for Tier I states in the corresponding regi on. These regional benchmark trajectories were developed by averaging the change , relative to 2010, in spending as a percentage of gas distribution utility revenues across Tier I states in each census region (see Table A

- 11). Those growth curves were the n applied to the 2010 spending for each Tier II state. As an example, in the medium case, spending for the three Tier I states in the northeast (MA, NY, and NJ) is projected to increase by, on average, 0.6% of revenues; thus, the same 0.6% increase in spe nding as a percent of revenues was stipulated for the Tier II

northeastern states in the medium case.

Tier III Scen a rio Defi n iti o ns

For states in Tier III that currently have little or no customer

- funded gas efficiency program activity, we assumed that spending will stay flat as an absolute dollar value in the low scenario. In the medium scenario, we project that gas efficiency progra ms will continue spending at their present percentage of retail gas revenues. In the high case, we assume that gas efficiency programs will increase program spending to approximately 0.25% of revenues above their

current levels (e.g., if current gas progr am spending is equal to 0.5% of revenues, then we assume in the high case that spending rises to 0.75% of revenues)

.

### 47 Table A

- 10

. Gas Energy Efficiency Program Spending: Tier II States

Case Increase (Relative to 2010) in Customer

- Funded Gas EE Spending as a Percent of Revenues from Retail Gas Sales

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Northeast Low 0.0% 0.8% 1.3% 1.4% 1.4% 1.4% 1.4% 0.9% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5% 0.5%
Medium 0.0% 0.8% 1.3% 1.4% 1.4% 1.4% 1.4% 1.1% 1.1% 1.1% 1.2% 1.2% 1.2% 1.2% 1.2% 1.2% High 0.0% 0.8% 1.3% 1.6% 1.8% 2.0% 2.3% 2.7% 2.9% 3.3% 3.6% 3.4% 3.3% 3.3% 3.2% 3.2%

Midwest

& South Low 0.0% 0.2% 0.4% 0.6% 0.6% 0.6% 0.6% 0.3% 0.0% 0.0% 0.0%

- 0.2%

- 0.2%

- 0.2%

- 0.2%

- 0.2%
Medium 0.0% 0.2% 0.4% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.6% 0.4% 0.2% 0.2% 0.2% 0.2% 0.2% High 0.0% 0.2% 0.4% 0.6% 0.8% 0.8% 1.0% 1.2% 1.3% 1.5% 1.5% 1.4% 1.3% 1.3% 1.3% 1.2%

West Low 0.0% 0.1% 0.2% 0.1% 0.3% 0.3% 0.3%

- 0.2%

- 0.6%

- 0.6%

- 0.7%

- 1.0%

- 1.0%

- 1.0%

- 1.0%

- 1.0%
Medium 0.0% 0.1% 0.2% 0.1% 0.3% 0.3% 0.3% 0.2% 0.2% 0.2%

- 0.1%

- 0.5%

- 0.5%

- 0.6%

- 0.7%

- 0.7% High 0.0% 0.1% 0.2% 0.0% 0.4% 0.6% 0.9% 1.1% 1.2% 1.4% 1.5% 1.4% 1.3% 1.2% 1.2% 1.1%

### 48 

## Technical Appendix B : State

- Level Spending and Savings Projections

The results presented in Section 4 focused on regional and national spending and savings projections. This technical appendix provides additional state

- level details

. Table B

- 1 presents projected electric efficiency program spending by state

, Table B

- 2 presents projected electric efficiency program savings by state, and Table B

- 3 presents projected gas efficiency program spending by state. Note that the spending projections are presented in terms of nominal dollars, as used throughout the report, and the savings proj ections are presented in terms of first

- year GWh. Please refer to Technical Appendix A for details on the underlying scenario definitions and assumptions used for each state in order to develop these spending and savings projections.

### 49 Table B

- 1 . Electric Energy Efficiency Program Spending Projections by State ($M, nominal)

State 2010 * Low Medium High

2015 2020 2025 2015 2020 2025 2015 2020 2025

AK

- 2 3 4 5 5 6 6 9 9

AL 9 23 34 38 51 61 68 60 94 104

AR 9 38 44 48 46 59 65 48 73 85

AZ 88 191 117 136 199 138 160 250 321 361

CA 938 755 721 701 927 878 852 1,203 1,050 1,021

CO 48 62 71 82 105 167 180 107 173 186

CT 108 86 88 89 102 109 123 278 282 301

DC 15 8 17 18 19 20 22 19 24 26

DE 7 0 7 7 7 7 7 12 21 22

FL 165 145 134 160 234 278 325 310 433 727

GA 15 60 73 83 70 93 106 84 143 216

HI 25 66 78 95 66 78 95 62 100 106

IA 59 103 110 119 123 130 140 161 264 288

ID 57 32 40 34 43 51 57 48 71 77

IL 85 233 235 235 314 373 389 666 675 681

IN 5 150 255 267 200 385 395 202 389 399

KS 6 8 13 14 19 21 24 23 34 37

KY 21 45 54 63 75 89 102 93 124 142

LA

- 17 28 32 39 45 53 48 72 84

MA 245 200 221 243 568 580 593 613 625 666

MD 74 172 182 193 174 181 192 168 269 292

ME 14 22 24 27 40 42 46 48 50 54

MI 75 167 172 185 167 172 185 205 278 338

MN 107 110 117 129 121 141 153 175 284 296

MO 36 91 87 93 97 184 188 135 322 342

MS 6 16 22 26 34 42 48 39 63 72

MT 17 14 17 17 17 19 21 20 27 33

NC 52 66 113 124 103 116 147 110 202 314

ND 8 2 3 4 5 5 6 6 9 10

NE

- 5 8 9 12 13 15 14 21 23

NH 18 20 22 26 30 48 52 41 98 101

NJ 191 129 93 97 184 188 194 211 348 532

NM 18 22 25 27 27 31 36 35 59 66

NV 33 16 18 21 36 41 48 49 97 109

NY 482 552 559 608 778 653 704 778 865 913

OH 70 78 162 175 170 297 451 183 463 484

OK 21 53 59 67 57 111 123 58 121 195

OR 135 107 126 131 158 181 199 152 223 243

PA 98 210 224 245 240 240 240 263 329 405

RI 44 43 46 51 73 74 74 96 97 103

SC 15 75 89 103 89 114 131 109 140 210

SD 0 2 3 4 5 5 6 6 8 9

TN 38 47 58 66 93 121 136 98 172 189

TX 114 119 150 171 163 215 254 246 252 285

UT 48 54 60 70 35 46 51 39 63 71

VA 0 27 34 39 43 63 72 115 130 143

VT 36 44 45 48 44 45 48 64 63 66

WA 218 167 229 192 209 287 317 295 428 466

WI 74 83 92 99 83 126 135 255 264 268

WV

- 6 9 10 13 15 17 16 24 27

WY 3 11 12 12 12 13 14 14 23 25

Total 3,948 4,757 5,202 5,536 6,522 7,398 8,062 8,336 10,769 12,222

* Source of 2010 spending data: CEE (201 2 ) , with minor modifications

### 50 Table B

- 2 . Electric Energy Efficiency Program Savings Projections by State (First

- Year GWh)

State 2009* Low Medium High

2015 2020 2025 2015 2020 2025 2015 2020 2025

AK 1 11 16 16 29 28 27 36 46 45

AL 63 121 164 170 288 314 322 337 496 520

AR 60 224 233 236 268 313 318 280 396 421

AZ 571 1,101 761 810 1,164 894 945 1,281 1,430 1,474

CA 2,293 2,497 2,202 1,979 3,067 2,681 2,402 3,670 3,214 2,887

CO 255 327 346 365 546 696 704 556 725 736

CT 250 287 268 248 338 332 342 633 591 577

DC 56 44 90 89 113 111 110 113 133 130

DE 0 0 35 32 40 35 32 73 112 110

FL 365 384 324 343 640 697 747 877 1,166 1,796

GA 54 336 370 388 399 499 517 493 800 1,113

HI 113 176 185 195 176 185 195 172 218 211

IA 410 422 415 410 503 489 480 653 861 864

ID 186 193 218 167 254 274 282 283 351 352

IL 553 1,085 1,005 923 1,465 1,600 1,528 2,494 2,331 2,156

IN 40 885 1,289 1,239 1,183 1,675 1,574 1,191 1,697 1,596

KS 1 42 60 62 103 106 108 126 175 177

KY 65 253 278 294 439 476 501 543 666 699

LA 0 87 129 139 205 220 236 261 377 401

MA 459 563 572 578 1,077 1,008 944 1,105 1,037 1,013

MD 274 688 666 648 698 666 648 673 928 891

ME 94 114 113 117 183 175 171 215 204 202

MI 376 956 936 919 990 936 919 959 1,191 1,279

MN 638 651 635 640 714 766 760 967 1,232 1,178

MO 86 348 355 349 385 755 707 611 1,170 1,086

MS 31 84 111 118 193 220 231 219 341 358

MT 57 75 82 77 88 93 93 101 124 135

NC 52 364 591 591 608 631 733 650 1,095 1,562

ND 3 11 15 16 25 26 27 31 43 44

NE 65 26 37 38 62 64 65 75 105 107

NH 68 60 60 64 90 131 128 123 214 201

NJ 497 211 112 110 311 197 191 374 631 1,018

NM 59 123 125 126 148 156 165 197 282 289

NV 439 89 92 93 211 223 239 287 491 507

NY 950 2,563 2,311 2,299 2,647 2,414 2,449 2,647 2,894 2,936

OH 530 458 875 864 1,007 1,522 1,857 1,084 2,082 1,999

OK 20 174 188 197 198 368 379 208 415 606

OR 292 472 507 484 642 676 681 640 785 782

PA 279 831 815 815 951 872 798 1,042 1,197 1,346

RI 82 107 103 102 149 140 131 172 159 155

SC 46 311 341 362 383 447 470 469 562 760

SD 22 10 15 15 24 25 26 30 42 44

TN 121 256 292 305 552 658 674 581 933 942

TX 751 650 755 786 924 1,118 1,211 1,451 1,364 1,409

UT 177 210 220 232 233 281 286 268 371 381

VA 1 189 214 223 318 454 479 972 1,000 1,003

VT 90 114 110 105 114 110 105 176 164 164

WA 665 722 909 698 903 1,137 1,152 1,231 1,487 1,482

WI 584 435 441 435 435 606 592 1,301 1,220 1,161

WV 0 30 42 44 70 72 76 89 124 128

WY 7 62 62 61 70 68 66 81 122 124

Total 13,147 20,433 21,092 20,613 26,624 28,641 28,823 33,102 39,792 41,561

* Source of 2009 savings data: Sciortino et al. (2011)

### 51 Table B

- 3 . Gas Efficiency Program Spending Projections by State ($M, nominal)

State 2010 * Low Medium High

2015 2020 2025 2015 2020 2025 2015 2020 2025

AK 0 0 0 0 0 0 0 0 1 1

AL 0 0 0 0 0 0 0 2 4 7

AR 2 11 3 2 11 10 8 14 29 30

AZ 2 2 2 2 2 3 4 3 5 8

CA 201 152 100 118 171 134 157 198 249 315

CO 19 15 10 12 15 17 17 31 74 83

CT 12 31 23 27 31 35 41 39 73 80

DC 0 0 0 0 0 0 0 0 1 2

DE 0 0 0 0 0 0 0 0 1 2

FL 11 11 11 11 12 14 17 13 17 23

GA 0 0 0 0 0 0 0 3 8 14

HI 0 0 0 0 0 0 0 0 0 0

IA 40 51 39 34 51 55 55 66 115 127

ID 2 3 1 1 4 4 1 4 12 13

IL 26 126 86 69 126 127 118 126 235 254

IN 11 18 14 12 18 18 18 18 45 44

KS 0 0 0 0 0 0 0 1 3 6

KY 1 1 1 1 1 1 1 2 4 7

LA 0 0 0 0 0 0 0 4 10 18

MA 72 139 114 136 139 157 186 182 277 308

MD 6 18 8 3 18 18 15 23 45 47

ME 1 1 1 1 1 1 2 1 2 3

MI 41 87 74 74 87 95 99 99 138 149

MN 36 40 33 33 40 48 52 47 63 69

MO 7 20 9 4 20 21 17 26 52 55

MS 0 0 0 0 0 0 0 1 2 4

MT 0 0 0 0 0 0 0 1 1 2

NC 1 1 1 1 1 2 2 3 6 11

ND 0 0 0 0 0 0 0 0 1 1

NE 0 0 0 0 0 0 0 1 2 4

NH 4 15 15 18 15 18 21 17 26 30

NJ 126 101 78 72 101 83 99 156 247 273

NM 2 4 1 1 5 5 1 5 15 16

NV 3 6 1 1 6 7 1 7 21 23

NY 39 158 135 160 158 178 211 158 371 410

OH 32 68 38 24 68 69 61 84 152 156

OK 0 0 0 0 0 0 0 2 6 10

OR 23 40 32 28 40 45 45 41 62 70

PA 13 95 53 63 95 103 122 132 266 287

RI 5 13 10 12 13 15 17 17 31 34

SC 0 0 0 0 0 0 1 1 3 5

SD 1 4 2 1 4 4 3 5 9 9

TN 0 0 0 0 0 0 0 2 4 7

TX 2 2 2 2 2 3 3 10 22 39

UT 36 34 23 16 34 34 20 36 65 72

VA 3 3 3 3 3 4 5 5 9 14

VT 2 4 3 4 4 4 5 4 7 8

WA 29 14 3 3 15 17 3 18 49 53

WI 24 21 25 30 21 25 30 104 122 132

WV 0 0 0 0 0 0 0 1 1 3

WY 0 1 0 1 2 1 1 2 7 8

Total 838 1,313 957 982 1,335 1,376 1,458 1,721 2,968 3,346

* Source of 2010 spending data: CEE (201 2 ) , with minor modifications