## Demand - S ide Energy Efficiency Technical Support Document August 2015ii 

### Contents **Page List of Figures**  **................................ ................................**  **................................ ................................**  **. v**  **List of Tables ................................**  **................................ ................................**  **................................ ..**  **v 1.**  **Introduction ................................**  **................................ ................................**  **............................. 1**  **2. Background**  **................................ ................................**  **................................ .............................**  **1** 2.1 EE Technologies and Practices ................................ ................................ ................ 2 2.2 Barriers to EE Investment ................................ ................................ ........................ 3 2.3 EE Policies ................................ ................................ ................................ ............... 5 2.3.1 Objectives and Role in Reducing CO 2 Emissions from the Power Sector ................................ ................................ ................................ .......... 5 2.3.2 Policy

Ty pes ................................ ................................ ................................ 7 **3. History of EE Policies in the United States**  **................................ ................................**  **.......... 9**  **4. EE Programs**  **................................ ................................**  **................................ .........................**  **15** 4.1 EE Programs ................................ ................................ ................................ .......... 15 4.1.1 Administrators ................................ ................................ ........................... 16 4.1.2 Po licy

Drivers ................................ ................................ ........................... 16 4.1.3 Funding Sources ................................ ................................ ........................ 17 4.2 The Demand - Side EE Opportunity ................................ ................................ ........ 17 4.2.1 Rapid Growth in Demand - Side EE ................................ ........................... 17 4.2.2 EE Program Impacts ................................ ................................ ................. 18 4.2.2.1 Impacts to - date ................................ ................................ .................... 18 4.2.2.2 Projected Impacts ................................ ................................ ................ 22 4.2.3 EE Potential ................................ ................................ .............................. 23 4.2.3.1 Ev aluations of EE Potential ................................ ................................ 23 4.2.3.2 Overview of Results ................................ ................................ ............ 25 4.3 Costs and Cost - Effectiveness of State EE Policies ................................ ................ 26 4.3.1 EE Cost - Effectiveness ................................ ................................ .............. 26 4.3.2 Costs of Saved Energy ................................ ................................ .............. 28 **5. Other EE Strategies**  **................................ ................................**  **................................ ..............**  **30** 5.1 Bu ilding Energy

Codes ................................ ................................ .......................... 30 5.1.1 Overview ................................ ................................ ................................ ... 30 5.1.2 Authority

/ Obligated Parties ................................ ................................ .... 31 5.1.3 Building Code Regulations Over Time ................................ ..................... 31 5.1.4 Remaining Market Potential ................................ ................................ ..... 32 5.2 State Appliance Standards ................................ ................................ ..................... 33

### Contents **(Continued) Page** 

iii 5.2.1 Overview ................................ ................................ ................................ ... 33 5.2.2 Authority

/ Obligated Parties ................................ ................................ .... 33 5.2.3 Appliance Standards over Time ................................ ................................ 34 5.2.4 Remaining Market Potential ................................ ................................ ..... 35 5.3 Energy

Service Performance Contracting ................................ .............................. 36 5.3.1 Overview ................................ ................................ ................................ ... 36 5.3.2 History of ESPC Programs and the ESCO Business Model ..................... 37 5.3.3 Remaining Market Potential ................................ ................................ ..... 38 5.4 Volt/VAR Optimization ................................ ................................ ......................... 39 5.4.1 Overview ................................ ................................ ................................ ... 39 5.4.2 History of VVO Programs ................................ ................................ ........ 41 5.4.3 VVO Market Potential ................................ ................................ .............. 42 **6. Utility Regulatory Models for Supporting Demand**  **- Side EE**  **................................ ...........**  **42 7.**  **Energy Efficiency Certificates ................................**  **................................ .............................**  **45 8.**  **Demand -**  **Side EE Plan Scenario: Magnitude and Timing of Savings ..............................**  **48** 8.1 Approach ................................ ................................ ................................ ................ 48 8.2 Inputs ................................ ................................ ................................ ...................... 49 8.2.1 Step 1: Dete rmine Current L evel of Demand - Side EE Performance ....... 49 8.2.2 Step 2: Determine EE Plan Scenario Level of Performance ..................... 50 8.2.3 Step 3: Determine Start Year for EE Plan Scenario ................................ . 53 8.2.4 Step 4: Determine Start Year Level of Performance ................................ 53 8.2.5 Step 5: Determine Pace of Improvement from Start Year to EE Plan Scenario Level of Performance ................................ ................................ . 53 8.2.6 Step 6: Determine Average Portfolio Measure Life and Distribution of Measure L ives ................................ ................................ ....................... 54 8.2.7 Step 7: Determine Sustainability of EE Plan Scenario Level of Performance ................................ ................................ .............................. 56 8.2.8 Summary

of the Demand - Side EE Plan Scenario Construction ............... 57 8.3 Calculations ................................ ................................ ................................ ............ 58 8.3.1 Step 1: Determine Annual Business as Usual (BAU) Sales ..................... 58 8.3.2 Step 2: Determine Annual Incremental EE Savings as a Percentage of Sales ................................ ................................ ................................ .......... 59 8.3.3 Step 3: Determine Annual Incremental EE Savings (GWh) ..................... 59 8.3.4 Step 4: Determine Annual Expiring EE Savings (GWh) .......................... 60 8.3.5 Step 5: Determine Net Cumulative EE Savings (GWh) ........................... 60 8.3.6 Step 6: Determine Sales after Net EE (GWh) ................................ ........... 60

### Contents **(Continued) Page** 

iv 8.3.7 Step 7: Determine Net Cumulative EE Savings as a Percentage of BAU Sales ................................ ................................ ................................ . 61 8.3.8 Summary

of Calculation Formulas and Results by Step for South Carolina ................................ ................................ ................................ ..... 61 8.4 Results ................................ ................................ ................................ .................... 62 8.4.1 Summary

of Results ................................ ................................ .................. 62 8.4.2 Results in Context ................................ ................................ ..................... 64 **9. D**  **emand -**  **Side EE Plan Scenario: Assessment of Costs ................................**  **...................... 65**  9.1 Approach ................................ ................................ ................................ ................ 65 9.2 Inputs ................................ ................................ ................................ ...................... 66 9.2.1 Step 1: Dete rmine Incremental Electricity Savings ................................ .. 66 9.2.2 Step 2: Determine First - Year Program Costs and Cost Factors ............... 66 9.2.3 Step 3: Determine the Ratio of Program to Participant Costs .................. 68 9.2.4 Summary

of Inputs for Demand - Side EE Cost Analy sis .......................... 69 9.3 Calculations ................................ ................................ ................................ ............ 70 9.3.1 Step 1: Calculate Annual First - Year Costs ................................ ............... 70 9.3.2 Step 2: Calculate Levelized Cost of Saved Energy ................................ .. 71 9.3.3 Step 3: Calculate Annualized Costs ................................ .......................... 71 9.3.4 Summary

of General Formulas and Results by Step for South Carolina ................................ ................................ ................................ ..... 72 9.4 Results ................................ ................................ ................................ .................... 73 9.4.1 Summary

of National Results ................................ ................................ ... 73 9.4.2 Results in Context ................................ ................................ ..................... 74 9.4. 3 Costs per Tonne CO 2 Reduced ................................ ................................ . 74 **10. Analysis Considerations**  **................................ ................................**  **................................ .......**  **75** 10.1 State EE Policies in the Baseline Electricity Demand ................................ ........... 75 10.2 Energy

Information Administration Form EIA - 861 as Data Source ..................... 76 **References ................................**  **................................ ................................**  **................................ .....**  **84** 

### Appendices Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009 - 2014) ................................ . 79 Appendix 2: Incremental Electricity Savings Pace of Improvement Analysis ................................ ........... 80 Appendix 3: Demand - Side Energy Efficiency Plan Scenario: Data, Calculations and Results .................. 83v 

### List of **Figures Page**  Figure 1. EE Program Portfolio ................................ ................................ ................................ .................. 16 Figure 2. Residential Energy Building Code Adoption (2005 to 2014) ................................ ...................... 32 Figure 3. Remaining Market Potential for ESCOs ................................ ................................ ...................... 39 Figure 4. Hypothetical Voltage Profiles with and without VVO Technologies ................................ ......... 41 Figure 5. The Four - Tier Distribution of Demand - Side EE Program Measure Lifetime ............................. 55 

### List of **Tables Page**  Table 1. Relative Opportunities Provided by Key EE Programs and Building Codes ................................ . 9 Table 2. U.S. Elec tric Utility EE Program Funding (2006 - 2013) ................................ ............................... 18 Table 3. U.S. State Adoption of EE Resource Standards ................................ ................................ ............ 18 Table 4. Aggregate Electricity Savings by State, Tribal Area and Territory ................................ .............. 20 Table 5. Summary of Impacts: Scenarios of Future Utility Customer - Funded Electric EE Programs ...... 23 Table 6. Summary of EE Potential Studies (2009 to 2014) ................................ ................................ ........ 26 Table 7. Estimated Energy Savings Potential of State Appliance Standards ................................ .............. 35 Table 8. Summary of State Regulatory Frameworks: December 2014 ................................ ....................... 43 Table 9. International Examples of Trading Energy Savings ................................ ................................ ..... 47 Table 10. 2013 State Levels of Incremental Annual Savings ................................ ................................ ..... 51 Table 11. 2013 Incremental Annual Savings

– Investor- Owned Utilities ................................ .................. 51 Table 12. 2013 Incremental Annual Savings

– Cooperative - Owned Utilities ................................ ............ 51 Table 13. 2013 Incremental Annual Savings

– Municipal - Owned Utilities ................................ ............... 52 Table 14. Levels of Incremental Savings Required by State EERS ................................ ............................ 52 Table 15. NPCC Achievable EE Potential and Achieved Incremental Savings (1998 - 2010) .................... 57 Table 16. Summary of Demand - Side EE Plan Scenario Inputs ................................ ................................ .. 57 Table 17. BAU Sales for South Carolina ................................ ................................ ................................ .... 59 Table 18. Annual Incremental EE Savings as a Percentage of Sales for South Carolina ........................... 59 Table 19. Annual Incremental EE Savings (GWh) for South Carolina ................................ ...................... 59 Table 20. Expiring EE Savings for South Carolina ................................ ................................ .................... 60 Table 21. Net Cumulative EE Savings for So uth Carolina ................................ ................................ ......... 60 Table 22. Sales after Net EE Savings for South Carolina ................................ ................................ ........... 61 Table 23. Net Cumulative EE Savings as a Percentage of BAU Sales for South Carolina ........................ 61 Table 24. Summary of Results by Step for South Carolina ................................ ................................ ........ 61 Table 25. Demand - Side EE Plan Scenario: Magnitude and Timing of Net Cumulative Savings ............. 62

### List of Tables **(Continued) Page** 

vi Table 26. Average Annual Savings Rates of Demand - Sid e EE Plan Scenario ................................ ........... 64 Table 27. Summary of EE Cost Analysis Inputs ................................ ................................ ........................ 69 Table 28. Calculation of Annual First - Year Costs for South Carolina ................................ ....................... 70 Table 29. Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) ................................ 71 Tabl e 30. Annualized Costs for South Carolina ................................ ................................ .......................... 72 Table 31. Summary of Results by Step for South Carolina. ................................ ................................ ....... 72 Table 32. First - Year Demand - Side EE Costs (billions 2011$) (Continental U.S.) ................................ .... 73 Table 33. Annualized Demand - Side EE Costs (3% discount rate, billions 2011$) (Continental U.S.) ...... 73 Table 34. Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.) .............. 73 Table 35. Annual Growth Rates of Demand - Side EE Program Spending

– Historic and Demand Side EE Plan Scenario ................................ ................................ ................................ ................................ 741



### 1. **Introduction** This chapter provides information on demand - side energy efficiency

(EE) as a measure for reducing carbon dioxide (CO 2 ) emissions from fossil fuel - fired electric generating units (EGUs). Specificall y, this chapter supports the discussion of demand - side EE as a non - BSER compliance measure in section V, “The Best Sy stem of Emission Reduction (BSER) and Associated Building Blocks,” and section VIII, “State Plans,” of the preamble. In addition, support is provided for the magnitude, timing, and cost of demand - side EE measure s included in the illustrative demand - side EE plan scenario developed as a basis for the Regulatory Impact Analy sis (RIA) and related discussion in section X, “Impacts of this Action,” of the preamble. Th e chapter is organized as follows: 2. Background 3 . History of EE Policies in the United States 4 . EE Programs 5 . Other EE Strategies 6. Utility Business Models 7. Experience with EE Certificates 8. Demand - Side EE Plan Scenario: Magnitude and Timing of Savings 9. Demand - Side EE Plan Scenario: Assessment of Costs 10. Analy sis Considerations Sections 2 through 7 provide support for both the preamble sections cited above as well as background for the illustrative demand - side EE plan scenario presented in sections 8 and 9. Sections 8 and 9 provide details on the approach, inputs, calculations, and results for the magnitude and timing of savings and the assessment of associated costs for the demand - side EE plan scenario. Lastly , section 10 discusses considerations relevant to sections 8 and 9. 

### 2. **Back ground**  D emand - si de EE policies and programmatic efforts have existed for decades and are now used in all 50 states. These strategies are intended to help states achieve energy savings goals,2

reduce the environmental impacts (including CO 2 emissions) of meeting energy serv ice needs, save energy

and money

for consumers, and provide a significant resource for meeting power sy stem capacity requirements (EPA 2015) . EE policies currently in place are generally considered b y states to be cost - effective strategies for contributing to these policy objectives. 1 Moreover, states

– through their utilities, primarily

– have been rapidl y increasing their funding of EE programs in recent years, more than tripling budgets in the five years from 2006 to 2011, from $1.6 billion to $5.9 billion (ACEEE 2014c) . In 2012, th e utility- reported cumulative impacts of these programs represented a 3.7% reduction in national electricity demand (EIA 2012a) . And, EE spending is projected to continue to grow at a substantial rate. A recent study by L awrence Berkeley

National Laboratory (LBNL) projects EE program spending to reach $8.1 billion to $12.2 billion (“Medium Case” and “High Case,” respectivel y) in 2025 ( Barbose et al. 2013 ) . **2.1 EE Technologies and Practices**  EE is using less energy

to provide the same or greater level of service . Demand - side EE refers to an extensive array of technologies, practices and measures that are applied throughout all sectors of the economy to reduce energy

demand while providing the same, or better, level and quality

of service. Utilities employ an array of strategies in implementing EE programs, these include financial incentives such as rebates and loans, technical services such as audits and retrofits, and educational campaigns about the benefits of EE improvements. The purpose of these EE programs is to induce EE investments and practices that would not otherwise occur due to market failures and behavioral impediments. In the residential sector, examples of EE activities include the purchase of more efficient products and equipment (e.g., ENERGY STAR labeled), the upgrading of insulation in attics and walls, sealing of air leaks, and undertaking home energy

audits leading to customized whole home retrofits. Opportunities for cost - effective EE in commercial buildings can include optimization of heating, ventilation, and air conditioning (HVAC) s ys tems, upgrades of windows, and use of more efficient office equipment at replacement. In the industrial sector key EE strategies include motor upgrades and maintenance programs, recovery

of waste heat streams, and optimization of processes through modern instrumentation and controls sy stems.

1 S ee below for discussion of cost - effectiveness and related cost tests used by states to evaluate EE programs.3

The opportunity

presented for economic investment in EE is dy namic, growing over time as technologies and practices advance, as populations grow, and as investment occurs in the construction of new homes, buildings, and industrial facilities. After decades of experience implementing policies to accelerate investment in cost - effective EE , states are finding renewed opportunities as they

develop more sophisticated and effective strategies, evolving from a focus on individual end - uses and products to whole - building and s ys tems - based strategies that account for the interactions between the many energy end - uses in buildings and industry (ACEEE 2013) . As will be discussed, the experi ence in the U.S. has been that , on balance, a persistent and large potential for achievable and cost - effective EE is expected to remain even as the impact of past and ongoing efforts have accumulated. **2.2 Barriers to**  **EE Investment**  Despite the persistent and large potential for electricity savings through investment in EE technologies and practices, market failures, as well as non - market barriers and behavioral impediments , limit the realization of the many benefits of these investments. Several market failures that lead to inefficiencies in the investment in energy efficiency are widely recognized by anal ys ts and practitioners, and are discussed extensively in the literature (Levine et al. 1995; Gillingham et al. 2009; Gerarden et al. 2015 ) . In the presence of market failures, users of electricity, or those making EE investments, face prices or incentives that prevent them from weighing the full social benefits and costs of their investments and thus under - invest in approaches to reduce electricity consumption. Th e behavioral impediments provide another explanation for why

individuals do not alway s make EE investments that are seemingly in their own best interest . Some of the most common examples of these market failures include:

 Pollution externalities. Fossil e nergy consumption is associated with negative externalities, such as emissions of CO 2 , SO 2 , and NO x that cause human health and environmental damages. Energy prices that do not correctly reflect the externalities associated with energy

use would lead to investments in EE below the socially optimal levels.  Imperfect information. Energy users often lack accurate information about energy savings and other attributes of energy efficient products or practices to understand the costs and benefits of EE investments.4

 Split incentives (or the “principal- agent problem”). In centives of individuals who make EE investment decisions are not alway s aligned with incentives of those who use and pay

for energy . Examples include misalignment between landlords and t enants, between builders and homeowners , and within organizations and institutions (DeCanio 1998; McKinsey 2007) .  Credit constraints . L imited access to credit may prevent some consumers, especiall y low - income consumers, from making cost - effective EE improv ement with higher upfront cost.  Under-

provision of research and development (R&D). Because of the public good nature of knowledge, firms involved in technology development may be less willing to invest in R&D, leading to sub - optimal levels of EE investments from a social perspective (Jaffe et al. 2003) .  Supply market imperfections. Market s for energy efficient products are often incomplete and fragmented, leading to underinvestment in innovation and limited EE supplied by

the manufacturers . In addition, su pply chain fragmentation may also add complexity

to the purchase and installation of otherwise economicall y rational investments, thereb y slowing the adoption of EE technologies (Fischer 2005) .  Behavioral impediments . Behavioral economics and psychology have identified potential behavioral phenomena that lead to consumers to deviate from the standard theory of welfare maximizing in consumption and other decisions, including EE investments (Gillingham and Palmer 2014) . O ther factors, such as hidden costs, ri sk and uncertainty experienced by

both consumers and suppliers of energy

efficient products, and heterogeneity among consumers, may also influence EE investment decisions. 2 Examples of such barrier s include:

 Risk and uncertainty . Adopting an unfamiliar, ty pically more expensive EE technology

can be an uncertain undertaking given the lack of credible information on

2 It has been recognized that there is a difference between cost - effective energy efficiency investment levels, based on cost minimizing consideration, and observed levels of energy efficiency. This phenomenon, also termed ‘energy paradox,’ or ‘energy efficiency gap,’ has been discussed extensively in the academic literature , although a full explanation of the gap remains elusive, likely because it differs by setting and energy efficiency inves tment . See, for example, Jaffe and Stavins 1994, DeCanio 1998, Sanstad and Howarth 1994, DeCanio and Watkins 2008, Allcott and Greenstone 2012 , and Gerarden et al. 2015.5 product performance and future energy prices, and the irreversibility of the investment. Imperfect or asy mmetric information can exacerbate the p erceived risk of EE investments and help explain why consumers and firms do not alway s invest in EE measures. Suppliers also face risk and uncertainty , without perfect information of consumer preferences for EE . In the presence of risk and uncertainties, c onsumers and suppliers alike will invest less in EE.  Transaction costs . Consumers face transaction costs in searching, assessing and acquiring energy

efficient technologies and services. It can be time - consuming and difficult for consumers to estimate lif etime operating costs of a product. The complexity

of the search process may put m ore efficient products at a disadvantage relative to better - known less - efficient products with , sometimes, lower upfront costs.  Capital market barriers . Consumers sometimes face higher interest rates to finance EE investments compared to other investments. Lenders can be reluctant to invest in EE loan portfolios in part because EE loans may lack standardization and financial markets have difficulty ascertaining the likel y pay off from such investments. EE policies and programs can play an important role in correcting market failures and addressing the barriers to the investment and adoption of socially beneficial EE opportunities. Examples of effective EE policies and programs include public funding of R&D, information programs (such as energy labeling, the ENERGY STAR p rogram, and consumer education), rebates for high - efficiency products, product energy performance standards, financing and loan programs, and technical assistanc e. **2.3 EE Policies**  **2.3.1 Objectives and Role in Reducing CO**  **2 Emissions from the Power Sector**  EE policies are implemented by states to meet a number of closel y related policy goals, including:

 Reducing costs to electricity customers,  Providing a significant resourc e for meeting power sy stem capacity needs,  Meeting energy

savings goals,6

 Stimulating local economic development and new jobs,  Reducing the environmental impacts of meeting electricity service needs , and

 Improving the health of lower - income households (EPA and DOE 2006; EPA 2015) . EE policies currently

in place are considered by states to be cost - effective strategies for contributing to each of these policy objectives (EPA and DOE 2006) . While each of these objectives, and others, contribute to the mot ivation of policymakers and utilities to pursue EE policies and programs , reducing energy costs to consumers over the long term is a primary purpose in pursuing these policies. In addition, EE policies are central to meeting state objectives for reducing C O 2 emissions from the power sector. EE policies are a leading tool for achieving CO 2 reductions from power plants, accounting for 35% to 70% of reductions of sector emissions in ten states with statutory requirements for greenhouse gas reduction s (EPA 2015 ). 3 Many evaluations of the economic potential for carbon dioxide reductions from the United States’ power sector identify

demand - side EE as the lowest cost strategy (typically, as noted above, with positive net present value) as well as the strategy havin g the greatest reduction potential. For example, McKinsey

found that EE accounted for more than 60% of their mid - range potential for greenhouse gas reductions from the U.S. power sector and that it was available at positive net present value if “persistent barriers to market efficiency” could be addressed (McKinsey 2007) . Similarly , e conom y- wide studies of climate mitigation scenarios confirm that EE can play a critical role in reducing the costs and enhancing the flexibility of meeting long - term climate s tabilization targets (Clarke et al. 2014; Kriegler et al. 2014) . An analy sis by the International Energy

Agency (I EA) suggested that in order to stabilize the carbon concentration in the atmosphere at 450 ppm, as much as 44% of the estimated global abatement potential in 2035 derives from greater EE in the world economy (IEA 2012b) . Several recent Energy Modeling Forum (EMF) studies have investigated the role of technology in achieving climate policy objectives in the U.S. (“EMF 24” and “EMF 25” studies) an d globally (“EMF 27” study). 4 These studies concluded that compared to business - as - usual EE , improvements in EE in various

3 States with GHG reduction laws include: California, Connecticut, Hawaii, Maine, Maryland, Massachusetts, Minnesota, New Jersey, Oregon, and Washington. 4 Energy Modeling Forum (EMF) is a consortium of energy economists and energy econ omic modeling teams that was established in 1976. Through ad hoc working groups, the EMF has focused on a series of energy and environmental topics that are of interest to policy decisions. In recent years, the EMF is recognized for its contribution to the advancement of economics of climate change and the reports of the Intergovernmental Panel on Climate Change (IPCC).7

economic sectors would slow the increases of GHG emissions in the short run, substantially reduce the costs of GHG mitigation (on ave rage, by about 50%), and ease the technology transformation pathways to achieve long - term carbon reduction goals (Kriegler et al. 2014; Kyle et al. 2011) . Several economic studies (including EMF25 studies) examined the role of EE policies (such as EE stand ards and subsidies) in relation to other climate policy instruments (such as carbon taxes). These studies found that when EE policies address market failures, they are welfare improving and can complement climate policy (Comstock and Boedecker 2011; Fischer 2005; RFF 2010) . In addition, EE policies are recognized to be an appropriate response to demonstrated market failures and behavioral impediments, particularl y in contexts where these failures have broader societal implications such as environmental externalities (Gillingham et al. 2009) . In addition to providing cost - effective opportunities for reducing GHG emissions, EE is recognized to provide other co - benefits, including air quality and public health benefits, wast e reduction from energy

generation, energy

security , energy

s ys tem reliability, community economic and social development, and consumer amenities (RAP 2012) . EE investments and policies that apply

to industry

have been found to spur productivity growth, te chnology

learning and innovation (Bo yd and Pang 2000; Worrell et al. 2003). Recentl y, more attention has been paid to developing methods for recognizing these co - benefits and integrating them into the cost benefit anal ys is framework used by state utility

c ommissions and administrators of EE programs. These co - benefits have not been fully accounted for in the EPA anal ys is. **2.3.2 Policy Types**  EE policies come in many forms. The most prominent and impactful EE policies in most states include those that drive development and funding of EE programs , and building energy codes. Other policies that are leading to significant impacts in some states include state appliance and equipment standards, “lead b y example” strategies targeting energy use in state operations through energy

services performance contracting, and “volt - VAR optimization.” See section 5 for further description of these policies, their history, and potential for achieving significant electricity savings. Comparing the relative impact (potential or achieved) of the different policy ty pes is challenging, particularly to do so comprehensively , across all states, and at the national8

level. EE programs are the only state EE approach that has comprehensive and detailed reporting of impacts, costs, and oth er characteristics from all 50 states. 5 This information is generall y based upon measurement and verification studies submitted annually , most commonly to state utility commissions, and reported to the Energy Information Administration (EIA) for all program administrator types ( including all utility ty pes, third - parties, and government agencies). EE program data reported to EIA includes : incremental savings (also referred to as “first - ye ar” savings), cumulative savings, peak demand savings, program costs broken down by component, and composition by

end - use sector (residential, commercial, industrial). As of 2013 EIA ended their collection of cumulative savings data. In 201 3 , utilities and other program administrators in all 50 states and the District of Columbia states reported savings from EE programs to EIA through form EIA - 861. At a national level, the EPA is not aware of a comprehensive dataset reported b y states of the achieved impacts of strategies other than those that lead to investment in EE programs . However, state and regional - level information does exist. For example, the Northwest Power and Conservation Council (NPCC) has been compiling the impacts of EE policies (including utility and third - party EE programs, state building energy codes, and federal appliance standards) across their member states (ID, MT, OR, WA) for more than three decades. For the past decade, EE programs have accounted for more than 75% of the cumulative energy savings from state EE policies for NPCC, with building energy codes accounting for the remaining savings (NPCC 2010) . Another representation of the relative opportunity provided by

different state EE strategies is presented b y evaluations of EE achievable potential or projections of the impacts of EE policies. The result s from two recent evaluations at a national level are presented in Table 1. EE programs account for 77% and 82% of achievable savings in studies by ACEEE and Georgia Tech, respectivel y (ACEEE 2014a; Wang and Brown 2013) . These studies indicate that the sub stantial majority

of potential savings from state EE efforts are available through EE programs, and that state and local building energy codes can make a significant additional contribution. Massachusetts provides a state example of the impacts of EE programs relative to other state EE policies. The Massachusetts Global Warming Solutions Act of 2008 established

5 In 2011, EIA began collecting data from third - party administrators of programs . Prior to 2011, this was a significant shortcoming in the breadth of the data collected . The breadth and quality of information collected through Form EIA - 861 has improved over time, however, outside entities (e.g., ACEEE and CEE ) have found that the data can be improved through expert review and supplementation with other data sources . While now fairly comprehensive, the EIA data can be improved further with regards to data quality and consistency. See section 10 for further discussion.9

statewide limits on greenhouse gas (GHG) emissions of 25 percent below 1990 levels by 2020. To achieve this target, Massachusetts is rel yi ng upon an integrated portfolio of clean energy policies. State EE policies are expected to provide the largest contribution to meeting the 25 percent target with utility - sponsored EE programs and state building energy codes accounting for 76% and 17%, respectively , of those policies (Massachusetts EOEEA 2010) . In their 2013 progress report, Massachusetts indicates that they are generally

on track for meeting or exceeding these projections (Massachusetts EOEEA 2013) . **T able**  **1 .**  **Relative Opportunities Provided by Key EE Programs and Building Codes**  **Study Year**  **EE Programs Building Codes**  **Other ACEEE**  2030 77% 13% 10% **Georgia Tech** 2035 82% 18% 0% Source: ACEEE 2014a, and Wang and Brown 2013 The next section reviews the length y history of EE policies in the U.S. In the following section, the focus returns to the EE programs highlighted here for an in - depth review of this important strategy . 

### 3. **History of EE Policies in the United States** Demand - side EE has been repeatedly supported through federal policies and market innovations over the past four decades , in recognition of its multiple benefits, including increased energy

security, reduced emissions from the power sector, lower customer energy bills, enhanced economic development and job creation, and improved reliability and resiliency

of the electricity s ys tem. Following the 1973 Organization of Petroleum Exporting Countries (OPEC) oil embargo, the Nixon administrated established the Federal Energy Administration, imposed oil price regulations, and created Project Independ ence to put the U.S. back on the path toward improved energy

security. The 94 th Congress took the first major U.S. legislative action on energy through the 1975 Energy Policy and Conservation Act (EPCA). Among other provisions, EPCA authorized the creatio n of vehicle fuel economy standards, and labeling requirements and efficiency standards for major household appliances (Congress 1975 ). Also i n 1975, the American Society of Heating, Refrigerating, and Air - conditioning Engineers (ASHRAE) developed the firs t national10

model building energy

code, 90 - 75 “Energy

Conservation in New Building Design,” for residential and commercial buildings (BCAP n.d.) . Continuing this trend of energy policy initiatives, Congress passed the Energy Conservation and Production Act in 1976, which authorized the creation of mandatory national building energy

performance standards (BEPS) . BEPS, which were repealed in the 1980s, provided loan guarantees for saving energy in public and commercial buildings, authorized grants to states fo r low - income weatherization and state energy conservation programs, and encouraged the development of electric utility rate structures designed to save energy . Federal energy

efficiency policy

action continued during the Carter administration. President Carter ’s 1977 National Energy Plan included energy

efficiency as “the quickest, cheapest, and m ost practical source of energy In the same year President Carter also issued the first executive order on energy efficiency , Energy

Policy and Conservation E.O. 12 003 requiring federal agencies to develop energy conservation plans ( Woolley and Peters n.d. ) . Other policy actions in 1977 included the creation of the U.S. Department of Energy , thereby consolidating multiple federal agencies and programs, including many that support energy

efficiency , into one cabinet - level department. In 1978, partl y in response to the Administration’s new energy plan, Congress enacted the National Energy

Act, inclu ding the National Energy Conservation Policy

Act (NE CP A) , which made EPCA’s provisions for federal efficiency standards mandatory and pre - emptive of state law; established the Residential Conservation Service (RCS), which required utilities to provide home energy

audits; and authorized the Institutional Conservation Program (ICP), a grant program for schools and hospitals . NE CP A also provides the core authority for the federal government’s energy

management goals and requirements, which have been updated an d amended since 1978 (Congress 1978a) . Congress also passed the Public Utility Regulatory Policies Act (PURPA) in 1978 which, among other provisions, introduced competition into generation markets, and required state utility commissions to consider ratemaking practices that encourage energy

efficiency

(Congress 1978b). As state commissions pursued PURPA - related requirements as well as their own related policy interests, integrated resource planning became more widel y used, and the concept of demand - side man agement (DSM) emerged, in which11

utilities implement programs to influence customer loads through energy efficiency

and other demand - side initiatives (RAP 2011). The energy

service company

(ESCO) business model also emerged during the 1970s and 1980s. L eade rs in the Federal Energy Administration, such as Roger Sant, defined the term “energy services” in the 1970s as the services people want from energy , such as lighting and comfort, and distinct from energy commodities such as fuels and electricity . This con cept gave rise to the energy

services business, in which ESCOs could reduce energy service costs through energy efficiency . The ESCO industry ’s growth was supported b y utilities paying ESCOs for services such as utility- supported energy audits, and by

help ing federal agencies implementing federal energy

management goals (NAESCO 2011) . ESCOs also succeeded in contracting with state and local and institutional customers, whose limited ability to finance energy

improvements matched up well with ESCOs’ ability to bring private capital to energy improvement projects. Federal policy

support for energy

efficiency declined dramatically in the 1980s under the Reagan Administration as energy supplies improved, prices stabilized , and a less favorable climate for feder al regulation emerged. The BEPS and RCS programs were repealed, and a “no standard” standard was issued under NECPA appliance standards authority. Some states acted to fill these policy

vacuums, enacting their own building codes, appliance standards, and utility programs. Appliance manufacturers, concerned about the proliferation of multiple, different state standards, supported a compromised effort that resulted in the 1987 National Appliance Energy Conservation Act (NAECA) (Congress 1987 ) . NAECA, which was further amended in 1988 , established the first national home appliance efficiency standards by

amending NECPA . During the 1980s, state and local residential and commercial building energy codes were also adopted to achieve greater energy savings in new constructio n (BCAP n.d.) . As integrated resource planning and DSM practices continued to spread in the 1980s , and electric utilities began to offer customers DSM programs under formal regulatory processes , including programs to incentivize more efficient technologies and practices. These DSM programs sometimes resembled utility conservation programs launched in the 1970s, but signified a larger shift to integrate energy efficiency

into broader utility planning and operations (ACEEE 2012b) . The rapid growth of DSM in the 1980s was the result of the 1970s’ increased energy prices, energy

security concerns, and the environmental consequences of electricity12

generation. Rising power plant capital and fuel cost s resulted in rate increases, rising customer dissatisfaction, and new requirements for utilities to pursue IRP to manage electricity demand, supply and risk (LBN L 1996) . By the end of the decade, 41 states had made some progress towards implementing a rul e to require integrated resource planning b y electric utilities (Sy napse 2011) . Utilities also played a key role in supporting ESCO projects into the 1990s by providing incentives that offset energy efficiency

project costs , as integrated resource plans fo und that investments in energy

efficiency

were lower - cost alternative to new power plants. The 1990s began with the Clean Air Act Amendments and the Pollution Prevention Act of 1990 , each of which in their own way s recogniz ed the role energy efficiency can play

in protecting human health and the environment. The Clean Air Act amendments provided the authority to use demand - side energy efficiency as a compliance option that gives utilities flexibility to obtain needed sulfur dioxide emission reductions through programs that encourage customers to conserve energy . In 1992, EPA introduced the ENERGY STAR label for office products which manufacturers voluntaril y design to perform at superior levels of energy savings. President Clinton signed Executive Order 1 2845 in 1993 requiring federal agencies to purchase ENERGY STAR qualified products. The Pollution Prevention Act gave EPA new authority to reduce emissions via voluntary and other approaches that reduce pollution sources. T he Energy

Policy Act (EPAct) of 1 992 also advanced energy efficiency

through a broad suite of policies, including federal appliance and equipment efficiency standards, requirements for states and the Department of Energy on model building codes, new requirements for federal energy managem ent, new provisions on least - cost planning for federal electric utilities, and new research and development authorizations . EPAct 1992 also included provisions to encourage states to adopt policies to support energy savings through electric utility regulat ory

reforms ( Congress 1992) . In 199 6 , DOE and EPA signed a joint Memorandum of Understanding to cooperate in delivering the growing suite of ENERGY STAR program s , increasing the number of products and markets covered and leveraging expertise and resources across the federal government. During the early

1990s, electric utility DSM programs grew rapidly , with energy savings exceeding 50 billion kilowatt - hours (kWh) and utility spending at $2.7 billion in 1994 (A CEEE 2000) . About 95 percent of the reported energy savings, though only

slightly over half of the13

spending, was attributed to programs reported as targeting energy efficiency (ACEEE 2000) . These utility

programs accelerated the commercialization of advanc ed energy efficiency technologies, often b y partnering with federal energy efficiency programs like ENERGY STAR (ENERGY STAR 2012a, 2012b) . By the late 1990s, funding for utility- sponsored energy efficiency programs fell to less than one - third of its 1994 peak, as about half of the states from a wave of electricity

restructuring policies, accompanied by increased political and regulatory pressures to hold down electricity prices (EPA and DOE 2006) . Some restructured electric utilities shifted their focus away from regulated DSM programs to unregulated power marketing and ESCO businesse s to deliver energy sales and services directl y to customers. However, the business model for these market - based ESCOs limited their ability to reach all but the largest indus trial, commercial and institutional customers. Recognizing the limits of private - market delivery of EE, and seeking to preserve the benefits of EE programs, as the more states restructured their electricity markets in the 1990s, some 20 states adopted publ ic benefit fund s (PBFs) to ensure continuation of ratepayer - funded programs. Several states and local governments also expanded the authorized use of ESCO services in public facilities via lead - byexample policies. Following the ye ar 2000, energy efficienc y policies and programs gained increased attention and, supported by decades of implementation experience, began to be widel y recognized as a legitimate, measureable, and low - cost option to serve the nation’s energy and environmental goals . Congress displaye d a strong commitment to energy efficiency

in the Energy Policy Act ( EPAct) of 2005 , which included sweeping energy efficiency

provisions : it authorized $250 million over five ye ars to provide energy efficiency

appliance rebates, $1. 8 billion over three ye ars for weatherization services, included direct enactment of federal efficiency standards, and authorized a national consumer education campaign on energy efficiency ( Congress 2005) . At the same time , however, the ESCO market declined due to an authori zation lapse for the federal Energy Savings Performance Contracting (ESPC) program and loss of market confidence for industrial financing mechanisms (NAESCO 2011) . This trend was soon reversed as ESCO s increased focus on state and local public buildings and innovated in order to provide customers new technologies and financial models. Eventual resolution of the ESPC authorization program, and e xecutive o rders by both President Bush and President Obama14

h ave also required more aggressive energy savings in federal facilities ( White House 2007, 2009, 2015 ). By

the mid - 2000s, the electric utility and regional electricity markets also solidified the validity of rate - payer funded energy efficiency

programs as s ys tem resources. In 2006, the National Action Plan for Energy Efficiency

L eadership Group of more than 60 leading gas and electric utilities, state agencies, energy consumers, energy

service providers, environmental groups, and energy

efficiency

organizati ons released five policy recommendations recognizing energy efficiency

as a high - priority

energy resource. States, utilities, and key stakeholders across 49 states joined in making aggressive commitments to energy efficiency

and endorsing the Action Plan recommendations (EPA and DOE 2006) . In the ye ars following the Action Plan’s efforts, about half the states enacted Energy Efficiency

Resource Standards (EERS), which are long - term, quantified energy savings targets designed to drive ratepayer - funded program plans. These policy drivers lead to the evaluation, planning, and adoption of EE programs and associated budgets, which are supported through different funding mechanisms. Funding for utility electricity energy efficiency

programs has in turn increased rapidl y, from $1.6 billion in 2006 to $6.3 billion in 2013 (ACEEE 2014c) . At the regional level, the wholesale electricity market operator in New England began to allow energy

efficiency

to participate in regional capacity markets as part of a 2006 settleme nt agreement with their regulator, the Federal Energy Regulatory

Commission (FERC) . Since 2009, energy efficiency providers have been compensated for its measureable contribution to meeting New England’s regional capacity needs, helping to ensure sufficient electricity will be available in the future to reliabl y meet demand across the region. PJM, the wholesale electricity market operator across 13 states around the Mid - Atlantic region, also integrated energy efficiency

into this capacity market rules in 20 09 (Booz Allen 2012) . In 2007, the Energy

Independence and Security Act (EI SA) passed due to growing concerns over both energy prices and climate change. EI SA included several new, and strengthened existing, appliance and equipment standards including a ma jor incandescent lamp efficiency standard, authorized industrial energy efficiency

programs, and included a provision for states to consider adopting policies to support greater energy efficiency. EI SA also enacted the federal agency

savings goals of Presi dent Bush’s 2007 Executive Order 13423, Strengthening15

Federal Environmental, Energy and Transportation Management, which required even more aggressive building energy savings than previous Executive Orders. The American Recovery

and Reinvestment Act of 20 09 (ARRA 2009) recognized energy efficiency for its economic benefits, including job creation, bill savings and economic development. ARRA 2009 allocated DOE around $ 16 billion for energy efficiency

programs, including appliance rebates, grants to local go vernments, state energy programs, weatherization assistance, and smart grid grants ( DOE 2012c) . These investments brought results: for example, ARRA’s weatherization program helped more than 650,000 low - income families nationwide, exceeding the original target of 600,000 homes. These retrofits improved the energy efficiency of the homes, saving families an average of $437 a year on their energy bills (DOE 2012c). 

### 4. **EE Programs 4.1**  **EE Programs** Portfolios of EE programs are comprised of numerous measures and measur e ty pes that are applied across all sectors of electricity end - users. Figure 1 illustrates the multi - level composition and breadth of EE program portfolios (LBNL 2013b) . The diversity represented by a ty pical portfolio of EE programs implemented by a utili ty (or other program administrator) is an important characteristic relevant to anal ys is of EE policies. Every detailed program type (as illustrated in the lower half of the figure) represents a unique set of characteristics including costs of energy

saved, ratio of program to participant costs, investment life, scale, M&V approach, etc. 6

6 See following sections for discussion of these factors.16 **F igure**  **1 . EE**  **Program Portfolio**  **7**

Source: LBNL 2013 b **4.1.1 Administrators**  EE programs are administered b y a variety of entities (“program administrators”) including utilities of all ownership ty pes (investor - owned, municipals, and cooperatives), non profit and for - profit third - parties (e.g., Vermont Energy Investment Corporation), and state and local government agencies (e.g., NYSERDA). Most EE programs (including all investor - owned utilities which account for more than 75% of reported savings 8 ) are overseen by state utility commissions, which review and approve program plans, projected impacts, and associated budgets; and establish annual reporting and M&V requirements (E IA 2013) . **4.1.2 Policy Drivers**  EE programs result from a number of different policy approaches or “drivers.” These include EE resource standards (EERS) (26 states), sy stem benefit charges (14 states), integrated resource planning (IRP) requirements (34 states), demand - side management plan or multi - ye ar 7 The “EM&V” box i s not comparable to the other program types and is not relevant to this discussion. It was included in the referenced source to indicate that EM&V is a key activity within a program portfolio. 8 EP A calculation using EIA - 861 2013 data.17

EE budget (28 states), and statutory requirement to acquire “all - cost - effective EE” (6 states) (ACEEE 2014c; Barbose et al. 2013) . 9 EERS is a more recentl y developed strategy and has quickly become the leading driver of the rapid growth in EE programs due to their clear goals and proven success as a policy tool (ACEEE 2011) . These policy drivers lead to the evaluation, planning, and adoption of EE programs and associated budgets, whic h are supported through different funding mechanisms. **4.1.3 Funding Sources**  Funding sources for EE programs are varied but for most states are dominated by revenues collected from ratepayers through electricity surcharges, ty

picall y ranging from $1 to $4 per meg awatt - hour (ACEEE 2014c) . More recently adopted funding sources include proceeds from the auction of allowances in the Regional Greenhouse Gas Initiative (RGGI) states and from EE resources bid into the forward capacity market operated b y the New England Independent S ys tem Operator (NE - ISO). Ratepayer - funding accounts for more than 90% of total EE program support nationally . **4.2 The**  **Demand -**  **Side EE Opportunity**  As discussed, states are employing a number of EE strategies with EE programs yielding the most signifi cant impacts both historically as well as in terms of future potential. Furthermore, EE programs are unique among state EE strategies in the comprehensiveness and transparency of their reported impacts, funding, and other characteristics. In this section w e address the rapid growth in EE programs, estimated impacts of EE programs to - date and projections of the impacts of existing EE programs and trends, and the electricity savings potential achievable through expanded use of EE policies and programs. Finall y, we will discuss the costs and cost effectiveness of EE programs, specificall y. **4.2.1 Rapid Growth in**  **Demand -**  **Side EE** Funding for EE programs has increased rapidly in recent years driven by recent policy innovations and increasing evidence of the effectiveness of these new strategies. Table 2 presents

9 The number of EERS states is from ACEEE 2014c and includes states with explicit EERS, those with long - term energy savings targets for individual program administrators, and those with EE incorporated as an eligible resource in a renewable portfoli o standard . The numbers for the other policy approaches are from Barbose et al. 2013 .18

levels of EE program funding in the U.S. since 2006 (ACEEE 2014c) . In the previous five ye ars, funding increased b y more than 250%, from $1.6 billion in 2006 to $5.9 billion in 2011.

**T able**  **2 .**  **U.S. Electric Utility EE Program Funding (2006 -**  **201 3**  **) Year**  **2006 2007**  **2008 2009**  **2010 2011**  **2012 2013**  Electric Efficiency Program Budgets (billions of $s, nominal) 1.6 2.2 2.6 3.4 4.6 5.9 5.9 6.3 Source: ACEEE 2014c Key new state policies that have helped to drive these rapid increases in EE program funding include EERS, electricity savings goals, and “all cost - effective EE ” requirements. The adoption of EERS, in particular, increased through this period and clearly has been the primary driving force behind the increasing success of and investment in EE programs. Table 3 shows the number of states adopting EERS b y year (ACEEE 2014 c) .

**T able**  **3 .**  **U.S. State Adoption of EE**  **Resource Standards Year**  **States Adopting an EERS Total**  **1997 -**  **2004** California, Hawaii, Texas, Vermont **4 2005**  Nevada, Pennsylvania **2 2006**  Rhode Island, Washington **2 2007**  Colorado, Connecticut, Illinois, Minnesota, North Carolina **5 2008**  Maryland, Michigan, New Mexico, New Year, Ohio **5 2009**  Arizona, Indiana, Iowa, Maine, Massachusetts **5 2010**  Arkansas, Oregon **2 2011**  Wisconsin **1 1997**  **- 2011**  **26** Source: ACEEE 2014c **4.2.2 EE Program Impacts**  **4.2.2.1 Impacts to**  **- date**  The primary

sources for EE program information (including costs and impacts) are annual EE program reports required b y utility commissions, or cooperative or municipal utility boards of directors. Program impact results provided in these reports are based on e valuation, measurement, and verification (EM&V) procedures and methods used to estimate the electricity19

savings from EE programs within the program portfolio. The EIA has been collecting data on EE programs through Form 861, “Annual Electric Power Industry Report,” for more than three decades. 10 The data collection reflects an increasing degree of breadth and detail over time. For example, third - party- administered programs were not initially required to report but were added beginning in 2011. Data fields have been added over the years to reflect industry trends (e.g., EE programs are now reported separatel y from load management programs). Outside organizations have taken the EIA data, supplemented it with additional sources including survey s of utility commi ssions and program administrators, and published their own annual reports that capture EE program impacts (ACEEE 2014c; CEE 2015) . The EPA has relied on the EIA Form 861 dataset for identifying historic al impacts of EE programs b y state , tribe and territo ry . Specifically , the reported sales data and incremental electricity savings in the 201 3 EIA 861 dataset are used to estimate electricity EE impacts b y state. 11 EIA data is reported by program administrator (e.g., utility , third - party , or state agency) and requires the disaggregation of reported data by state for administrators with programs in multiple states (e.g., multi - state investor - owned utilities). Program administrators in 50 states and the District of Columbia reported savings in 201 3 . The EPA has compiled this information to derive aggregate electricity sales and incremental electricity savings at the state , tribal and territorial level s . 12 Three tribal areas (Navajo, Ute, and Fort Mojave) and two territories (Puerto Rico and Guam) have affected EGUs under the final rule . The 2013 EIA form 861 data contain information to identify

historical electricity

sales and EE activities in these tribal areas and territories . The tribes are located in three states

– Arizona, New Mexico, and Utah . Electricity sales within the tribal areas are taken out of the total sales within these three states and reported separately . For Arizona, this includes utility sales in the “Navajo Tribal Utility Authority” (Navajo tribe) and the “Aha Macav Power Servic e” (Fort Mojave tribe). For New Mexico, this includes utility

sales in the “Navajo Tribal Utility Authority” (Navajo tribe). For Utah, this

10 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ . 11 The analysis uses 201 3 EIA 861 data , available at http://www.eia.gov/electricity/data/eia861/ . 12 The sub - form “Retail Sales 201 3 ” is used to calculate electricity sales . The sub - form contains details on utility el ectricity sales by service type (energy, delivery, and bundled) and by sector, within each state and territory. In addition, while EIA’s 861 survey encompasses most of each state’s utility sales , EIA makes an adjustment to account for the small remainder o f data not included in each utility’s entry. The adjusted data for “delivery” and “bundled” sales from all sectors are used to calculate sales at the state and territory levels.20

includes utility

sales in the “Navajo Tribal Utility Authority” (Navajo tribe) and the “Moon Lake Electric Assn, Inc .” (Ute tribe). Table 4 provides a summary of th e estimated electricity savings data by

state , tribal area and territory for the latest year of available data . At the national level, incremental electricity savings 13 in 201 3 was 0. 66 % of retail sales with individual state values ranging from 0.00% to 2. 00 %. Cumulative electricity savings 14 (representing the remaining impacts of programs from all prior years) reported at the national level for 201 2 represent 3.73% of retail sales with individual state values ranging from 0.0% to 15.44 %. 15 2012 was the most recent ye ar that EIA Form 861 included cumulative savings data, which has been discontinued in the 2013 version of the form. **T able**  **4 . Aggregate**  **Electricity Savings by State , Tribal Area and Territory** 

**State In**  **cremental Savings as a**

**Percent of Retail Sales (%)**

**2013 Cumulative Savings as a** 

**Percent of Retail Sales (%)**

**2012 Alabam**  **a** 0.10% 0.78% **Arizona** 1.59% 5.43% **Arkansas** 0.49% 0.39% **California** 1.16% 13.67% **Colorado** 0.92% 4.67% **Connecticut** 1.00% 13.39% **Delaware** 0.01% 0.00% **District of Columbia** 0.45% 0.57% **Florida** 0.17% 3.60% **Georgia** 0.26% 0.67% **Idaho** 0.54% 6.20% **Illinois** 1.06% 2.15% **Indiana** 0.93% 1.72% **Iowa** 1.18% 7.80%

13 I ncremental savings (also known as first - year savings) represent the reduction in electricity use in a given year associated with new EE activities in that same year , either new participants in DSM programs that already existed in the previous year s , or new DSM programs that existed for the first time in the current year . 14 C umulative savings (also known as annual savings) represent the reduction in electricity use in a given year from EE activities in that year and all preceding years, taking into account the lifetimes of installed measures. 15 EP A recognizes concerns associated with consistency and quality of 861 data that different reporting entities may have used different methodologies to estimate savings and the EIA 861 data are self - reported. O ver time , there has been increased standardization in data reporting . We believe his dataset remains the most comprehensive public ally available dataset. See section 3.10 for further discussion.21 **State In**  **cremental Savings as a**

**Percent of Retail Sales (%)**

**2013 Cumulative Savings as a** 

**Percent of Retail Sales (%)**

**2012 Kansas**  0.01% 0.24% **Kentucky** 0.38% 1.04% **Louisiana** 0.03% 0.00% **Maine** 1.14% 5.42% **Maryland** 1.07% 2.47% **Massachusetts** 1.06% 6.27% **Michigan** 1.15% 2.77% **Minnesota** 1.13% 13.10% **Mississippi** 0.23% 0.50% **Missouri** 0.49% 0.55% **Montana** 0.54% 5.85% **Nebraska** 0.15% 0.99% **Nevada** 0.48% 6.19% **New Ham pshire**  0.15% 4.90% **New Jersey** 0.70% 1.04% **New Mexico** 0.57% 1.87% **New York** 1.20% 6.89% **North Carolina** 0.60% 1.26% **North Dakota** 0.02% 0.22% **Ohio** 1.04% 3.20% **Oklahoma** 0.26% 0.70% **Oregon** 1.20% 7.72% **Pennsylvania** 0.91% 3.08% **Rhode Island** 2.00% 11.22% **South Carolina** 0.46% 1.12% **South Dakota**  0.13% 0.33% **Tennessee** 0.31% 1.76% **Texas** 0.20% 1.54% **Utah** 0.82% 6.70% **Verm ont**  1.77% 15.44% **Virginia** 0.02% 0.30% **Washington** 1.00% 7.37% **West Virginia** 0.25% 0.20% **Wisconsin** 1.32% 6.61%22 **State In**  **cremental Savings as a**

**Percent of Retail Sales (%)**

**2013 Cumulative Savings as a** 

**Percent of Retail Sales (%)**

**2012 Wyom** **ing** 0.17% 0.71% **Navajo** 0.00% 0.00% **Ute** 0.00% 0.00% **Fort Mojave** 0.00% 0.00% **Continental U.S. Total** 0.66% 3.76% **Alaska** 0.05% 0.15% **Hawaii** 0.04% 0.33% **Puerto Rico** 0.00% 0.00% **Guam** 0.00% 0.00% **U.S. Total and Territories** 0.66% 3.73% Source: EPA calculation based EIA 2013 and EIA 2012 a **4.2.2.2 Projected**  **Impacts** In 2013, Lawrence Berkeley National Laboratory

(LBNL) published an update to a 2009 anal ys is and projected future spending levels and savings through 2025 from EE programs funded b y electric and gas utility customers in the United States under three scenarios (high, medium, and low cases) ( Barbose et al. 2013 ) . The scenarios represent “a range of potential outcomes under the current policy environment” and were based on detailed, bottom - up analy sis of existing state EE policies. Significantl y, the study pre sumes no new major policy developments such as a “national EE standard, clean energy standard, or carbon policy” and specifies that such policy changes could “result in customer - funded EE program spending and savings that exceed the values in our High Case .” The study concludes that efficiency programs are “poised for dramatic growth over the course of the next 10 to 15 years” with the most significant increases occurring in regions with lower levels of program spending, historically , including the Midwest and South. For example, under the medium scenario total U.S. spending on electric efficiency programs increase b y 40% to $8.1 billion in 2025 from 2012 levels. Under the high scenario, spending more than doubles from 2012 levels to $12.2 billion in 2025. Incremental savings levels grow commensuratel y, to 0.8% and 1.1% of sales under the medium and high scenarios, respectively . The study results indicate that under the high scenario 20 states would be achieving 1.5% or higher levels of23

incremental savings, w ith 11 of those reaching or exceeding 2.0%. 16 Table 5 summarizes the results of the LBNL analysis.

**T able**  **5 .**  **Summary of Impacts: Scenarios of Future Utility**  **Customer -**  **Funded Electric EE**  **Programs**

**Case 2025** 

**Incremental Savings (% of**

**Sales) Program** **Costs**

**(billions of $, nom inal)**  **Programs Costs (% of Revenues)**  **Low** 0.5% 5.5 1.1% **Medium** 0.8% 8.1 1.7% **High** 1.1% 12.2 2.7% Source: Barbose et al. 2013 **4.2.3 EE Potential**  **4.2.3.1 Evaluations of EE Potential**  EE potential studies are a common tool for informing the development of EE program plans and budgets, as well as supporting the development of electricity savings targets, required savings levels under an EERS, or “all cost - effective” EE requirement. In cond ucting these studies, states and utilities have developed a methodology that is often described as a “bottom up, engineering - based” approach (EPA and DOE 2007a) . EE potential studies are conducted at various geographic scopes (national, regional, state, an d utility service territory

level) and at different degrees of aggregation (e.g., econom y- wide, sectoral, and program), and can be broadl y grouped into a few ty

pes: technical, economic, market, and program. 17

 **Technical potential** represents the theoretical maximum amount of energy use that could be displaced b y efficiency , without regard to non - engineering constraints such as costs and the willingness of energy consumers to adopt the efficiency measures. It often assumes immediate implementation of all technologically feasible energy saving measures, with additional efficiency opportunities assumed as they arise.  **Economic potential** refers to the subset of the technical potential that is economically cost - effective. Definition of “economic po tential” can vary to some degree by

study . Some estimate economic potential by evaluating technology

upfront cost, operating costs that considers energy prices, product lifetime and discount rate, compared to a 16 LBNL provided these unpublished results from their analysis. 17 The definitions discus sed below largely follow that outlined in EPA and DOE 2007 a.24 conventional alternative or the supply- side e nergy resources. Others incorporate consideration of consumer preferences in addition to consumers’ out - of - pocket expenditure when evaluating the economic potential. Both technical and economic potential estimates assume immediate implementation of efficiency measures without regard to technology

adoption process or real - life program implementation. In addition, these estimates do not alway s reflect market failures or barriers that impede EE and often fail to capture transaction costs (e.g., administration, marketing, anal ys is, etc.) beyond the costs of efficiency measures. Another key

factor determining economic potential is the level of aggregation at which the cost effectiveness evaluation is applied. Applying the cost test at lower levels of aggregation (e.g., at the measure or program level, rather than the sector level) will ty picall y lead to lower economic potential when evaluating a portfolio of programs.  **Market potential (or “achievable” potential** ) refers to the subset of economic potential that refl ects the estimated amount of energy savings that can realistically be achieved, taking into account factors such as technology adoption process, market failures or barriers that inhibit technology adoption, transaction costs, consumer preferences, social and institutional constraints, and possibly the capability

of programs and administrators to ramp up program activity over time.  **Program potential** refers to the subset of market potential that can be realized given specific program funding levels and design s. Program potential studies can consider scenarios ranging from a single program to a full portfolio of programs. 18 As mentioned, the EE industry standard for potential studies is the bottom -up, engineering evaluation of EE potential of individual end - use technologies and measures (EPA and DOE 2007a) . Bottom - up anal ys es all employ a similar methodology

but can vary significantl y in key assumptions (e.g., breadth of sectors and end - uses considered, aggregation level at which cost tests are applied, study period, discount rate, pattern of technology penetration, whether economically justified earl y replacement of technologies is allowed for, and

18 Each subsequent potential estimate described above is a subset of the previous potential estimate, e.g., the market potential is a subset of the economic potential, and the economic potential i s a subset of the technical potential.25

whether continued improvement in efficiency of technology

is provided for). As a re sult, estimated efficiency

potential can vary

significantly

among studies. 19 **4.2.3.2 Overview of Results**  EE potential studies have been conducted for decades and in recent years numerous studies have been conducted at utility , state, regional, and national levels. This section presents a summary of more than 50 recent studies (2009 through 2014) collected b y EPA. Further detail on the collection of studies is provided in Appendix 1, including a database of study sources and results. Studies were identified by review ing three recent meta - studies (Sreedharan 2013; ACEEE 2014b; and EPA 2014a) and supplementing those reviewed studies with more recent or overlooked studies. By

focusing on studies conducted after 2008, the potentially significant effects of the lighting provisions of the Energy Independence and Security Act of 2007 ( Congress 2007) are accounted for. The collected studies cover all major end - use sectors (residential, commercial and industrial) and all regions of the U.S. To normalize results of analy ses addr essing different study periods, we present average annual achievable potential by dividing cumulative percentage savings in the last year of the study by the duration (in years) of the study period. This is a common method of normalization for EE potential studies and allows for comparison with results from the EPA’s demand - side EE plan scenario later in this chapter (section 8) (Sreedharan 2013; ACEEE 2008, 2014b) . Man y studies provided multiple estimates of achievable potential. Some studies use the terms “high,” “medium” and “low” achievable potential, while others used terms such as “high achievable” and “achievable” potential or, in some cases “achievable” and “programmatic” potential. For studies that provided multiple levels of achievable potential, the highest value was classified as high potential and the lowest value was classified as low potential. If a study provided just a single value, it was used as both the high and low potential value for that study . In many instances, low potential values in the studies were arrived at using assumptions of limited budgets or other limiting factors. To be conservative, the EPA has included these low values but generall y consider the high achievable values to be the more appropriate point of comparison in evalu ating the magnitude and timing of savings represented in the illustrative EE plan scenario.

19 Because of the complex consumer behavior, energy market and macroeconomic drivers of energy use and energy efficiency, and in some cases due to the lack of consistent data, quantifying energy efficiency potential and energy savings from policie s and progr ams remains a challenging analytical task. Assumptions about consumer technology adoption behavior, market barriers and failures, and how technology diffusion occurs can also affect estimated potential.26

Table 6 provides summary results of EPA’s review of recent EE potential studies. Across all sectors for all studies, average annual achievable potential ranges fro m 0.9% to 1.3% per year for low to high potential estimates, respectively . Results vary between end - use sectors with the industrial sector showing less potential than residential and commercial sectors. Results for all sectors indicate very

substantial opp ortunity

for achievable EE savings. **T able**  **6 .**  **Summary of EE**  **Potential Studies (2009 to 2014) 20** 

**Sector Average Annual Achievable Potential (% per year)**  **High Low**  **Median Me**  **an Median**  **Me an**  Residential 1.0%/year 1.2%/year 0.7%/year 0.8%/year Commercial 1.3%/year 1.3%/year 0.8%/year 0.9%/year Industrial 0.9%/year 1.0%/year 0.5%/year 0.7%/year All 1.3%/year 1.2%/year 0.9%/year 0.9%/year Source: Appendix 1 **4.3 Costs and Cost**  **- Effectiveness of State EE Policies**  **4.3.1 EE Cost**  **- Effectiveness**  States enact EE policies to meet multiple policy objectives including reduction of customer electricity

bills, lower costs of meeting electricity suppl y needs, energy

reduction, environment and health benefits, and local economic development benefits (EPA and DOE 2006) . Most states evaluate their EE policy options through the application of cost tests, weighing the projected benefits with the costs of the EE technologies and practices (EPA and DOE 2008; RAP 2012) . Each state determines their own policies for the specific costs and be nefits to include in these tests. The costs and benefits are compared on an equal footing b y using present value anal ysis. This is necessary because EE typicall y requires primarily upfront expenditures (e.g., a whole home retrofit) while the economic benef its (e.g., electricity bill savings) accrue over the life of the investment (“measure life”) which can range from a few to twenty or more years. As such, the choice of discount rate and the estimation of measure life are significant determinants of the cos t - effectiveness results. Most states employ multiple tests, adjusting cost and benefit categories depending upon the economic perspective of interest (e.g.,

20 All 53 of the studies reported results for all sect ors but many did not report results for one or more of the individual sectors. At least 30 studies reported results for each sector shown in the table. Because of this, the median value for the “low” data fo r all sectors is higher than the median value for any of the three sectors.27

utility , ratepayer, program participant, society), and consider the results from each one, usually with an identified primary test type. Policies that are selected are those that are found to be cost effective, with benefits greater than costs, as determined by the utility

applyingmethods defined by their state utility

commission. There are five primary cost - effective tests used in the U.S.: 1. Participant cost test from the perspective of the customer installing the measure. Costs may

include incremental equipment and installation costs; benefits include incentive pay

ments, bill savings, and applicable tax credits or incentives. 2. Utility/program administrator cost test from the perspective of utility , government agency or third - party implementing the program. Costs may include program incentive, installation, and overhead costs; benefits may include avoide d energy

and capacity costs - including generation, transmission and distribution - by the utility . 3. Ratepayer impact measure test from the perspective of utility ratepayers not participating in available EE programs. This text includes the costs and benef its that will affect utility

rates, including program and administration costs, as well as “lost revenues” to the utility ; benefits include avoided energy and capacity

costs, and additional resource savings. 4. Total resource cost test from the perspective of all utility customers in the service area. Costs may

include the full incremental cost of the measure, program installation and overhead costs; benefits may include avoided energy

and capacity costs, and additional resource savings. 5. Societal cost test fro m the social perspective. In addition to benefits considered in total resource cost test, may also include non - monetized benefits such as environmental and health benefits. While many

states consider more than one cost test in evaluating EE programs, the most commonly

used (29 states) primary

test is the total resources cost test. This test is considered to be the best measure of the interests of all utility customers. The utility

and societal cost tests are the next most commonly

used primary

tests, used by

five states each. The utility cost test is considered to be the most comparable metric to compare with supply- side resource investments from a utility

resource planning perspective.28

**4.3.2 Costs of Saved Energy**  A common metric for comparing alternative electricity resource options within utility resource plans is the levelized cost of energy (LCOE) or, for EE resources, the levelized cost of saved energy

(LCSE) (EPA and DOE 2007b) . 21 LC SE EE is often compared favorabl y with LCOE of alternative new generation so urces such as fossil - fueled or nuclear power plants, or renewable energy

resources like wind or solar - power generation. In these comparisons, ty pically only utility (or program) costs are considered, not the total costs of saved energy that are discussed l ater in this chapter. The EE anal ys is literature reports average LCSE in the range of 1 - 6 cents/kWh based on program administrator cost. 22 A recent review b y AC EEE examined studies across 20 states between 2009 and 2012, and estimated L CSE for electricity E E programs in the range of 1.3 - 5.6 cents/kWh, with a mean value of 2.8 cents/kWh (ACEEE 2014d) . Earlier reviews of utility

EE programs identified a similar range of L CSE. In 2009 ACEEE reviewed 14 utility studies of LCSE and found a range from 1.6 to 3.3 c ents/kWh, with a mean value of 2.5 cents/kWh (ACEEE 2009) . An earlier ACEEE study reviewed cost - effectiveness anal ys is results in nine states and suggested that reported utility L CSE ranged between 2.3 - 4.4 cents/kWh, with a mean value of 3 cents/kWh (ACEEE 2004) . In 2014, an LBNL anal yz ed the program cost of saved energy

based on data from their DSM Program Impacts Database. The database includes program results reported to state regulators b y more than 100 program administrators in 31 state, primarily for the ye ars 2009 - 2011 (LBNL 2014b). LBNL found a national average LC SE of 2.1 cents/kWh of gross savings. The economic literature also evaluates the LCSE from EE measures using other techniques (e.g., econometrics, top - down modeling), although this body of s tudies is much smaller compared to the bottom - up, engineering - based anal ys is. The economic literature has varying treatment of the free ridership, EE program endogeneit y, and the rebound effect. The different assumptions used in these anal ys es make direct comparison challenging, but overall these empirical anal ys es present a wider range of estimates of cost of saved energy . For example, a 2008 study in the Energy Journal examining utility DSM programs estimated the average utility cost of saved energy

in th e range of 5.1 to 14.6 cents per kWh (Auffhammer et al. 2008) . 21 “Negawatt” or “negawatt - hour” is a term sometimes used to refer to electricity demand or energy reductions achieved through energy efficiency. 22 Unless otherwise noted, estimates of LCSE discussed in this section refer to progr am administrator cost (also known as utility cost). The discount rates, average measure lives, and other assumptions affecting the calculation of LCSE were not always consistent or reported in all studies.29

Some other studies in the economic literature suggest estimated L CSE in a similar range as from the bottom - up analy ses. RFF calculated an average cost of 3.4 cents per kWh saved from utility EE programs , based on the utility- reported savings in the EIA Form 861 ( Gillingham et al. 2006 ) . A 2012 econometric anal ys is of utility rate - payer funded demand - side management and EE programs between 1992 and 2006 found that the estimated energy savings in electricity consumption were achieved at an expected average cost to utilities of approximately 5 cents/kWh (Arimura et al. 2012) . In 2011, u sing a top - down approach that evaluates the savings potential of EE investments using state - and region - specific price elasticity, an RFF study estimated that electricity savings of 1 to 3 percent were available at a marginal cost of 5 cents/kWh and a corresponding average cost of 2.5 - 3.5 cents/kWh (RFF 2011) . A number of anal yt ical and data considerations related to LC SE estimation are also discussed in the literature, including the issue of “free riders” in EE programs, and the accuracy of utility reported costs and energy savings (Train 1994; Joskow and Marron 1992) . EE practitioners also recognize the need to consi der “free rider” and “spillover” effects in program evaluation ( Gillingham et al. 2006 ) . A slight majority of states adjust for free ridership in energy savings estimates, leading to higher LCSE values than otherwise would be the case (ACEEE 2012a) . A smal ler number of states adjust for spillover effects which reduce L CSE values when addressed. Another consideration related to L CSE estimation is the rebound effect. The economic literature has extensive discussion of the potential rebound effect, market inte ractions and econom y- wide response of EE policies and investments. An improvement in EE would effectivel y reduce the cost of a service or production input, potentially boosting its demand or production output thus increasing energy use (“direct” rebound). In addition, money saved from EE can be used for consumption or investment that can increase energy consumption for other goods and services in the econom y (two forms of “indirect” rebound). Reviews suggest that both direct and indirect rebound effects exi st and the size of such effects varies among different studies, technologies, sectors and income groups (UKERC 2007) . Overall, however, rebound effects are found to be relatively modest in most markets and settings ( Greening et al. 2000 ; Davis 2008; Gillin gham et al. 2013; UKERC 2007).30



### 5. **Other EE Strategies** In addition to ratepayer - funded EE programs, many other strategies are available to increase investment in EE and realize associated energy savings, emissions reductions and other benefits. In this section we address a few of the more significant opportunities for realizing additional EE potential: building energy codes, state appliance standards, energy service performance contracting, and volt/VAR optimization. **5.1 Building Energy Codes**  **5.1.1 Overview**  Building ener gy

codes establish minimum efficiency requirements for new and renovated residential and commercial buildings. Building energy codes lock in long - term energy savings at a low cost during the building design and construction phase; some of which would not be feasible or cost - effective as building retrofits. The primary policy rationale for energy

codes is the need t o overcome large and persistent market barriers, the largest of which is the concern over split - incentives or “principal - agents,” in which builders’ incentives to minimize the initial costs of constructing a building are misaligned with occupants’ incentiv es to minimize total occupancy

costs. Building energy codes are designed to eliminate inefficient technologies and building practices, minimizing total ownership and operating costs over the life of the building. Additional co - benefits of implementing buil ding energy codes include pollution prevention, improved electric s ys tem reliability , avoidance of new energy supply investment, and improved occupant comfort and health. Energy building code requirements are also designed to overcome the complexity

of adv anced codes, lack of local - level implementation resources, and a shortage of empirical data on the costs and benefits of codes. Building energy

codes specify

thermal performance 23 criteria for building thermal envelope components such as walls, ceilings, fl oors, and windows, and also set air leakage and duct leakage standards. They also address lighting sy stem efficiency , hot water sy stem efficiency , and heating and cooling equipment efficiency in cases where federal standards do not cover the affected equip ment or sy stem types. For the residential sector, the International Energy Conservation Code (IECC) is the prevailing model code, and ASHRAE 90.1 is the model code

23 The thermal performance of a building refers to how well the temperature of a building is insulated from the outside environment.31

for the commercial sector (EPA 2014b) . Various versions of these model codes are adopted and enforced at the state and local government levels. **5.1.2 Authority / Obligated Parties**  Model energy

building codes are typically developed at the national or international level, adopted at the state or local level, and administered and enforced locally . Local building industry parties, such as developers and property owners requiring building permits, are the most comm on obligated parties (EPA 2014b) . Federal authority affecting state code adoption was established in the Energy Policy Act (EPAct) of 1992 ( Congr ess 1992); its provisions require the U.S. Department of Energy to review each new version of the IECC and ASHRAE 90.1, released about every

three years, to determine whether they save energy compared to the previous version (EPA 2015). When DOE concludes that a new version of the code would improve energy efficiency , states must review and consider adopting the new code. However, since EPAct 1992, not all states have adopted or updated residential and commercial building energy codes per the current DOE de termination. **5.1.3 Building Code Regulations Over Time**  Federal authority for building codes was established in Energy Conservation and Production Act of 1976 (ECPA), which authorized DOE to create national Building Energy Performance Standards (BEPS) that would be legally binding nationwide. The BEPS provision was repealed b y Congress in the earl y 1980s. States, however, continued to develop energy codes. In 1978, California became the first state to include energy requirements in its code (EPA 2015). Other state s, such as New York, Massachusetts, Florida, and Minnesota, developed their own energy

codes during the 1980s. In response to the EPAct 1992 requirements, most states adopted various versions of the IECC and ASHRAE 90.1. Over the last decade, the number of states adopting residential and commercial energy

codes has remained relativel y s table at about 40 (EPA 2006 , 2015 ). F igure 2 shows this consistency

for residential energy

codes, as the states with the lightest shading are the ones that had either not adopted or recentl y updated their energy codes. However, the stringency of these codes has been updated multiple times since 2005 in most states. Eleven of the 40 states with residential building codes are using the 2012 IECC version, which DOE has determined would improve the energy efficiency

of residential buildings by approximately

30 percent32

compared to the 2006 IE CC (EPA 2015). A recent study estimated that upgrading the energy efficiency of a typical new home in Arizona from the 2006 IECC would save homeowners an average of $3,245 over 30 years with the 2009 IECC and $6,550 over 30 years with the 2012 IECC (DOE 2012 b ). DOE esti mates that currently - adopted building energy codes across the country will result in a financial benefit of nearl y $2 billion annually by 2015 and over $15 billion annually

by 2030 (DOE 2014). **F igure**  **2 . R** **esidential E**  **nergy B**  **uilding C**  **ode A**  **doption (**  **2005 to 20 14**  **)**

Source: EPA 2006 and EPA 2015 **5.1.4 Remaining Market Potential**  There is still significant potential for savings from building energy code adoption. From 1992 to 2012, building energy codes in the U.S. saved a cumulative 4 quadrillion Btu and reduced greenhouse gas emissions by 300 million metric tons (ACEEE 2015a) . From 2013 to 2040, the continued adoption of increasingl y stringent building energy codes in the U.S. is expected to save 40.1 quadrillion Btu (ACEEE 2015 a). A recent study b y ACEEE also found that for residential and commercial building codes, the potential for energy savings by

2030 was 155 million megawatt - hours (MWh) annuall y, or 4.2 percent of annual electricity consumption relative to 2012 (ACEEE 2014 a).33

**5.2 State Appliance Standards**  **5.2.1 Overview**  S tate appliance standards establish minimum energy efficiency

levels for those appliances and other energy- consuming products that are not already covered by the federal government. These standards ty picall y prohibit the sale of less efficient models within a state. States are finding that appliance standards offer a cost - effective strategy for improving energy efficiency and lowering energy

costs for businesses and consumers, though these standards are superseded when federal standards are enacted for new p roduct categories (EPA 2014 b ). The key objectives of appliance efficiency standards are to:

 Raise the efficiency

of a range of residential and commercial energy- consuming products, where cost - effective.  Overcome market barriers, such as split incentives be tween homebuilders and homebuyers and between landlords and tenants, and panic - purchase 24 situations in which appliances break and must be replaced on an emergency basis.  Ensure energy

use reductions to prevent criteria air pollution and greenhouse emission s, improve electric sy stem reliability , and reduce consumer energy bills (EPA 2015). While state appliance standards can be useful in testing and exploring the effectiveness of standards for new products, many state appliance standards have been preempted or superseded by existing federal standards. States may appl y to DOE for a waiver to implement more stringent standards. This is sometimes granted if a certain period of time has passed since the federal standard has been updated (EPA 201 5 ). **5.2.2 Authority /**  **Obligated Parties** Establishing efficiency

standards in a state typically requires enabling legislation from the state legislature. However, once legislation is enacted, it may allow an executive agency to set further standards administratively (EPA 2015).

24 In a panic purchase, customers usually do not have the time to consider a range of models, features, and efficiency levels, a nd the full range may not be available from all suppliers.34

State energy

offices, which ty

pically administer the federal state energy program funds, have generall y acted as the administrative lead for implementing standards. In contrast, inspection and enforcement of appliance standards regulations has ty pically involved self

policing among industry . Manufacturers of products being sold in a given state are ty picall y obligated to ensure their appliances meet the appropriate energy effi ciency standards (EPA 2014 b ) . **5.2.3 Appliance Standards over Time**  California’s applianc e standards program dates to the 1970s, when the state began to pursue standards before the enactment of federal legislation. When the federal government opted not to issue standards under its legislative mandate in 1982, other states joined California in developing state standards. These state initiatives helped create the impetus for new federal legislation in 1987 (the National Appliance Energy Conservation Act or NAECA), the Energy Policy Acts (EPAct s ) in 1992 and 2005, and the Energy Independence and S ecurity Act (EISA) in 2007 (EPA 2015). These federal laws have established appliance efficiency standards for more than 50 products, representing about 90% of home energy use, 60% of commercial building energy use, and about 30% of industrial energy use (D OE 2015a). Bolstered by new standards contained in the EPAct 2005, EI SA 2007, and additional DOE rules, 25 these savings rose to 278 terawatt - hours (TWh), or 7% of U.S. electricity use in 2010. By

2025, total electricity savings from existing federal standards are projected to increase to 682 TWh per year, or 14% of the projected annual U.S. electricity use. Net savings to consumers and businesses are also expected to increase from $27 billion in 2010 to $61 billion in 2025 (ASAP 2012). While the NAECA effect ively preempted state action on federally covered consumer products, California has continued to develop efficiency standards for other products and technologies that aren’t included in the Act. California’s standards program has contributed to substantial improvements in energy efficiency . Since its inception, the program has saved consumers over $75 billion on electricity bills (CEC 2013). Since 2001, 15 states and the District of Columbia have enacted appliance efficiency standards. While most of these i ndividual standards have been superseded by federal standards,

25 DOE is required to review federal appliance standards and test procedures periodically, and initiate a process to set new standards provided that improvements to energy efficiency are "technically feasible and economically justified" (DOE 201 5b).35

as of February

2014, 12 states 26 and the District of Columbia have one or more appliance efficiency standards for products not covered by federal standards (ASAP 2014). **5.2.4 Remaining Market Potentia**  **l** While federal appliance standards continue to expand, the direct economic and environmental benefits of state standards are still substantial. California draft regulations for 15 new appliance standards are expected to save 50 billion gallons of water, 1 ,400 MW of peak electricity, 9.8 TWh of electricity, and 162 million therms of natural gas per year. This is expected to result in annual savings of $2 billion (CEC 2014). From a national perspective, there is still untapped potential for states to move into product areas not yet covered b y federal standards. Table 7 shows potential energy savings from products that do not currently have federal standards. The three products with the greatest potential in the residential, commercial and industrial, and lighting sectors are shown, along with a summary

line for each sector. Some of the greatest potential savings in 2035 are in the residential sector, specifically improvements in water heater efficiency . Implementing all of these standards through federal regulations would result in annual savings of 212 TWh in 2025, or 5% of projected national electricity consumption (ASAP 2012 ; EIA 2015). **T able**  **7. Estimated Energy Savings**  **Potential of**  **State Appliance Standards**  **Annual savings in 2025 Annual savings in 2035**  **Electricity savings**  **Peak**

**dem and**  **Natural gas Electricity**  **savings Peak** 

**dem and**  **Natural**

**gas Products**  **(TWh) (GW)**  **(Tbtu) (TWh)**  **(GW) (Tbtu)**  **RESIDENTIAL STANDARDS** Water heaters 18.2 2.5

— 43.0 5.9

— Set top boxes and digital communication equipment 14.7 2.0

— 14.7 2.0

— Air handlers 13.7 5.6

— 29.1 11.9

— **Total (14 products) 98.5**  **16.8 51.6**  **142.3 27.0**  **51.6 COMMERCIAL AND INDUSTRIAL STANDARDS**  Walk - in coolers and freezers 14.7 3.4

— 14.7 3.4

— Distribution transformers 10.9 1.5

— 22.4 3.1

— Electric motors 9.0 1.4

— 18.6 2.9

— **Total (13 products) 62.4**  **15.5 74.2**  **98.5 24.5**  **139.9**

26 These states include Arizona, California, Connecticut, Georgia, Maryland, Nevada, New Hampshire, New York, Oregon, Rhode Island, Texas, and Washington .36 **Annual savings in 2025 Annual savings in 2035**  **Electricity savings**  **Peak**

**dem and**  **Natural gas Electricity**  **savings Peak** 

**dem and**  **Natural**

**gas Products**  **(TWh) (GW)**  **(Tbtu) (TWh)**  **(GW) (Tbtu)**  **LIGHTING STANDARDS** Incandescent reflector lamps 20.2 5.0

— 20.2 5.0

— Outdoor lighting fixtures 10.3 0.7

— 26.1 1.8

— General service fluorescent lam ps 6.9 1.7

— 6.9 1.7

— **Total (7 products)**  **50.8 9.3**  **— 65.6**  **15.6 —**  **ALL PRODUCTS 212**  **42 126**  **306 67**  **235** Source: ASAP 2012 **5.3 Energy Service Performance Contracting**  **5.3.1 Overview**  Energy service performance contracting (ESPC) uses cost savings from reduced energy consumption to repay

the cost of installing energy conservation measures (HUD 2014). Under an EPC program, an Energy Service Company (ESCO) first conducts an energy audit. The ESCO then designs and constructs a project that achieves the building owner’s energy efficiency

needs and arrange s for the project’s financing, usually through a third party. The third party is repaid by the building owner/operator from the savings in their energy costs. In this ty

pe of E S PC arrangement, the builder owner/operator does not need to incur upfront expen ses, and will experience the benefits of the upgrades, including monetary savings once the financer’s costs have been repaid (ICF 2007). E S PC programs have been used extensively by state, federal, and local facilities to reduce utility and operating costs and to help meet environmental and energy efficiency

goals, which can include the use of combined heat and power (CHP). Forty- nine states have implemented performance contracting activities, primarily through legislation, covering a combination of entities that include public agencies, school districts, municipalities, state colleges and universities, counties, or the state as a whole ( ORNL 201 5 ). 27 While E S PC programs are already widespread, states have found that they can further utilize this approach by extending eligibility to all public facilities in the state (EPA 2015).

27 Wyo ming currently has no performance contracting legislation.37

While ESCOs deliver E S PC projects, and the E S PC market is the main driver for ESCOs, the two markets do not overlap completel y. Since 2006, the share of the ESCO market consisting of E S PC projects has remained stable at about 70 percent (ICF 2007 ; LBNL 2014 a ). The other 30 percent of the market is comprised of design/build services; engineering, procurement, and construction services; consulting services; and operation and maintenance c ontracts. These services are not considered to be EPC projects since the project financial performance risk is not incurred b y the ESCO or potential third party lenders (ICF 2007). **5.3.2 History of E**  **S PC Programs and the ESCO Business Model**  The ESCO business mode l emerged during the 1970s, supported in part by utilities paying ESCOs a fee for services such as energy audits provided to customers via their electric utilities and implementing federal energy management goals (NAESCO 2011). EPC activities emerged in th e mid - 1980s as power plants became more expensive to build and utilities were ordered to produce Integrated Resource Plans (IRPs) where energy efficiency

measures could be used to meet projected demand. ESCOs were used to deliver energy savings from projec ts implemented at industrial and institutional sites, relying on advances in energy efficiency monitoring and verification techniques (ICF 2007). By the late 1990s, funding for utility- sponsored energy efficiency

programs was reduced in about half of the s tates due to changed regulatory structures and increased political and regulatory

pressures to maintain lower electricity prices (EPA 2006). Many restructured electric utilities shifted their focus away from sponsoring programs to starting new affiliated E SCOs to deliver energy

services directly

to customers. However, the business model for these market based ESCOs was limited in reach to mainl y larger industrial, commercial and institutional customers. Several states and local governments also turned to EP C activities to provide services in public facilities via lea d - by- example policies (see section 3 ). These governments also saw the benefits of addressing capital equipment and maintenance projects that would not have otherwise been addressed through the ca pital budget process (I CF 2007). Commercial lenders also became involved in EPC funding, lowering the cost of projects through competition and paving the way for new financing vehicles (ICF 2007). During the early

2000s, however, the growth of the EPC and ESCO market declined due to loss of both performance contracting in federal buildings and market confidence for industrial38

financing mechanisms (NAESCO 2011). This trend reversed in 2005 as the EPC and ESCO market increased focus on state and local public buildings and innovated program offerings to provide customers new technologies and financial models (see section 3 and IC F 2007). **5.3.3 Remaining Market Potential**  The market potential for EPC programs and ESCOs remain significant. Annual revenues for the U.S. E SCO market was expected to increase from $5.3 billion in 2011 to about $6.4 billion in 2013 ( LBNL 2014 a). An anal ys is by L awrence Berkeley

National L ab estimated the investment opportunity

for ESCOs ranged between $71 billion and $133 billion (LBNL 2013a) . Figure 3 shows a breakdown of investment opportunity by market segment. Commercial buildings, K - 12 schools, and the health and hospital sectors have the highest remaining potential for ESCOs. Commercial buildings in particular have high barriers to implem enting energy efficiency under EPC activities, as the private sector is generall y averse to financing energy efficiency activities and has more stringent requirements for a lower pay back period on projects ( LBNL 2013a). While the two markets do not overlap completel y, the potential for ESCO investments provide a strong indicator for the remaining potential of EPC activities.39 **Figure 3. Remaining M**  **arket P**  **o tential for ESCOs** 

Source: LBNL 2013a **5.4 Volt/VAR Optimization**  **5.4.1 Overview**  Volt/VAR optimization (VVO) refers to coordinated efforts by utilities to manage and improve the delivery

of real and reactive power28 in order to increase the efficiency of electricity distribution. VVO is accomplished primarily through the implementation of Smart Grid technologies that imp rove the real - time response to the demand for real and reactive power. Technologies for VVO include load tap changers and voltage regulators, which can help manage voltage levels, as well as capacitor banks, which allow for the delivery of reactive power. By using local capacitor banks to respond to reactive power demand, rather than adjusting the output of remote generators, sy stems can achieve reductions in transmission line loss. The U.S. Energy In formation Administration (EIA) estimates that on average, 7.5% of generated electricity is lost

28 Reactive power is an inherent f eature of alternating current systems, and is related to the phase shift between the current and voltage. Reactive power does no work, but is important for circuits that include capacitive and inductive loads, most notably motors. It is measured in Volt - Am pere Reactive (VAR).40

in transmission and distribution, a number that could be reduced b y effective use of VVO (EIA 2012 b ).VVO efforts are often closely related to conservation voltage reduction (CVR), which are actions taken to reduce in itial delivered voltage levels in feeder transmission lines while remaining within the 114 volt to 126 volt range required at the customer meter (based on ANSI C84.1 standards). Like VVO, CVR relies on the use of Smart Grid technologies in order to more ef fectivel y manage voltage levels across all loads in a system. A simple presentation of these benefits are shown in Figure 4 , where the implementation of VVO technologies allows electricity distributors to deliver electricity initially at a lower voltage, thus decreasing the generation fuel requirements, while still delivering within the acceptable range to all 7 loads. The combination of VVO and CVR can lead to a range of benefits, including reductions in peak electricity demand, reductions in energy

requirements necessary

to meet a certain level of demand, and a reduction in line loss.41

**Figure 4. Hypothetical V**  **oltage P**  **rofiles with and without VVO T**  **echnologies**

Source: DOE 2011 **5.4.2 History of VVO Programs**  VVO and CVR efforts were first introduced with partial implementation in California in 1977 (UNLV 2014). In 1987, the Snohomish Public Utility District instituted a pilot CVR project that demonstrated the possibility of reducing voltage and energy requirements by

approximately 2.1% (NRECA 2013). More recentl y, the advent of Smart Grid technology with increased availability

of energy

use data, has led to a renewed interest in VVO and CVR as the potential for increased efficiency

has become more apparent. Of the 99 projects funded by the Smart Grid Investment Grant program under the American Recovery and Reinvestment Act of 2009, 2642

included the implementation of VVO technologies. These projects, which target reduced voltage during peak demand, electricity conservation due to reduced voltage, and reduced losses from feeder lines, are expected to achieve a 1% reduction in energy consumption for every

1% reduction in voltage level (DOE 2012 a ). There is no formal state requirement directing implementation of Volt/VAR optimization. However, in 2012, the National Association of Regulatory Utility Commissioners published a resolution in support of VVO implementation at the state - level. The resolution encouraged public utility

commissions to work with state legislature to include deplo yment of VVO technologies as part of energy efficiency

resource standards (NARUC 2012). **5.4.3 VVO Market Potential**  Large market potential currentl y exists for the implementation of VVO. The Pacific Northwest National Labo ratory estimates that implementing VVO nationwide could potentially yi eld as much as a 3.2% annual reduction in energy consumption ( PNNL 2012). The Bonneville Power Administration estimates that the cost - benefit ratio for transmission and distribution opti mization is positive and well understood, and that CVR is a key component of these positive effects (BPA n.d. ). McKinsey estimates that VVO could provide $43 billion in gross annual benefits in 2019 in the U.S., with CVR comprising the largest share of the se benefits (McKinsey 2010). IHS Technology

estimates that the shipments of VVO technology devices will double between 2013 and 2018, with a quadrupling of revenue over the same time period (IHS 2013). The Institute of Electrical and Electronics Engineers estimates that CVR has the potential to save approximately

750 trillion BTUs of electricity, equivalent to 5.4% of total U.S. electricity generation, at a total cost of $3.3 billion dollars (IEEE 2014). While these estimates vary in level of impact, they

display the strong market potential that exists for VVO projects. 

### 6. **Utility Regulatory Models for Supporting Demand -**  **Side EE** The electricity

sector widely

recognizes that state utility regulatory

models for supporting energy efficiency

are key to expanding uti lity investments and higher energy savings from programs (IE I 2014) . Further, all states which regulate investor - owned utilities have adopted or modified one or more policies in order to better align utility incentives with the delivery of cost effective energy

efficiency (I EI2014) . States with the highest per capita spending on energy43

efficiency programs have the most comprehensive regulatory models for energy

efficiency

in place (NRDC 2012) . The most common regulatory models for incentivizing energy efficiency through electric utilities include decoupling revenue from sales volumes, ensuring program cost recovery , and providing shareholder incentives linked to program performance. The Edison Foundation reports on the adoption of these policies across states through its Institute for Electric Innovation, as shown in Table 8 .

**Table 8. Summary of State Regulatory Frameworks: December 2014 Energy Efficiency Incentive Mechanism**  **Number of States Pending**  **Fixed -**  **Cost Recovery Mechanism**  **s Lost Revenue Recovery**  19 0 **Revenue Decoupling** 14 1 **Perform ance Incentives**  29 2 **Program Cost Recovery**  46 0 Source: IEI 2014 These approaches have been widel y used for over a decade, with performance incentives approved b y Connecticut as earl y as 1988 (IE I 2014) . In 2006, the need for such policies was recognized b y the National Action Plan for Energy Efficiency Leadership Group of gas and electric utilities, state agencies, energy consumers, energy

service providers, environmental groups, and energy

efficiency

organization s. Over 100 states, utilities, and key stakeholders across 49 states made aggressive commitments to energy efficiency

under the Action Plan and endorsed the Action Plan five key policy recommendations which included modifying utility incentives (EPA and D OE 2006) . In 2014, the Edison Electric Institute (EEI) and the Natural Resources Defense Council (NRDC) issued a joint statement urging state utility regulators to adopt a number of policies to sustain utility financial health given the greater investment being made in both energy

efficiency

and customer - sited electricity generation (NRDC 2014) . The need for states to adopt utility financial incentive policies is due in part to how traditional ratemaking allows utilities to recovery costs based on the volume of electricity sold. This creates an incentive to maximize the volume of sales (often referred to as the “throughput incentive”), which in turn creates a disincentive to invest in energy efficiency

since such programs will reduce sales volume. A decoup ling policy counteracts the financial incentive to44

increase sales b y fixing a utility ’s revenue for a specific term, in order to match the amount of anticipated costs incurred plus an appropriate profit. I f the utility can reduce its costs during the term through energy

efficiency , the utility

will be able to increase its profits. Alternatel y, lost revenue adjustment mechanisms may be used to diminish the throughput incentive by allowing utilities to directly

recover the lost revenue associated with not sel ling energy due to energy efficiency programs. Reduced electricity sales may affect utility finances in additional ways, including reducing the utility ’s opportunity to earn additional profit from the regulated rate of return on new capital projects whic h would otherwise be needed to meet higher load growth, such as new generators and transformers upgrades. As an alternative, performance incentive policies are used to allow utilities to earn a profit for achieving high levels of energy savings, recognizin g that multiple benefits of energy efficiency

resources that are not otherwise explicitly accounted for in traditional regulatory

models. Performance incentive policies come in different forms, such as earning bonuses and allowing a utility to earn a high er rate of return on capital invested in energy efficiency . The level of capital investment made by a utility is often informed b y el ectricity resource planning. Most states require one or more forms of electricity resource planning, such processes used t o inform long - term integrated resources plans (IRPs) and regulatory approvals for large projects often referred to as Certificates of Public Convenience and Necessity (CPCNs). IRPs are required or present in more than 30 states, including most verticall y integrated states, and at least 19 states have some form of CPCN (EPA 2015) . Incorporating energy efficiency

into electricity resource planning enables the utility to consider a broad range of electricity resource options and avoid capital investment in m ore expensive electricity suppl y or delivery infrastructure. Due in part to the shifts in long - term incentives from regulators, utilities are also increasing ly seeing energy efficiency

as part of their business model. According to a 2015 survey of utility executives, 71% of utilities are developing new business models for energy efficiency and demand response (Utility DIVE 2015) . A significant number of the utilities surveyed see their business model changing from a traditional verticall y integrated utility , to an energy services or smart integrator businesses model. Further, states such as New York and Hawaii45

have initiated actions at their public utility commissions to examine and encourage changes to the utility business model. 29 Both of these state are e xperiencing growth in distributed electricity generation and can build upon their existing energy efficiency

regulatory

frameworks which include energy

efficiency program cost recovery , decoupling, and performance incentives. 

### 7. **Energy Efficiency Certificates** In the U.S., extensive consideration of tradable energy efficiency

credits (EEC) developed almost a decade ago, most often framed as a potential compliance mechanism for state energy efficiency

resource standards (EERS), a widely adopted and effective pol icy mechanism for achieving cost - effective electricity savings through utility

energy efficiency

programs. 30 State EERS as a key

policy strategy and the use of EECs for compliance were concepts borrowed from the earlier adoption of renewable portfolio stand ards (RPS) and the use of renewable energy credits (RECs) for compliance. Building from the successful experience with EERS/RECs, several significant policy papers discussed how the generation, tracking, and trading of energy efficiency

credits could work as a means for supporting compliance with EERS or as a mechanism for achieving greenhouse gas reductions (NREL2008; CRS 2007; IEA 2012a) . A few states put regulations in place that allowed for the use of EECs for EERS or RPS compliance. 31 Consistent with these developments, several of the regional tracking sy stems developed to support the use of RECs for compliance with state RPS also included a capability to perform the same functions for the use of EECs for potential compliance with state policies. 32 However, these capabilities have yet to be widely used as significant levels of EEC transactions have not developed in the U.S. There are, however, some voluntary markets for energy

savings credits. For example, North American Renewable Registry extended its certificate tracking s ys tem to energy efficiency , and registered the first voluntary energy efficiency

certificates for IBM Corporation 29 For more information, see the New York Reforming Energy Vision proceeding ( http://www3.dps.ny.gov/W/PSCWeb.nsf/All/26BE8A93967E604785257CC40066B91A?OpenDocument) and Summary from Hawaii PUC of the Commissions Inclinations on the Future of Hawaii’s Electric Utilities ( http://puc.hawaii.gov/wp content/uploads/2014/04/Commissions -Inclinations.pdf ). 30 Many terms have been used or suggested including energy efficiency certificates, energy savings certificates, white tags, and white certificate s. 31 These states include Connecticut , Michigan, and North Carolina. 32 NEPOOL Generation Information System issues energy efficiency certificates for CT. PJM Generation Attribute Tracking System issues energy efficiency certificates for PA. NC Renewable En ergy Tracking System allows energy efficiency to count tow ards compliance with the NC RPS.46

in March 2010 (NARR 2010) . Sterling Planet, a retailer of RECs and carbon off s ets, also offers White Tags® trading instruments that recognize the meter - measured, performance - based results of energy efficiency

projects. In June 2012, Sterling Planet completed the first White Tags transaction b y purchasing Michigan’s Selfridge Air National Guard Base’s 2011 White Tags in order to fulfill energy

efficiency

compliance requirements for a Michigan utility (Sterling Planet 2012) . The Climate Registry has a program called “Cool Planet Energy Efficiency” which provides assistance to utility company business customers with measuring and managing their energy and carbon outputs (TCR 2015) . Even though experience with EECs in the U.S. is limited, their use internationally has been significant. Nine energy efficiency

policies across seven countries outside of the United States allow for trading of the energy savings used for compliance (IEA 2012a) . Trading has been incorporated in the design of these policies to help identify cost - effective opportunities from a broader pool of participating projects and parties. Table 9 summarizes the ty pes of energy savings and trading included in policy design. Over half of these policies allow for trading through the creation and sale of EECs.47 **T able**  **9. International Examples of Trading Energy Savings Country (Jurisdiction)**  **Eligible Energy savings**  **Type of Trading** Australia (New South Wales) Preapproved projects implemented by

obligated and accredited non obligated parties EECs Australia (South Australia) Activities undertaken by, or on behalf of, obligated energy retailer Limited transfer of credits among obligated parties Australia (Victoria) Installation of preapproved energy

efficiency products by obligated and accredited non - obligated parties EECs China Ac hieved by grid companies and

energy service companies subsidiaries of grid companies Obligated parties may purchase savings from customers and ESCOs under bilateral contracts Denmark Distributors must engage third parties

to achieve energy savings across energy types Energy savings may only be traded among obligated energy distributors France Produced by obligated parties, local

authorities, and social housing landlords Over - the - counter trading of EECs Italy Produced by obligated distributors ad

accredited energy service providers Over - the - counter and spot market trading of EECs Poland Projects selected through annual

auctions EECs fully tradable on the Polish Power Exchange United Kingdom Projects implemented by obligated parties or their contractors Transfers of emissions reductions and trading of obligations allowed among obligated parties Source: IEA 2012a Two good international examples are provided b y programs in Ital y and in Australia (New South Wales) that have been trading EECs successfull y and at significant scale over the past decade (Pavan 2008; NSW 2015) . It al y uses EECs as part of its White Certificates s ys tem, a key policy supporting their international climate commitments (Ital y 2014) . In implementing this policy , electricity and gas distribution utilities use EECs to meet established energy savings targ ets. These utilities may either generate their own EECs or buy from third parties through the market. Approximately

one - third of EECs are generated b y third parties and subsequently trade on the open market. EECs are also exchanged between energy efficien cy

providers and utilities through bilateral contracts. 33 The majority of energy savings from the Italian White Certificates sy stem are realized from reducing electricity usage through a set of pre - established eligible project ty pes (IEA 2012a) .

33 From 2005 - 2009, electricity savings met 77% of the overall energy savings targets, as reported in The Fifth National Communication under the UN Framework Conventio n on Climate Change, submitted by Italy in November 2009.48

In Australi a, the state of New South Wales initially incorporated EEC trading as part of their Greenhouse Gas Emissions Trading scheme. In 2009, New South Wales began trading EECs via the Energy

Savings Scheme recognizing that unique barriers to energy efficiency per sisted. Obligated parties, which include electric utilities, are required to acquire and surrender EECs to meet the energy

savings targets of the Energy Savings Scheme. Other organizations also use EECs to voluntarily

reduce their greenhouse gas emissions. Energy efficiency

projects across residential, commercial and industrial sectors are used to create EECs in New South Wales. Active trading of EECs also exists in New South Wales. Trading markets have been established by

the private sector with some organ izations, such as the Australian Financial Markets Association and individual brokers of environmental commodities, providing regular updates on EEC wholesale market prices (NSW 2015) . Even though most EECs are traded via bilateral contracts, trading of EE Cs has also developed across spot, forward, and options contracts. Since an y party who is an “Accredited Certificate Provider” may create EECs, the Energy Savings Scheme policy

has fostered the development of a robust energy services industry of firms impl ementing energy efficiency

projects supported in part by revenues from the sale of associated EECs. 

### 8. **Demand -**  **Side EE Plan Scenario: Magnitude and Timing of Savings 8.1**  **Approach** Chapter 3 of the Regulatory Impacts Analy sis (RIA) presents illustrative analy sis of the final rule b y making assumptions about the approaches that states might pursue as they develop their state plans. These approaches are embodied in the illustrative plan scenarios developed b y EPA for the RIA and used to estimate the costs, benefits, an d impacts of the final rule. The final CPP provides the states the flexibility to use demand reductions resulting from demand - side EE as a component of their state plan strategy , either directly recognized towards compliance with a rate - based goal or as a complimentary approach fo r achieving a mass - based goal (see section VIII, State Plans, of the final rule preamble for details) . The EPA has included in the illustrative plan scenarios (both rate - and mass - based) a level of demand reduction that could be ac hieved, and the associated costs incurred, through implementation of demand - side EE measures. In this section, the EPA provides the basis for the illustrative demand - side EE plan scenario that is49

included as a component of the plan scenarios presented in the RIA and, then, in the following section, the basis for associated EE cost estimates are provided. The demand - side EE plan scenario is derived from state and utility experience with ratepayer - funded EE programs and the resulting energy savings , as described in preceding sections of this TSD . Demand - side EE is included in the illustrative analysis contained in the RIA for several reasons including the current use of such programs in every state, the substantial savings relative to other opportunities to re duce CO 2 emissions from the power sector , overall cost - effectiveness, and the quality of available information on their impacts and associated costs. The ratepayer - funded EE programs are the basis for the levels of demand - side EE that are included as part of the RIA, although there are other strategies and measures that states may employ for purposes of the final CPP. For example, the final rule provides states the flexibility to use building codes, appliance standards, and energy service performance contra cting (to name a few examples ) , and those strategies also offer substantial, low - cost opportunities to reduce CO 2 emissions from affected EGUs. **8.2 Inputs**  The following steps were taken to establish the inputs for development of the illustrative demand - side EE plan scenario for each state , tribal area, and territory :  Step 1: Determine current level of demand -side EE performance  Step 2: Determine EE plan scenario level of performance  Step 3: Determine start year for EE plan scenario  Step 4: Determine start year level performance  Step 5: Determine pace of improvement from start year to EE plan scenario level of performance  Step 6: Determine average portfolio measure life and distribution of measure lives  Step 7: Determine sustainabilit y of EE plan scenario level o f performance **8.2.1 Step 1: Determine Current Level of**  **Demand -**  **Side EE Performance**  A fundamental indicator of the level of EE program performance is incremental annual savings (also known as first - ye ar savings) as a percent of retail sales. This is a common metri c50

defining savings levels for state EERS and is readily calculated from EIA Form 861 data for each state , tribal area and territory (hereafter, just referred to as “states”) . Incremental annual savings are also more directly

estimated and evaluated than are cumulative savings. 34 To determine the current level of demand - side EE performance , the EPA aggregated the most recent (2013) EIA Form 861 data to the state level to establish their current level of performance. Estimated incremental savings results are p resented in Table 4 . **8.2.2 Step 2: Determine**  **EE Plan Scenario Level of Performance**  As discussed previousl y ( section 4.2.3 ), achievable demand - side EE potential exists at significant and comparable levels (on the basis of total cumulative potential over a period of ten to twenty years) in all regions of the country . While varied regional characteristics (e.g., avoided power s ys tem costs, economic growth, sectoral mix, climate, and level of past EE efforts) affect estimates of achievable potential, ongoing improvem ents in energy- efficient technologies and practices, economic growth, population increases, and continually improving strategies for program delivery

have resulted in persistent and substantial levels of achievable potential regardless of specific regional characteristics. D irect indicator s of the achievable incremental levels of energy savings performance is provided b y past performance at the state and utility levels, and b y requirements states have put in place for levels of savings to be achieved b y 202 0. As discussed, these requirements are often in the form of EERS or similar savings goals that are applied to utilities in the state. Table 10 summarizes incremental savings levels as a percentage of retail sales from EIA Form 861 (201 3 ) data, aggregated to the state level, and categorized into four ranges of savings levels (< 0.5%, 0.5% to 0.99%, 1.0% to 1.49%, and >= 1.5%). As shown, three states achieved the highest level of performance (> 1.5%) and an additional fourteen states achie ved the second highest level of performance (1.0% to 1.49%).

34 Estimates of cumulative savings impacts in a given year are derived from incremental savings values and information on measure lives . Information on measure lives is less consistently gathered tha n is information on incremental savings values.51 **T able**  **10. 201**  **3 State Levels o**  **f Incremental Annual Savings Incremental Savings as**  **% of Retail Sales # of States**  **States >= 1.5%**  3 AZ, RI, VT **1.0% to 1.49%** 14 CA, CT, IL, IA, ME, MD, MA, MI, MN, NY, OH, OR, WA , WI **0.5% to 0.99%** 9 CO, ID, IN, MT, NJ, NM, NC, PA, UT **< 0.5%** 25 AL, AK, AR, DE, DC, FL, GA, HI, KS, KY, LA, MS, MO, NE, NV, NH, ND, OK, SC, SD, TN, TX, VA, WV, WY Source: EPA calculation using EIA 2013 In addition to the state levels of performance represented in Table 10, numerous utilities of all ownership types and in all regions of the country have achieved significant levels of incremental savings. Tables 11, 12, and 13 provide examples of investor - , cooperative - , and municipal - owned utilities and their incremental savings levels for 2013 based on EIA Form 861 data. **T able**  **11 . 2013 Incremental Annual Savings**  **– Investor**  **- Owned**  **Utilities**

**Investor -**  **Owned Utility State**  **Incremental Savings as % of Retail Sales**  Arizona Public Service Company AZ 1.6% Commonwealth Edison Company IL 1.2% DTE Electric Company MI 1.3% Niagara Mohawk Power Corporation NY 2.2% Pacific Gas & Electric Company CA 1.7% Potomac Edison Company MD 1.8% Source : EPA calculation using EIA 2013 **T able 12**  **. 2013 Incremental Annual Savings –**  **Cooperative -**  **Owned Utilities**  **Cooperative -**  **Owned Utility State**  **Incremental Savings as % of Retail Sales**  French Broad Electric Membership Corporation NC 2.2% Jackson Electric Cooperative Inc. WI 2.6% KEM Electric Cooperative Inc. ND 1.6% Southern Maryland Electric Cooperative Inc. MD 1.4% Southern Pine Electric Power Association MS 2.4% Umatilla Electric Cooperative Association OR 1.5% Source: EPA calculation using EIA 201352 **T able 13**  **. 2013 Incremental Annual Savings –**  **Municipal -**  **Owned Utilities**  **Municipal -**  **Owned Utility State**  **Incremental Savings as % of Retail Sales**  Austin Energy TX 0.9% City of Fort Collins CO 2.2% City of Oberlin OH 2.6% City of Seattle WA 1.5% City of Saint Peter MN 2.1% Jacksonville Electric Authority FL 0.8% Los Angeles Department of Water & Power CA 0.9% Source: EPA calculation using EIA 2013 Table 14 summarizes incremental savings levels required by state EERS and categorized into the same four ranges as Table 10 ( ACEEE 201 5b) . Seven states are required to achieve the highest level of performance (> 1.5%) and an additional eight states are required to achieve the next highest level of performance (1.0% to 1.49%).

**T able**  **14. Levels of Incremental Savings**  **Required by State EERS**  **Incremental Savings as % of Retail Sales**  **# of States States**  >= 1.5% 7 AZ, ME, MA, MN, RI, VT, WA 1.0% to 1.49% 8 CO, CT, HI, IA, MD, MI, NY, OR 0.5% to 0.99% 4 AR, CA, IL , WI < 0.5% 1 TX Source: ACEEE 2015b For the illustrative demand - side EE plan scenario level of performance, the EPA has determined that a 1. 0 % incremental savings , as a percentage of retail sales , is appropriate . This level was achieved by seventeen states and b y numerous utilities of all ownership ty pes in 201 3. An additional two states (CO and HI ), accounting for overlap, are expected to achieve this level by 2020 as shown in Table 14 . Thus, nineteen states have either achieved or are required to achieve this level of performance.53

**8.2.3 Step 3: Determin**  **e Start Year for EE Plan Scenario**  For construction of the EE plan scenario, the EPA has determined that 2020 is appropriate as the first ye ar for state demand - side EE efforts. The EPA believes this is reasonable for the illustrative plan scenario as effort s are ramped up in advance of the 2022 - 2029 interim plan performance period . This allows sufficient time

– more than four ye ars – after issuance of the final rule for planning of these efforts. This reflects a three ye ar delay from the 2017 start ye ar used for the proposal. **8.2.4 Step 4: Determine Start Year Level of Performance**  For construction of the best practices scenario, the EPA has set each state’s level of performance in the start year ( 2020 ) to the level of performance observed in 2013 , based on the aggregate state - level incremental electricity savings based on EIA Form 861 data (Table 4). This reflects the experience of states and utilities in ramping up EE program efforts over time, with less experienced program administrators requiring time to increase savings from current levels. **8.2.5 Step 5: Determine Pace**  **of Improv**  **ement from Start Year to**  **EE Plan Scenario Level**  **of Performance**  To determine a reasonable trajectory of incremental savings levels from the 20 20 level to the EE plan level of performance , the EPA considered past performance of individual program administrators 35 as well as requirements of existing state EERS . For the past performance of individual program administrators, the EPA first screened the data and divided them into moderate and high performing sub - groups. The moderate group (47 entities) was defined as programs that achieved from 0.8% to 1.5% maximum incremental savings levels and the high group (26 entities) was defined as programs that achieved greater than 1.5% maximum incremental saving s levels. The EPA then calculated the rate at which each entity had increased savings over time and calculated average values for each sub - group. For the moderate group, the average rate of improvement of incremental annual savings rate was 0.30% per ye ar. For the high group, the average rate of improvement of incremental annual savings rate was 0.38% per ye ar. See Appendix 2 for supporting data and analysis. EPA also considered requirements of existing state EERS and evaluated the rate at which their incre mental savings levels increase over time. For several EERS, the EPA w as unable to 35 EIA 861 was the primary data source ; h owever, EIA 861 data was supplemented with data for third -party program administrators . Prior to 2011 EIA did not collect data from third - party program administrators.54

clearl y identify ramp - up schedules. The EPA identified ten states with clear schedules and c alculated the average rate of improvement for each. The average rate of improvement of incremental annual savings rate required for these ten states is 0.21% per ye ar. See Appendix 2 for supporting data and analy sis. Based on these results, the EPA has chosen 0.2% per year for the demand - side EE plan scenario rate of improvement. These values are at the low end of the range in comparison with our anal ys is of past state performance and state requirements. **8.2.6 Step 6: Determine Average Portfolio Measure Life and Distribution of Measure**  **Lives** The next step in defining the EE plan scenario entai ls projecting the cumulative future impacts of the annual incremental savings levels for each state. The incremental (or first - ye ar) savings impacts in a ye ar reflect the savings from EE measures put in place in that year. The cumulative incremental saving s in a given ye ar represent s the total impacts of all EE measures , those put in place in that year and all prior ye ars , that still have remaining savings impacts in the given ye ar . The cumulative savings account for the continuing impacts of EE measures th at remain in place for a period of time (the “measure life”) before being replaced. For example, the purchase of a high - efficiency refrigerator may

lead to savings for twelve years, before being replaced with a new model. To estimate cumulative impacts of a series of years of incremental savings, the industry

uses the concept of an average measure life for the entire portfolio of EE programs being administered . Rather than use a single, average measure life to represent a diverse portfolio of programs, that range in measure lives from less than ten ye ars (e.g., commercial lighting technologies and applications , residential behavioral feedback) to as long as twenty years or more (e.g., residential HVAC, residential building insulation) , the EPA is assuming a distribution of measure lives around the average to account for future impacts of incremental savings levels. To approximate a distribution of measure lives across a portfolio of EE programs represented b y the EE plan scenario, EPA relie d on a recent anal ys is by the L awrence Berkeley National Laboratory

(LBNL) of the distribution of EE program lifetimes ( LBNL 2015a ). The anal ys is was based on the LBNL DSM Program Database. The database consists of program level data, including first - ye ar s avings and program savings lifetimes, reported directl y b y the EE55

program administrators since 2009. Across the database of nearly 6,000 program years of data, program savings lifetimes are available for about 1,600 program y ears (27% of the program ye ars) . More than 50 utilities and other EE program administrators in 25 states contributed to those data through their regulatory filings, statewide databases, and other sources. The weighted average of EE measure lifetimes for the entire population in the LBNL analy sis is 10.2 years. The LBN L anal ys is further partitions the measure lifetimes of the EE programs into four tiers based on a cluster anal ys is

– a statistical approach for grouping values based on their similarity. 36 The four - tier distribution of EE pro gram measure lifetimes, as well as the associated weighted average measure lifetime and the shares of first - ye ar savings of each tier, are used in the analys is. Figure 5 presents LBNL’s four - tier distribution of the EE program measure lifetimes, and the as sociated weighted average measure lifetime and share of first - year savings for each tier. **F igure**  **5. The Four**  **- Tier Distribution of**  **Demand -**  **Side EE Program Measure Lifetime** 

Source : LBNL 2015a

36 The method used for the cluster analysis is the k - means approach. The method starts with assignment of each data point to a cluster so as to minimize the distance of cluster members from the center of the cluster, which is designated randomly. In essence , the method seeks to minimize differences within each cluster and maximize differences among the clusters. In this case, the programs within each cluster would have similar lifetimes and program types.56

The weighted - average EE program measure lifetime of 10.2 years for the entire population of data in the LBNL anal ys is is roughly consistent with a 2014 ACEEE study that found an average measure life of 10.6 ye ars based on electricity EE program data across all sectors in 20 states (ACEEE 2014d). A more common approach in other studies is to assume a portfolio with no diversity of measure lives, with the entirety of incremental savings being realized in each ye ar from the first through the full average measure life and then dropping to zero in the following ye ar. By comparison, the approach used here is a conservative one , leading to the same quantity

of total energy

savings, but with a greater portion of the savings occurring in later years than occur with the more common, simpler approach of using a single average measure life to represent the entire portfolio of EE programs. **8.2.7 Step 7: Determine Sustainability of**  **EE Plan Scenario Level of Performance**  For construction of the best practices scenario, once a stat e achieves the best practices level of performance, the EPA has kept the level of performance constant through the anal ys is period . Li mited empirical data suggests the reasonableness of this approach; however, comprehensive data, across all regions and states, does not exist because these levels of performance have not been achieved and sustained nationwide previousl y. The Northwest Power Conservation Council (NPCC) provides one such example. NPCC has been conducting the most consistent and long - running series of evaluations of achievable cost - effective potential in the country , updated every

five years, as part of their five - state 37 regional energy resource plans (NPCC 2010) . These analy ses have become more detailed, reliable, and purposeful over time. Since 1998, NPCC’s estimates of achievable potential have more than tripled even as evaluated electricity savings from EE programs have increased rapidl y, more than quadrupling between 1998 and 2010 (while levelized costs of saved energy achieved have remained flat), and exceeding plan targets every year since 2005. A study of the NPCC results concludes: “our research shows that when programs invest in higher levels of efficiency , this helps drive measurement improvements and technical innovation, resulting in l arger and more reliable conservation suppl y estimates” (NPCC 2008). Table 1 5 summarizes the NPCC’s achievable potential estimates and evaluated savings since 1998 (NPCC 2010) .

37 NPCC’s resource plans cover Idaho, Oregon, and Was hington in their entirety, and western regions of Montana and Wyoming.57

**T able**  **1 5.**  **NPCC Achievable EE Potential and Achieved Incremental Savings (1998 -**  **20 10)**  **Year 1998**  **2005 2010**  **Achievable Potential over 20 Years**

**(GWh)** 13,447 24,651 51,684 **Achieved Incre mental Savings**  **from EE Program**  **s (GWh)**  547 1,184 2,248 Source : NPCC 2010 Additional substantiation of this approach is provided by average annual achievable rates from reviewed studies, as discussed previousl y, and comparison of those with the rates resulting from the best practices scenario. We address this in a later section, 8.4.2, after presenting those results. **8.2.8 Summary of**  **the Demand -**  **Side EE Pla n Scenario**  **Construction** Table 1 6 provides a summary of inputs for the demand - side EE plan scenario. The pace of improvements, average measure life, and distribution of measure lives are each conservative and, therefore, lead to lower cumulative savings than would otherwise result. Similarly , the EE plan scenario , being based solely on results from and requirements of EE programs, is less stringent than a level would be that accounted for potential impacts of other EE strategies such as building energy

codes , state appliance standards , or energy services performance contracting . **T able**  **1 6.**  **Summary of Demand**  **- Side EE Plan**  **Scenario Inputs Input**  **Dem and**  **- Side EE Plan Scenario**  **Current Level of Perform ance**  **(incremental savings as % of retail sales)** Calculated using data from EIA 861 (201 3 ) **EE Plan Scenario Level of Perform**  **ance (incremental savings as % of retails sales)**  1. 0 %

**Start Year** 20 20 **Start Year Level of Performance** 2013 level of performance **Pace of Im provement**  **(increase in incremental savings rate per year)**  0.20% per year **Average Measure Life and Distribution of**

**Me asure Lives**  10 .2 years with a 4 - tier distribution of measure lifetime s (LBNL database and analysis) **Continued Perform ance**  Once achieved, EE plan scenario savings level is sustained Source: Section 8.258

**8.3 Calculations**  This section presents the calculations for determining cumulative savings levels for each state, for each ye ar based on the demand - side EE plan scenario . The cumulative savings levels are derived based upon the key inputs summariz ed in Table 1 6 . Calculating the net cumulative savings of the demand - side EE plan scenario involves six steps. For each state, for each ye ar ( 2020 - 2030) the following steps are taken: 1. Determine annual business - as - usual (BAU) sales 2. Determine annual incremental EE savings as a percentage of sales 3. Determine annual incremental EE savings (GWh) 4. Determine annual expiring EE savings (GWh) 5. Determine net cumulative EE savings (GWh) 6. Determine sales after net EE (GWh) 7. Determine net cumulative EE savings as a p ercentage of BAU sales To illustrate these calculations, each step is described and results provided for one state (using South Carolina as an example ) for 2020 through 202 7 . We truncate the results at 202 7 for simplicity , but all calculations and full res ults are presented for all states for all years in the spreadsheet file included as Appendix 3 . **8.3.1 Step 1: Determine Annual Business as Usual (BAU) Sales**  BAU sales are derived by aggregating 201 3 sales to the state level using EI A Form 861 data and increasing the value for each subsequent year by the average annual growth rate (AAGR) from the AEO 201 5 Reference Case for the region corresponding to the state. Specificall y, the AAGR from AEO 201 5 for 201 3 - 2040 is used to derive BAU sales f or 201 4 through 2040 , and the AAGR from AEO 201 5 for 2030 - 2040 is used to derive BAU sales f or 2041 through 2050 . For South Carolina the corresponding region is S RVC and the AAGR from 201 3 - 2040 is 0.88 % per year and from 2030 - 2040 is 0.76% per year . The resulting values are p rovided in Table 1 7 for South Carolina.59 **T able**  **1 7.**  **BAU Sales for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  BAU Sales (GWh) 83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918 **8.3.2 Step 2: Determine Annual Incremental EE Savings as a**  **Percentage of Sales** Next, the 2020 value for annual incremental EE savings as a percentage of sales is set at the 2013 value based upon aggregated EIA - 861 reported data (see Table 4). For South Carolina that value is 0.46%. This value is then increased by the pace of improvement of 0.2% per ye ar until the goal level of 1.0% is reached and then held constant. The resulting values are provided in Table 18 for South Carolina. **T able**  **1 8.**  **Annual Incremental EE**  **Savings as a Percentage of Sales for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Annual Incremental**  **EE Savings**  **(% sales)** 0. 46 % 0.66 % 0.86 % 1. 0 0% 1. 0 0% 1. 0 0% 1.00% 1.00%

**8.3.3 Step 3: Determine Annual Incremental**  **EE Savings (GWh)**  Annual incremental EE savings (also known as first - ye ar savings) are calculated by multiplying the annual incremental savings as a percentage of sales times the prior year sales after net EE . The resulting values are provided in Table 1 9 for South Carolina.

**T able**  **1 9.**  **Annual Incremental EE**  **Savings (GWh)**  **for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Annual Incremental**  **EE Savings (GWh)**  385 553 721 834 833 833 832 83260

**8.3.4 Step 4: Determine Annual Expiring EE Savings (GWh)**  As discussed above (section 8.2.6) , the annual incremental (or first - ye ar) EE savings of the portfolio decline (or expire) over time according to the four - tier distribution of measure lives. Expiring EE savings are calculated as the sum of all expired savings in a given year from all prior ye ars’ incremental (first - ye ar) savings . The resulting values for expiring EE savings are provided in Table 20 for South Carolina.

**T able**  **20. Expiring EE Savings for South Carolina**  **Year 2020**  **2021 2022**  **2023 2024**  **2025 2026**  **2027 Expiring**  **EE Savings**  **(GWh)** 0 0 0 0 0 0 69 165 **8.3.5 Step 5:**  **Determine Net Cumulative EE Savings (GWh)** Net cumulative EE savings in a given ye ar are equal to annual incremental savings for that year minus total expiring savings for that year plus net cumulative savings for the prior ye ar. The resulting values are provided for South Carolina in Table 21 .

**T ab**  **le 2**  **1 .**  **Net Cumulative EE**  **Savings for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Net Cumulative**  **EE Savings (GWh)**  385 938 1,659 2,493 3.327 4,159 4,922 5,589 **8.3.6 Step 6: Determine Sales after Net EE (GWh)**  Sales after net EE are calculated by subtracting net cumulative savings from BAU sales. The resulting values are provided in Table 22 for South Carolina.61 **T able**  **22. Sales after Net**  **EE Savings for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Sales after Net EE**  **Savings (GWh)**  83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329 **8.3.7 Step**  **7 : Determine Net Cumulative EE Savings as a Percentage of BAU Sales**  Net cumulative EE savings as a percentage of BAU sales are equal to net cumulative savings divided b y BAU sales. The resulting values are summarized for South Carolina in Table 23 . **T able**  **23. Net Cumulative EE Savings as a Percentage of BAU Sales for South**  **Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Net Cumulative**  **EE Savings as % of**  **BAU Sales** 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29%

**8.3.8 Summary of**  **Calculation Formulas and Results by Step for South Carolina**  Table 24 provide s summaries of the results for South Carolina for each step , respectivel y .

**T able**  **2 4.**  **Summary of Results by Step for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **BAU Sales (GWh)**  83,601 84,341 85,087 85,840 86,599 87,365 88,138 88,918 **Annual Incremental**  **EE Savings (% sales)**  0.46% 0.66% 0.86% 1.00% 1.00% 1.00% 1.00% 1.00% **Annual Incremental**  **EE Savings (GWh)**  385 553 721 834 833 833 832 83262 **Year 2020**  **2021 2022**  **2023 2024**  **2025 2026**  **2027 Annual**  **Expiring EE Savings**  **(GWh)** 0 0 0 0 0 0 69 165 **Net Cumulative**  **EE Savings (GWh)**  385 938 1,659 2,493 3.327 4,159 4,922 5,589 **Sales after Net EE**  **(GWh)** 83,216 83,403 83,428 83,346 83,272 83,206 83,216 83,329 **Net Cumulative**  **EE Savings as % of BAU**  **Sales** 0.46% 1.11% 1.95% 2.90% 3.84% 4.76% 5.58% 6.29% **8.4 Results**  **8.4.1 Summary of Results**  As discussed, the EE plan scenario results in reduced electricity demand in each state, in each ye ar of the plan scenario. Table 25 provides the results for 2020, 2025, and 2030, as electricity demand reduced in gigawatt - hours and as a percentage of BAU sales for the ye ar. F ul l results are presented for all states for all ye ars in the spreadsheet file included as Appendix 3 .

**T able**  **2 5. Demand**  **- Side EE Plan Scenario: Magnitude and** 

**Timing of Net Cumulative Savings States**  **Net Cumulative Savings 2020**  **2025 2030**  **GWh % of BAU** 

**Sales GWh**  **% of BAU Sales**  **GWh % of BAU**  **Sales** Alabama 91 0.10% 3,287 3.37% 7,258 7.13% Arizona 797 0.99% 4,786 5.65% 7,195 8.09% Arkansas 241 0.48% 2,519 4.81% 4,279 7.80% California 2,735 0.99% 16,310 5.70% 24,310 8.19% Colorado 524 0.91% 3,367 5.58% 5,116 8.06% Connecticut 305 1.00% 1,805 5.77% 2,658 8.34% Delaware 1 0.01% 352 2.98% 849 7.06% District of Columbia 51 0.45% 556 4.81% 938 7.99% Florida 389 0.17% 9,054 3.69% 18,673 7.31% Georgia 357 0.26% 5,889 4.07% 11,314 7.48%63 **States Net Cumulative Savings**  **2020 2025**  **2030 GWh**  **% of BAU**

**Sales GWh**  **% of BAU Sales**  **GWh % of BAU**  **Sales** Idaho 137 0.54% 1,327 4.98% 2,195 7.91% Illinois 1,458 1.00% 8,633 5.76% 12,742 8.31% Indiana 1,006 0.92% 6,347 5.69% 9,459 8.29% Iowa 484 0.99% 2,878 5.73% 4,270 8.25% Kansas 2 0.01% 1,265 2.95% 3,080 6.97% Kentucky 337 0.37% 4,232 4.50% 7,544 7.68% Louisiana 23 0.02% 2,905 3.02% 6,983 6.92% Maine 121 1.00% 717 5.77% 1,056 8.34% Maryland 632 1.00% 3,729 5.78% 5,482 8.36% Massachusetts 566 1.00% 3,344 5.77% 4,925 8.34% Michigan 1,055 1.00% 6,232 5.77% 9,176 8.34% Minnesota 712 0.99% 4,230 5.73% 6,275 8.25% Mississippi 119 0.23% 2,159 3.95% 4,244 7.40% Missouri 420 0.49% 4,324 4.89% 7,230 7.97% Montana 79 0.53% 770 4.98% 1,274 7.91% Nebraska 49 0.15% 1,205 3.65% 2,500 7.35% Nevada 176 0.47% 1,862 4.81% 3,162 7.84% New Hampshire 17 0.15% 425 3.67% 875 7.42% New Jersey 531 0.69% 4,202 5.40% 6,514 8.24% New Mexico 138 0.56% 1,300 5.02% 2,140 7.87% New York 1,477 1.00% 8,637 5.86% 12,537 8.51% North Carolina 814 0.59% 7,385 5.12% 11,984 7.95% North Dakota 3 0.02% 517 3.00% 1,244 7.00% Ohio 1,545 1.00% 9,150 5.76% 13,506 8.31% Oklahoma 168 0.26% 2,744 4.07% 5,276 7.45% Oregon 500 0.99% 2,986 5.69% 4,460 8.17% Pennsylvania 1,351 0.90% 8,677 5.70% 12,915 8.33% Rhode Island 80 1.00% 471 5.77% 693 8.34% South Carolina 385 0.46% 4,159 4.76% 7,117 7.79% South Dakota 16 0.13% 464 3.54% 986 7.28% Tennessee 320 0.31% 4,586 4.26% 8,503 7.57% Texas 801 0.20% 16,359 3.82% 32,959 7.32% Utah 257 0.81% 1,823 5.53% 2,789 8.12% Vermont 57 1.00% 338 5.77% 498 8.34% Virginia 28 0.02% 3,707 3.02% 8,907 6.94% Washington 975 0.99% 5,822 5.69% 8,696 8.17%64 **States Net Cumulative Savings**  **2020 2025**  **2030 GWh**  **% of BAU**

**Sales GWh**  **% of BAU Sales**  **GWh % of BAU**  **Sales** West Virginia 79 0.24% 1,347 4.06% 2,580 7.59% Wisconsin 710 1.00% 4,201 5.76% 6,197 8.31% Wyoming 31 0.17% 700 3.73% 1,433 7.33% Navajo 0 0.00% 21 2.90% 53 6.85% Ute 0 0.00% 17 2.91% 41 6.90% Fort Mojave 0 0.00% 2 2.90% 4 6.85% Continental U.S. 23,150 0.59% 194,126 4.81% 327,092 7.83% Alaska 2 0.04% 161 3.13% 376 7.04% Hawaii 3 0.04% 284 3.10% 668 7.03% Puerto Rico 0 0.00% 528 2.97% 1,271 7.14% Guam 0 0.00% 46 2.97% 112 7.14% U.S. Total 23,156 0.59% 195,145 4.80% 329,518 7.82% Source: Appendix 3 **8.4.2 Results in Context**  To provide context for state cumulative savings results provided in Table 2 6 , the average annual savings were calculated for each state through 2030 , starting from 20 20 . Table 22 summarizes the results. **T able**  **2 6.**  **Average Annual Savings Rates of Demand**  **- Side EE Plan**  **Scenario Years**  **Number**

**of Years Range of Cum**  **ulative**

**Savings (% of Sales) across States in 2030**  **Range of Average Annual Savings Rates**  **across States National Average Annual**  **Savings Rate** 2020 - 2030 11 6.92% to 8.36% 0.63%/year to 0.76%/year 0.71% per year Source: EPA calculations based on Table 25 The state range and national value for the average annual savings rate represented in the demand - side EE plan scenario are below the range of values found in recent utility , state, and regional studies ( 0.9% to 1.3% per ye ar) as summarized in Table 6 . These results provide additional support for the reasonableness and feasibility of the demand - side EE plan scenario and associated st ate - specific results .65

**8.4.3 Representation of Demand**  **- Side EE**  **in Modeling of the Illustrative Plan Scenario** To reflect the implementation of the illustrative energy efficiency

plan scenario in power sector modeling, as discussed in the RIA (Chapter 3), the IPM ba se case electricity demand was adjusted exogenousl y to reflect the estimated future - ye ar demand reductions described and summarized above. State - level demand reductions were scaled up to account for transmission losses and applied to base case generation d emand in each model ye ar to derive adjusted demand for each state, reflecting the energy efficiency

plan scenario energy

reductions. The demand adjustments were applied proportionally across all segments (peak and non - peak) of the load duration curve. This approach to allocating annual electricity reductions is consistent with the significant levels of savings represented in the illustrative demand - side plan scenario. Achieving 1.0% incremental savings ty picall y require robust EE program portfolios, employi ng a diverse set of measures across all sectors and most end - use categories. The impacts of such comprehensive portfolios occur across the full annual load duration curve with higher periods of demand providing proportionally greater EE potential. In other words, potential demand reductions through EE measures tend to mirror the underlying load duration curve which, in turn, is formed from the demands from all end uses, across all end - use sectors. Appendix 1 provides summary results from more than 50 recent EE potential studies

– conducted between 2009 and 2014

– from across the U.S. The detailed results of these studies indicate the breadth of end - uses, across all end - use sectors, which provide cost - effective EE potential. 

### 9. **Demand -**  **Side EE Plan Scenario: Asse**  **ssment of Costs**  **9.1 Approach**  This section provides the basis for estimating the costs that would be incurred to achieve the magnitude and timing of EE savings represented by the EE plan scenario. Chapter 3 of the Regulatory

Impacts Analy sis (RIA) presents illu strative anal ys is of the final rule by showing approaches that states might pursue as they develop their state plans. The scenarios used in the RIA are the basis for costs, benefits, and other power sector impacts of the final rule. The final rule allows s tates to use demand - side EE as a component of their state plan strategy , either directly recognized towards compliance with a rate - based goal or as a complimentary approach for achieving a mass - based goal. The EPA has included in the illustrative plan scen arios (both rate - and mass - based) a level of demand reduction that could be achieved, and the associated66

costs incurred, through implementation of demand - side EE measures as shown in this TSD . In this section, the EPA provides the basis for the costs of th e illustrative demand - side EE plan scenario that is included as a component of the plan scenarios presented in the RIA. The electricity

savings resulting from the demand - side EE methodology was used to adjust electricity demand levels in an exogenous manner, applied to the power sector modeling for the illustrative plan scenarios of the RIA. In other words, the degree to which EE is employed is not determined endogenously within the power sector modeling based upon optimization of costs but, rather, “h ard wired” into the illustrative plan scenarios. The electricity savings represented in the demand - side EE plan scenario lead to substantial reductions in power s ys tem costs due to the reductions in specified electricity demand. Since EE is not represented endogenousl y as an abatement measure within the power sector modeling, the costs associated with the EE plan scenario must be estimated outside of the power sector modeling and integrated with the results from that modeling. These EE cost estimates, their basis, and calculations are addressed in the following sections. **9.2 Inputs**  The following steps were taken to establish the inputs for development of the EE cost estimates for each state in each year .  Step 1: Determine incremental electricity savings  Step 2: Determine first - year program costs and cost factors  Step 3: Determine the ratio of program to participant costs **9.2.1 Step 1: Determine**  **Incremental Electricity Savings**  Results from section 8 provide the starting basis for estimation of EE costs used in the RIA . The resulting incremental savings (MWh) by state and by

year provide the first step in calculating EE plan scenario costs. **9.2.2 Step 2: Determine First**  **- Year Program Costs**  **and Cost Factors** First - year program costs refer to the full costs (e.g., incentive pay ment s, administration, technical support, marketing, evaluation, measurement and verification (EM&V), information to consumers, etc.) incurred by a utility or other administrator of EE programs in a given ye ar that67

lead to EE measures (technologies and practi ces) put in place in that year , and result in reductions in electricity

demand in that and future years (driven by the mix of measure lives across the portfolio of EE programs emplo yed). First - ye ar program costs are represented as these costs divided by

th e incremental (or first - ye ar) savings for that year. Unlike participant costs, program costs are readil y known by the administrator of EE programs and are, therefore, an appropriate starting point for cost anal ys is of the demand - side EE plan scenario . Rec ent studies have collected and anal yz ed first - year program cost data directly from EE program administrators from across the U.S. In 2009, ACEEE conducted a national review of data on EE program costs from program annual reports, evaluation reports, and in formation compiled from contacts at program administers in 14 states (ACEEE 2009) . The program cost data were compiled from multiple EE program administrators over multiple years in each state. ACEEE found average first - ye ar net 38 program costs of $275/MWh (2011$). Two newer national anal ys es have found lower program costs than the 2009 ACEEE study . In 2014, ACEEE updated their anal ys is from 2009, expanding the number of states to 20, and including a greater number of program administrators and years (ACEEE 2014d) . In this anal ys is ACEEE found average first - ye ar net program costs of $230/MWh (2011$). In 2014, an LBNL study presented results from a uniquel y comprehensive study of EE program costs (LBNL 2014b) . The LBNL anal ys is reviewed program - level data from over 100 program administrators in 31 states. Data were collected from over 1,700 individual programs for up to three ye ars (2009 - 2011), covering more than 4,000 individual program - ye ars data points. Because of the broad scope of their study and the lack of net savings information for many programs, LBNL focused on gross , rather than net, savings values. 39 LB NL found national average first - ye ar program cost of gross savings of $162/MWh (2012$). Applying an average net - to - gross ratio of 0.9 and deflating co sts at 1.8 %, results in an estimated national average first - ye ar cost of net savings of $17 7 (2011$). 40 The results cited from these three studies represent average first - ye ar costs from program administrators across the U.S.

38 “Net costs” refers to costs per electricity saved after accounting for effects of free - ridership on those savings . Depending upon the state, spillover effects may also be accounted for in net costs. 39 “Gross savings” refers to electricity savings before any accounting for effects of free - ridership or spillover. 40 Source: BEA National Income and Product Accounts Table 1.5.4 Price Indexes for Gross Domestic Product (version revised on March 27, 2015).68

There are two factors that would influence first - ye ar EE costs as higher levels of performance are achieved. Economies of scale in the operation of larger EE programs and larger portfolios of EE programs, and learning and expertise gained over time from the continued implementation of programs, are two factors that would lower costs as programs scale up and expand to realize higher levels of performance (Jaffe et al. 2003) . However, the limited supply of EE abatement measures and the need to employ higher cost measures, over tim e, to reach higher levels of performance, and to sustain high levels of performance, are factors that would increase costs as higher levels of performance are achieved. Several analy ses of EE program costs and savings based on empirical data are consistent with th is , indicating significantly declining costs up to incremental savings levels of 1.0% or higher, and then level or increasing costs as higher savings levels are achieved (Sy napse 2008; GEEG 2012; LBNL 2015b). Based on anal ys is like these, a recent LBNL study adopted an approach that generically represented declining costs at lower savings levels and increasing costs at higher savings levels ( Barbose et al. 2013 ). Counter to these studies, other analy sis of empirical data has indicated onl y steadil y rising costs as savings levels increase even from levels below 0.5% (ACEEE 2014d). In consideration of the above discussion, the EPA has chosen to use a three - tier approach to genericall y represent declining first - year program costs through the maximum sav ings level of 1.0% represented in the EE plan scenario. The first tier is used for incremental savings levels less than 0.5%; the second tier is used for incremental savings levels equal to or greater than 0.5% and less than 1.0%; and the third tier is use d for incremental savings levels of 1.0% (the highest level assumed for demand - side EE plan scenario). To reflect the significantly higher costs found at the lowest savings levels, program costs for the first tier are set at two times the level found in th e 2009 ACEEE study . This results in first - ye ar program costs of $550/MWh ($2011) for the first tier. For the second and third tiers, these costs are reduced b y 20% and 40%, respectively, resulting in first - ye ar program costs of $440/MWh and $330/MWh. The a pproach results in reasonable but conservative cost assumptions for analyzing the demand - side EE plan scenario. **9.2.3 Step 3: Determine the Ratio of Program to Participant Costs**  As noted above, while program costs are readily known and consistently tracked and reported b y program administrator s , participant costs are not easil y known , and are less consistently

estimated and reported. The ratio between program and participant costs will vary69

significantl y from one program to the next within a utility ’s portfolio. To determine an appropriate ratio for the impacts assessment of the CPP proposed rule, EPA conducted research and anal ys is of industry

data (annual EE program reports from administrators in 22 states) and found that on average program costs represented 53% of total measured costs (with direct participant costs representing the remaining 47%) (EPA 2014a). Based on this analy sis, the EPA used a ratio of 1 - to - 1 for program to participant costs for the EE cost estimates contained in the CPP proposed rule. In Ap ril 2015 the Lawrence Berkeley National Laboratory

(LBNL) published a technical brief that anal yz ed program and participant costs for EE programs based on their extensive LBNL DSM Program Database (LBNL 2015b). The database contains information on approxim ately 1,700 individual EE programs across multiple years from 34 states. The LBNL summary findings indicate a ratio of program to participant costs of 51% to 49%. These results are consistent with the earlier anal ys is and support the use of the 1 - to - 1 rat io for the current anal ys is. This ratio is used to derive participant and total costs based upon program costs. **9.2.4 Summary of Inputs for**  **Demand -**  **Side EE Cost Analysis**  Table 2 7 provides a summary of inputs for the EE cost anal ys is including incremental electri city savings, first - year costs and cost factors , and ratio of program to participant costs.

**Table 2 7.**  **Summary of EE**  **Cost Analysis Inputs Input**  **Source or Value** Incremental Electricity Savings Results from demand - side EE plan scenario First - Year Program Cost $550/MWh (2011$) Ratio of Program to Participant Costs 1:1 First - Year Participant Cost $550/MWh (2011$) First - Year Total Cost $1100/MWh (2011$) Cost Factors and Total Costs by Level of Incremental Savings Incremental Savings Rate < 0.5% 0.5% to < 1.0% 1.0% 100% of first - year costs $1100/MWh (2011$) 80% of base costs: $880/MWh (2011$) 60% of base costs: $660/MWh (2011$) Source: Section 9.270

**9.3 Calculations**  This section addresses the methodology for calculati ng the cost associated with the EE levels included in the illustrative plan scenario s . Specificall y, three values are calculated (annual first - ye ar costs, levelized cost of saved energy (LCSE), and annualized costs); for each, program and participant components are then calculated using th e 1:1 ratio (i.e., 50% of total for each) derived above. Specific results from prior sections on timing and magnitude of savings, and costs inputs are used for these calculations. For each state, the following steps are taken for each ye ar : 1. Calculate annual first - ye ar costs of saved energy 2. Calculate levelized cost of saved energy (L CSE) 3. Calculate annualized costs To illustrate these calculations, each step is described and results are provided for one state (using South Carolina as an example) for 2020 through 2027 (truncated for simplicity) . The full national results ( for 2020, 2025, and 2030) are presented following the step - by- step example for South Carolina . **9.3.1 Step 1: Calculate Annual First**  **- Year Costs**  Annual total first - year costs are calculated b y multiplying annual total incremental savings (MWh) (from Table 1 9 ) b y the first - ye ar total cost of saved energy (from Table 2 7, using the corresponding incremental savings rate ). Program and participant portions of the first - ye ar costs are then calculated a s 50% of total first - ye ar costs for each per Table 2 7 . The resulting values are summarized for South Carolina in Table 28. **T able**  **2 8.**  **Calculation of Annual First -**  **Year Costs for South Carolina Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Annual Incremental**  **Savings (GWh)**  385 553 721 834 833 833 832 832 **First -**  **Year Total Cost of**  **Saved Energy (2011$/MWh)**  1100 880 880 660 660 660 660 66071 **Year 2020**  **2021 2022**  **2023 2024**  **2025 2026**  **2027 First**  **- Year**  **Total Cost (m** **illions 2011$)**  423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 **First -**  **Year Program**  **(m illions of**  **2011$)** 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6 **First -**  **Year Participant**  **(m illions of**  **2011$)** 211.7 243.3 317.3 275.3 275.0 274.8 274.6 274.6 **9.3.2 Step 2: Calculate Levelized Cost of Saved Energy**  The LC SE calculation algorithm is based on the 2002 California Standard Practice Manual (California GOPR 2002) . T his approach was adopted in order to appropriatel y allocate the cost of a single ye ar EE program over declining lifetime savings. The net present value of all savings from a single year’s EE activities (i.e., over the entire distribution of program lifetimes) is calculated using the real discount rate rate s of 3% and 7% . The levelized cost of saved energy is then calculated b y dividing the annual first - ye ar costs (from Table 2 8 ) b y the net present value of the savings. The resulting values are summarized for South Carolina in Table 2 9 . **T able**  **2 9.**  **Levelized Cost of Saved Energy for South Carolina (at 3% discount rate) Year**  **2020 2021**  **2022 2023**  **2024 2025**  **2026 2027**  **Levelized Savings (GWh)**  3,279 4,710 6,141 7,105 7,099 7,092 7,087 7,087 **First -**  **Year Total Cost**  **(m illions 2011$)**  423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 **Total LCSE (**  **2011$/MWh )**  129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5 **Program LCSE**  **( 2011$/MWh**  **)** 64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.7 **Participant LCSE (**  **2011$/MWh )**  64.6 51.7 51.7 38.7 38.7 38.7 38.7 38.7 **9.3.3 Step 3: Calculate Annualized Costs**  Annualized costs are calculated by multiplying the L CSE for each ye ar b y the estimated savings of the demand - side EE plan scenario in that ye ar through the full distribution of measure72

lifetimes. For a given ye ar in the anal ys is, the total annualized costs resulting from all current and past investments are summed to calculate the total annualized costs for that year. The resulting value s are summarized for South Carolina in Table 30 .

**T able**  **30. Annualized Costs for South Carolina**  **Year 2020**  **2021 2022**  **2023 2024**  **2025 2026**  **2027 Annualized**  **Total Costs (m** **illions 2011$)**  49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.4 **Annualized Program**  **Costs (m** **illions 2011$)**  24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2 **Annualized Participant**  **Costs (m** **illions of 2011$)**  24.9 53.4 90.7 123.0 155.3 187.6 215.3 238.2

**9.3.4 Summary of General Formulas and Results by Step for South Carolina**  Table 31 provide s results for South Carolina for each step.

**T able**  **31. Summary of Results by Step for South Carolina.**  **Year 2020**  **2021 2022**  **2023 2024**  **2025 2026**  **2027 Total First**  **** **Year Costs (m** **illions 2011$)**  423.4 486.6 634.5 550.6 550.1 549.6 549.2 549.2 **Total LCSE (2011$/MWh)**  129.2 103.3 103.3 77.5 77.5 77.5 77.5 77.5 **Annualized Total Costs**  **(m illions**  **2011$)** 49.7 106.9 181.4 246.0 310.6 375.1 430.7 476.473

**9.4 Results**  **9.4.1 Summary of National Results**  Tables 32 and 3 3 provide the national first - ye ar and annualized EE costs for the best practices EE scenario for 2020, 2025, and 2030. Table 3 4 provides the estimated national L CSE for the same ye ars. Each of the three tables includes values for program, participant, and total costs. **T able**  **32. First**  **- Year**  **Demand -**  **Side EE Costs (billio**  **ns 2011$) (Continental U.S.)** 

**Year 2020**  **2025 2030**  **Program** 9.1 12.7 12.7 **Participant** 9.1 12.7 12.7 **Total** 18.1 25.4 25.3 **T able**  **3 3.**  **Annualized Demand**  **- Side EE**  **Costs (3% discount rate, billions 2011$) (Continental U.S.)** 

**Year 2020**  **2025 2030**  **Program** 1.1 8.4 13.2 **Participant** 1.1 8 .4 13.2 **Total** 2.1 16.7 26.3 **T able**  **3 4.**  **Levelized Cost of Saved Energy (3% discount rate, 2011$/MWh) (Continental U.S.)** 

**Year 2020**  **2025 2030**  **Program** 4 6.0 4 3.1 4 0.3 **Participant** 4 6.0 4 3.1 4 0.3 **Total** 92.0 86.3 80.5 F ull calculations and results are presented for all states for all ye ars in the spreadsheet file included as Appendix 3 . The file includes results for LC SE (total, program and participant , discounted at 3% and 7% ), first - year costs (total, program and parti cipant), and annualized costs (total, program and participant , discounted at 3% and 7% ).74

**9.4.2 Results in Context**  To provide context for the pace of increase in EE program spending levels represented b y the demand - side EE plan scenario , we consider the compound annual growth rate (CAGR) of the recent rapid increase in historic investment (2006 to 2011) and the CAGR from 201 3 (the most recent historic value) through 2020, 2025 , and 2030 represented b y program costs of the plan scenario . Historic data is from Table 2 and EE plan scenario data is from Table 32 . Table 3 5 provides a summary

of the results. The CAGRs represented b y the EE plan scenario through 2020, 2025 , and 2030 vary from 4 % to 6 %. The historic growth rate reflecting the rapid recent growth in EE program spending is 30%, roughly six times the EE plan scenario values. The pace of increase in EE program spending represented by the demand - side EE plan scenario is modest and much lower than recently achieved performance. **T able**  **3 5.**  **Annual Growth Rates of De**  **mand -**  **Side EE Program Spending**  **– Historic and**  **Demand -**  **Side EE Plan Scenario Time Period (Years)**  **Compound Average Growth Rate** Historic (2006 - 2011) 29.8% Demand - Side EE Plan Scenario (201 3 - 20 20 ) 5.4% Demand - Side EE Plan Scenario (201 3 - 202 5 ) 6.0% Demand - Side EE Plan Scenario (201 3 - 20 30 ) 4.2% **9.4.3 Costs per Tonne CO**  **2 Reduced**  For the CPP proposed rule, the EPA anal yz ed a scenario incorporating the reductions in electricity demand associated with the proposal’s “EE scenario” and compared the results with the base case scenario (EPA 2014a). Both anal ys es were conducted using the Integrated Planning Model (IPM) described in RIA . Combining the resulting power s ys tem cost reductions with the energy efficiency

cost estimates associated with the EE scenario, ne t cost impacts were derived for 2020, 2025, and 2030. Dividing these net cost impacts by the associated CO 2 reductions for each ye ar, the average cost of the CO 2 reductions achieved were found to range from $ 16 to $ 24 per metric tonne of CO 2 . These results confirmed the general cost - effectiveness of demand - side EE at the levels and costs of the proposal, and relative to the IPM base case used at that time (since updated). The final rule incorporates changes to the IPM base case, and the levels (significantl y lower) and costs (higher in 2020, lower in 2025 and 2030) of demand - side EE incorporated into the illustrative plan scenario. These changes are likel y to result in similar or75

lower average costs per tonne of CO2 achieved through the demand - side EE plan s cenario in the final rule. 

### 10. **Analysis Considerations** Two considerations are worth noting in regards to the anal ys is described in the previous two sections (9 and 10) which characterize the energy savings and costs of the demand - side EE plan scenario : 1) the implicit representation of demand - side EE policies and programs in the base case electric demand forecast and 2) Form EIA - 861 as a data source. **10.1 State**  **EE Policies in the Baseline Electricity Demand**  The AEO 2015 reference case provides the basis for the base case electric demand forecast used for EPA’s power sector modeling of the final rule (as discussed in the RIA) and also serves as a basis for estimates of electricity savings for the demand - side EE plan scenario. The AEO 201 5 reference case does not expli citly represent existing EE programs , the continuation of EE programs or policies, or future requirements (e.g., EERS) to achieve savings through such programs. For example, existing state EERS are not evaluated and represented in the AEO 201 5 reference ca se. However, to some degree, AEO 201 5 may implicitly reflect a continuation of the effects of past or existing EE programs in the electricity demand projections represented in the reference case. This implicit representation is captured in part through a c alibration process that is affected b y several historic factors including reported electricity sales and sectoral energy

consumption survey s. As noted, the estimates of electricity savings for the demand - side EE plan scenario do rely on AEO 2015 as a basis for base case electricity demand projections. The underl yi ng mechanics of the calculations are such that modest changes in base case demand growth rates lead to minor impacts on cumulative savings levels relative to other inputs such as 2020 starting savi ngs levels (based on 2013 EIA 861 data), the target savings level (i.e., 1.0% incremental annual savings), and the pace of improvement from the starting to target savings levels (i.e., 0.2% per ye ar). An y implicit representation of the effects of existing EE programs in the base case demand forecast would also, to some degree, affect estimates of the impacts of the final rule in the RIA. The effects of these considerations would have some degree of influence on the assessment of demand - side EE, however, for purposes of the RIA, the methodology for76

determining levels of demand - side EE, and associated costs, is appropriate in the context of the final rule. **10.2 Energy Information Administration Form EIA**  **- 861 as Data Source**  Comprehensive national data on electricity savings are limited for evaluating and comparing energy

efficiency

programs and their effectiveness at the utility , state, and national scale. Issues related to the lack of standardized definitions and reporting, and data quality are noted to limit evaluat ion of EE programs ( MJ Bradley 2011) . The EIA Form 861, “Annual Electric Power Industry

Report,” remains the most comprehensive effort that collects data annually on demand - side management (DSM) programs, including their spending and energy savings impacts, nationally . 41 The form is requested for electric utilities, electric power producers, energy

service providers, wholesale power marketers, and all DSM program managers and entities responsible to estimate the DSM activity for the reporting ye ar usi ng their best available data, including costs and electricity savings from EE and load management programs. This analy sis uses onl y two EIA - 861 data variables. Specificall y, we use the 201 3 sales data and reported incremental annual electricity savings of EE programs to estimate the current performance of EE programs (aggregated to the state level) to inform the illustrative demand side EE plan scenario discussed in this chapter . EPA notes potential concerns associated with consistency and quality

of reported DSM program data in Form EIA - 861. Specifically , the data are self - reported b y utilities and other DSM program administrators. The definition s and data categories may not be consistently applied across different program administrators, utilit ies , and st ate s , and may vary by data ye ar. Over time, however, the data quality has improved significantl y and there is increased standardization in data reporting and more detailed and up - to - date data categories are being reported. For instance, in 2011, EIA began collecting data from third - party administrators of programs and now requires that entities operating programs in multiple states report results disaggregated to the state level . While now comprehensive, outside entities have found that the

41 More information on EIA Form 861 can be found at http://www.eia.gov/electricity/data/eia861/ .77

EIA - 861 data can be improved through supplementation with publicly available annual EE program reports. 42

42 See, for example, ACEEE 2014c and CEE 2015.78 

### Appendices **Appendix 1: Summary of Recent Energy Efficiency Potential Studies (2009**  **- 2014)**  **Appendix 2: Incremental Electricity Savings Pace of Improvement Analysis**  **Appendix 3: Demand**  **- Side Energy Efficiency Plan Scenario: Data, Calculations and Results** 79 

### Appendix 1 **: Summary of Recent Energy Efficiency Potential Studies**  **(2009 -**  **2014)** The EPA conducted an extensive review of energy efficiency potential studies of electricity savings. Studies were identified from recent published meta - analy ses (Sreedharan 2013; ACEEE 2014 b ) and supplemented with additional studies identified through research. Comprising results from more than 50 potential studies conducted between 2009 and 2014, the resulting database of results is one of the most comprehensive of its kind. See the attache d file for detailed information on the sources, data, calculations, and results:

 GHG Abatement TSD

– Demand - Side EE Potential Studies.xlsx80 

### Appendix 2 **: Incremental Electricity Savings Pace of Improvement Analysis**  This appendix summarizes and anal yz es data to characterize the pace of improvement of incremental (or first - ye ar) savings as a percentage of retail sales for electricity energy efficiency (EE) programs. We considered two different perspectives: 1) historical data reflecting achieved savings of EE programs and 2) requirements of existing state energy efficiency

resource standards (EERS). For the historical perspective, we reviewed data from the Energy Information Administration’s Form EIA - 861 on EE program electricity savings (supplemented as needed with program administrator reports) and identified the pace at which entities reaching higher savings levels have historically increased energy

savings over time. 43 Specifically , we reviewed the historical savings data in the following two groups of energy efficiency program administrators. 1. Top saver 1% - a group with 47 entities that achieved a maximum first - ye ar savings level of 0.8% to 1.5%. 2. Top saver 2% - a group with 26 entities that achieved a maximum first - ye ar savings level of 1.5% to 3.0%. 44 For the existing state requirements perspective, we reviewed energy savings ramp - up schedules established under EERS for states that provide clear ramp - up schedules. According to ACEEE’s 2013 State Energy Efficiency

Scorecard, there are a total of 26 states that have mandatory

EERS policies (ACEEE 2013a) . Our analy sis contains 10 states for which clear ramp up schedules were identifiable. Our research findings on historical savings performance are:

 The “Top Saver 1%” group (savings between 0.8% and 1.5%) exhibits a trend that these entities took or would take about 3.4 years on average to increase first - ye ar

43 The EIA 861 was the main data source (EIA 2012a) . However, we have supplemented the EIA 861 with third -party program administrator data because the EIA 861 just started to collect third -party admini strator data in 2011. The third - party entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact . In addition, we supplemented the EIA 861 database with additional data for two utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These entities are Burlington Electric and Massachusetts Electric Company (now part of National Grid) . 44 In addition to these maximum first - year savings thresholds, we screened program adminis trators for the following conditions: (a) the maximum savings levels occurs after the minimum savings levels; (b) sufficient amounts of increase in first - year savings are provided to evaluate reasonable ramp - up schedules to gain an incremental 1% first - ye ar savings; and (c) savings data series are continuous between the years for the minimum and maximum savings levels.81 savings b y 1% (with a range of 1.6 years to 10 ye ars) (see Table 2 - 1 ). The entities in this group have increased the level of first - ye ar savings by 0.30% per year on average from their minimum to their maximum first - year savings levels (with a range of 0.10% per ye ar to 0.63% per year). 45  The “Top Saver 2%” group (savings between 1.5% and 3%) exhibits a trend that took or would take about 2.6 years on average to increase savings b y 1% (with a range of 0.8 years to 7.3 years) (see Table 2 - 1). The entities in this group have increased the level of first - ye ar savings by 0.38% per year on average from the minimum to the maximum first - ye ar savings levels (with a range of 0.14% per ye ar to 1.28% per ye ar). 46 **Table 2**  **- 1.**  **Energy savings ramp -**  **up trends in first -**  **year savings for “Top Saver 1%” and “Top Saver 2%” groups** 

**Top Saver 1% Top Saver 2%**  **Average Annual First**  **- Year**  **Savings Increase Estimated Years to** 

**Gain Incremental 1%**  **Average Annual**

**First -**  **Year Savings Increase**  **Estimated Years to Gain**  **Incremental 1% Average**  **0.30% 3.4**  **0.38% 2.6**  **Median 0.29%**  **3.4 0.34%**  **3.0** Max 0.63% 1.6 1.28% 0.8 Min 0.10% 10 0.14% 7.3 # of sample entities 47 26 Sources: EIA 2012 a and third - party program administrator data 47 Our research findings on incremental electricity savings ramp - up based on existing state EERS policies are:

 The states with EERS policies which exhibit savings ramp - up schedules are requiring increases in first - year energy savings at a pace that ranges from 0.11% (Colorado and Oregon) to 0.40% (Rhode Island) as shown in Table 2 - 2.

45 This is a simple average estimate of the annual average increase in first - year savings from each entity in this group. 46 This is the simpl e average estimate of the annual average increase in first - year savings from each entity in this group. 47 Data sources: The EIA 861 was the main data source (EIA 2012a) . However, we have supplemented the EIA 861 with third party program administrator data because the EIA 861 just started to collect third -party admini strator data in 2011. The third party entities included in our analysis are Efficiency Vermont, Energy Trust of Oregon, Efficiency Maine Trust, and Cape Light Compact . In addition, we supplement ed the EIA 861 database with additional data for two utilities that we found achieved high energy savings, but did not report savings data in the EIA 861 data for one or two years. These entities are Burlington Elect ric and Massachusetts Electric Company (now part of National Grid) .82

 The first - ye ar savings pace of increase averages 0.21% per ye ar across the 10 states. This savings level translates to about 4.7 years to achieve an incremental 1% first ye ar savings increase. **Table 2**  **- 2**  **. First -**  **Year Energy Savings Ramp -**  **up Review of State EERS Policies State**  **Minim um** **Targ et**  **Maximum Target Climb**  **Time (years)**  **Annual**

**Average % Increase**  **Years to**

**Achieve 1% Increase**  **Min Year**  **Max Year**  a b c d e=d - b f=(c - a)/e g=1/f **Arizona** 1.25% 2011 2.5% 2016 5 0.25% 4.0 **Arkansas** 0.25% 2011 0.9% 2015 4 0.16% 6.2 **Colorado** 0.80% 2011 1.7% 2019 8 0.11% 9.3 **Illinois** 0.20% 2008 2.0% 2015 7 0.26% 3.9 **Indiana** 0.30% 2010 2.0% 2019 9 0.19% 5.3 **Massachusetts** 1.4% 2010 2.6% 2015 5 0.24% 4.2 **Michigan** 0.3% 2009 1.0% 2012 3 0.23% 4.3 **Ohio** 0.3% 2009 1.2% 2019 10 0.17% 5.9 **Oregon** 0.8% 2010 1.0% 2013 3 0.07% 15.0 **Rhode Island** 1.7% 2011 2.5% 2013 2 0.40% 2.5 **Average** 0.21% 4.8 Sources: ACEEE 201 4c and APSC 201383 

### Appendix **3 :**  **Demand -**  **Side Energy Efficiency Plan Scenario: Data, Calculations and**  **Results** See attached files for comprehensive data, calculations, and results supporting the illustrative demand - side energy efficiency (EE) plan scenario. These files address the magnitude and timing of savings, and the assessment of costs of the demand - side energ y efficiency included in the illustrative plan scenario of the Regulatory Impacts Assessment (RIA). The two files reflect costs discounted at 3% and 7%, respectively:

 GHG Abatement Measures TSD

– Demand - Side EE @ 3%.xlsx  GHG Abatement Measures TSD

– Demand - Side EE @ 7%.xlsx84



### References ACEEE. 2000. Utility

Energy Efficiency

Programs: A Brief S ynopsis of Past and Present Efforts. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/U007.pdf . ACEEE. 2004. Five Years In: An Examination of the First Half - Decade of Public Benefits Energy Efficiency

Policies. American Council for an Energy- Efficient Economy . Available at http://aceee.org/research - report/u042 . ACEEE. 2008. State - Level Energy Efficiency

Analy sis: Goals, Methods, and L essons Learned. American Council for an Energy- Efficient Economy . Available at http://aceee.org/files/proceedings/2008/data/papers/8_468.pdf . ACEEE. 2009. Saving Energy Cost - Effectivel y: A National Review of the Cost of Energy Saved Throu gh Utility- Sector Energy Efficiency

Programs. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/U092.pdf . ACEEE. 2011. Energy

Efficiency

Resources Standards: A Progress Report on State Experience. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/u112.pdf . ACEEE. 2012a. A National Survey of State Policies and Practices for the Evaluation of Ratepayer - Funded Energy Efficiency

Programs. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/u122.pdf . ACEEE. 2012b. Three Decades and Counting: A Historical Review and Current Assessment of Electric Utility

Energy Efficiency

Activity in the States. American Council for an EnergyEfficient Econom y. Available at http://aceee.org/sites/default/files/publications/researchreports/u123.pdf .85

ACEEE. 2013. Leaders of the Pack: ACEEE’s Third National Review of Exemplary Energy Efficiency

Programs. American Council for an Energy- Efficient Econom y. Available at http://aceee.org/sites/default/files/publications/researchreports/u132.pdf . ACEEE. 2014a. Change Is in the Air: How States Can Harne ss Energy Efficiency

to Strengthen the Economy

and Reduce Pollution. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/f iles/publications/researchreports/e1401.pdf . ACEEE. 2014b. Cracking the TEAPOT: Technical, Economic, and Achievable Energy Efficiency

Potential Studies. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/u1407.pdf . ACEEE. 2014c. The 2014 State Energy Efficiency

Sco r ecard. American Council for an EnergyEfficient Econom y. Available at http://aceee.org/sites/default/files/publications/researchreports/u1408.pdf . ACEEE. 2014 d . The Best Value for America’s Energy Dollar: A National Review of the Cost of Ut ility Energy Efficiency Programs. American Council for an Energy - Efficient Economy . Available at http://aceee.org/sites/default/files/publications/researchreports/u 1402.pdf . ACEEE. 2015a. The Role of Building Energy Codes in the Clean Power Plan. American Council for an Energy- Efficient Economy . Available at http://aceee.org/sites/de fault/files/building- codes 111d - 1 - 22 - 15.pdf

. ACEEE . 2015b. State Energy Efficiency

Resource Standards (April 2015). American Council for an Energy- Efficient Economy . Available at http:/ /aceee.org/sites/default/files/eers 04072015.pdf

. Allcott, H., and M. Greenstone. 2012. I s There an Energy Efficiency

Gap? J Econ Perspect , 26 (1): 3

–28. APSC. 2013. Docket Nos. 13 - 002 - U, Order No. 7. Arkansas Public Service Commission. Available at http://www.apscservices.info/pdf/13/13 - 002 - u_72_1.pdf .86

Arimura, T.H., S. Li, R. Newell, and K. Palmer. 2012. Cost - Effectiveness of Electricity Energy Efficiency

Programs. Energy Journal , 3 3(2). ASAP. 2012. The Efficiency Boom: Cashing In on the Savings from Appliance Standards. Appliance Standards Awareness Project. Available at http://www.app liance standards.org/sites/default/files/The%20Efficiency%20Boom.pdf . ASAP. 2014. Energy

and Water Efficiency

Standards Adopted and Pending by State. Appliance Standard Awareness Project. Available at http://www.appliance standards.org/sites/default/files/State_status_grid_Feb_21_2014.pdf . Auffhammer , M., C. Blumstein, and M. Fowlie. 2008. Demand Side Management and Energy Efficiency

Revisited. Energy Journal , 29(3): 91

– 104. Barbose, G.L., C.A. Goldman , I. M. Ho ffman , M.A. Billingsley . 2013 . The Future of Utility Customer - Funded Energy Efficiency

Programs in the United States: Projected Spending and Savi ngs to 2025. Energy Efficiency , 6:475

–493 . BCAP. n.d. The History

of Energy

Codes in the United States. Building Codes Assistance Project. Available at http://energycodesocean.org/sites/default/files/resources/The%20History%20of%20Energy%20 Codes%20in%20the%20United%20States.pdf . Booz Allen . 2012. Energy Efficiency

in the Forward Capacity Market: Evaluating the Busine ss Case for Building Energy Efficiency

as a Resource for the Electric Grid. Booz Allen Hamilton. Available at http://aceee.org/files/proceedings/2012/data/papers/0193 - 000167.pdf . Bo yd, G.A., and J.X. Pang. 2000. Estimating the Linkage Between Energy Efficiency

and Productivity . Energy Policy , 28: 289

–296. BPA. n.d. BPA Study of Smart Grid Economics Identifies Attractive Opportunities and Key Uncertainties. Bonneville Power Administration. Available at http://www.bpa.gov/

Projects/Initiatives/SmartGrid/DocumentsSmartGrid/BPA - Smart- GridRegional-Business - Case - Summary - White -Paper.pdf .87

California GOPR. 2002. California Standard Practice Manual: Economic Analy sis of Demand Side Programs and Projects. California Governor’s Offic e of Planning and Research. Available at http://www.calmac.org/events/SPM_9_20_02.pdf . CEC. 2013. 2013 Integrated Energy Policy Report. California Energy Commission. Available at http://www.energy.ca.gov/2013publications/CEC - 100 - 2013 - 001/CEC

- 100 - 2013 - 001 - CMF.pdf . CEC. 2014. Notice of Pre - Rulemaking Schedule. California Energy Commi s sion. Available at http://www.energy.ca.gov/appliances/2013rulemaking/documents/pre reulemaking_schedule.pdf . CEE. 2015. 2014 State of the Efficiency Program Industry Report. Consortium for Energy Efficiency . Available at http://library.cee1.org/sites/default/files/library/12193/CEE_2014_Annual_Industry_Report.p df . Clarke, L., A. Fawcett, J. Weyant, J. McFarland, V. Chaturvedi, and Y. Zhou. 2014. Technology and U.S. Emissions Reductions Goals: Results of the EMF 24 Modeling Exercise. Energy Journal , 35 (SII ): 9

– 31. Available at https://web.stanford.edu/group/emfresearch/docs/emf24/EMF_24.pdf . Comstock, O., and E. Boedecker. 2011. Energy and Emissions in the Building Sector: A Comparison of Three Policies and Their Combinations. Ene rgy Journal , 32: 23

–41. Congress. 1975. Public Law 94 - 173 : Energy Policy and Conservation Act . U.S. Congress. Available at http://www.gpo.gov/fdsys/pkg/STATUTE- 89/pdf/STATUTE- 89 -Pg871.pdf . Congress. 1978 a . Public Law 95 - 619 : National Energy Conservation Policy Act . U.S. Congress. Available at http://www.gpo.gov/fdsys/pkg/STATUTE- 92/pdf/STATUTE- 92 -Pg3206.pdf . Congress. 1978b. Public Utility Regulatory

Policies Act. U.S. Congress. Available at http://www.usbr.gov/power/legislation/purpa.pdf . Congress . 1987. National Appliance Energy Conservation Act. U.S. Congress. Available at https://www.congress.gov/bill/100th - congress/house - bill/87 .88

Congress . 1992. Energy

Policy Act of 1992 . U.S. Congress. Available at http://www.usbr.gov/power/legislation/epa92.pdf . Congress . 2005. Energy

Policy Act of 2005. U.S. Congress. Available at http://energy.gov/sites/prod/files/2013/10/f3/epact_2005.pdf . Congress. 2007. Public Law 110 - 140: Energy Independence and Security Act of 2007. U.S. Congress. Available at http://www.gpo.gov/fdsys/pkg/PLAW - 110publ140/html/PLAW110publ140.htm . CRS. 2007. The Potential for Energy Savings Certificates as a Major Tool in Greenhouse Gas Reduction Programs. Center for Resource Solutions. Availa ble at http://www.resource solutions.org/pub_pdfs/Draft_Report_ESC_V12_cleanFINAL_5 - 24 - 07.pdf . Davis, L .W. 2008. Durable Goods and Residential Demand for Energy and Water: Evidence from a Field Trial. RAND J Econ , 39: 530

– 546. DeCanio, S. 1998. The Efficiency Paradox: Bureaucratic and Organizational Barriers to Profitable Energy- Saving Investments. Energy Policy , 26(5): 441

–458. DeCanio, S., and W.E. Watkins. 2008. Investment in Energy Efficiency : Do the Characteristics of Firms Matter? Rev Econ Stat , 80: 95

– 107. DOE. 2011. Energy

Efficiency

in Distribution Systems: Impact Anal ys is Approach. U.S. Department of Energy . Available at https://www.smartgrid.gov/sites/default/files/Distribution%20System%20Energy%20Efficiency% 2017Nov11.pdf

. DOE. 2012a. Application of Automated Controls for Vo ltage and Reactive Power Management

—Initial Results. U.S. Department of Energy . Available at https://www.smartgrid.gov/sites/default/files/doc/files/VVO%20 Report%20 - %20Final.pdf . DOE. 2012b. Arizona Energy and Cost Savings for New Single - and Multifamily Homes: 2009 and 2012 IECC as Compared to 2006 IECC. U.S. Department of Energy . Available at89

http://www.energycodes.gov/sites/default/files/documents/ArizonaResidentialCostEffectiveness.p df . DOE. 2012c. Department of Energy : Successes of the Recovery Act. U.S. Department of Energy . Available at http://www.energy.gov/sites/prod/files/RecoveryActSuccess_Jan2012final.pdf . DOE. 2014. Step 1. Understand the Benefits of Code Adoption. U.S. Department of Energy . Available at https://www.energycodes.gov/resource - center/ACE/adoption/step1 . Accessed July 28, 2015. DOE. 2015a. Saving Energy and Money

with Appliance and Equipment Standards in the United States. U.S. Department of Energy . Available at http:/ /energy.gov/sites/prod/files/2015/07/f24/Appliance%20and%20Equipment%20Standards% 20Fact%20Sheet%207- 21 - 15.pdf . DOE. 2015b. Rulemakings and Notices. U.S. Department of Energy . Available at http://energy.gov/eere/buildings/rulemakings - and - notices . Accessed July

28, 2015. EIA. 2012a. Electric Power Sales, Revenue, and Energy Efficiency

Form EIA - 861 Detailed Data Files. U.S. Energy

Information Administration. Available at http://www.eia.gov/electricity/data/eia861/ . EIA. 2012b. State Electricity Profiles 2010. U.S. Energy Information Administration. Available at h ttp://www.eia.gov/electricity/state/pdf/sep2010.pdf . EIA. 2013. Electric Power Sales, Revenue, and Energy Efficiency

Form EIA - 861 Detailed Data Files. U.S. Energy

Information Administration. Available at http://www.eia.gov/electricity/data/eia861/ . EIA. 2015. Annual Energy Outlook 2015: Table: Electricity Suppl y, Disposition, Prices, and Emissions. U.S. Energy

Information Administration. Available at http://www.eia.gov/forecasts/aeo/pdf/0383%282015%29.pdf .90

ENERGY STAR. 2012a. ENERGY STAR Products : 20 Years of Helping America Save Energy, Save Monday

and Protect the Environment. Avail able at http://www.energystar.gov/ia/products/downloads/ES_Anniv_Book_030712_508compliant_v2.p df . ENERGY STAR. 2012b. Celebrating 20 Years of ENERGY ST AR. Available at http://www.energystar.gov/ia/about/20_years/ES_20th_Anniv_brochure_spreads.pdf?ab0b 60a9 . EPA. 2006. Clean Energy and Environment Guid e to Action: State Policies and Best Practices for Advancing Energy

Efficiency , Renewable Energy , and Combined Heat and Power. U.S. Environmental Protection Agency . EPA. 2014a. Clean Power Plan Proposed Rule: Greenhouse Gas Abatement Measures Technical Sup port Document. U.S. Environmental Protection Agency. Available at http://www2.epa.gov/sites/production/files/2014 - 06/documents/20140602tsd -g hg- abatementmeasures.pdf

. EPA. 2014b. Clean Power Plan Proposed Rule: State Plan Considerations Technical Support Document. U.S. Environmental Protection Agency . Available at http://www2.epa.gov/sites/production/files/2014 - 06/documents/20140602tsd -state - plan considerations.pdf

. EPA. 2015. Energy

and Environment Guide to Action: State Policies and Best Practices for Advancing Energy

Efficie ncy , Renewable Energy , and Combined Heat and Power. U.S. Environmental Protection Agency . Available at http://epa.gov/statelocalclimate/documents/pdf/guide_action_full.pdf . EPA and DOE. 2006. National Action Plan for Energy Efficiency : A Plan Developed by More Than 50 Leading Organizations in Pursuit of Energy Savings and Environmental Benefits through Electric and Natural Gas Energy Efficiency . U.S. Environmental Protectio n Agency and U.S. Department of Energy . Available at http://www.epa.gov/cleanenergy/documents/suca/napee_report.pdf .91

EPA and DOE. 2007 a . Guide for Conducting Energy Efficiency

P otential Studies: A Resource of the National Action Plan for Energy Efficiency . U.S. Environmental Protection Agency and U.S. Department of Energy . Available at http://www.ep a.gov/cleanenergy/documents/suca/potential_guide.pdf . EPA and DOE. 2007 b . Guide to Resource Planning with Energy Efficiency: A Resource of the National Action Plan for Energy Efficiency . U.S. Environmental Protection Agency and U.S. Department of Energy . Available at http://www.epa.gov/cleanenergy/documents/suca/resource_planning.pdf . EPA and DOE. 2008. Understanding Cost - Effectiveness of Energy Efficiency

Programs: Best P ractices, Technical Methods, and Emerging Issues for Policy- Makers

— A Resource of the National Action Plan for Energy Efficiency . U.S. Environmental Protection Agency and U.S. Department of Energy . Available at http://www.epa.gov/cleanenergy/documents/suca/costeffectiveness.pdf

. EPA and DOE. 2009. Energy Efficiency

as a Low - Cost Resource for Achieving Carbon Emissions Reductions: A Resource of the National Action Plan for Energy Efficiency . U.S. Environmental Protection Agency and U.S. Department of Energy . Available at http://www.epa.gov/cleanenergy/documents/suca/ee_and_carbon.pdf . Fischer, C. 2005. On the Importance of the Supply Side in Demand - Side Management. Energ Econ , 27 : 165

– 180. GEEG . 2012. An Empirical Model for Predicting Electric Energy Efficiency

Resource Acquisition Costs in North America: Anal ys is and Application. Green Energy Economics, Inc . Available at http://aceee.org/files/proceedings/2012/data/papers/0193 - 000170.pdf . Gerarden, T., R.G. Newell, and R.N. Stavins. 2015. Deconstructing the Energy- Efficiency Gap: Conceptual Frameworks and Evidence. American Economic Review , 105(5): 183 - 86. Gillingham, K., and K. Palmer. 2014. Bridging the Energy Efficiency

Gap: Policy

Insights from Economic Theory

and Empirical Evidence. Rev Env Econ Policy , 8(1) : 18

– 38.92

Gillingham, K., M.J. Kotchen, D.S. Rapson, and G. Wagner. 2013. Energy Policy : The Rebound Effect is Overplayed. Nature , 493: 475

–476. Gillingham, K., R. Newell, and K. Palmer. 2006. Retrospective Examination of Demand - Side Energy Efficiency

Pol ic ies. Annual Review of Environment and Resources, Vol. 31: 161 - 192. Gillingham, K., R. Newell, and K. Palmer. 2009. Energy Efficiency

Economics and Policy . Annu Rev Resour Economics , 1: 597

– 619. Greening, L.A., D.L. Greene, and C. Difiglio. 2000. Energy Efficiency

and Consumption

—the Rebound Effect

—a Survey . Energy Policy , 28: 389

–401. HUD. 2014. Energy

Performance Contracting. U.S. Department of Housing and Urban Development. Available at http://portal.hud.gov/hudportal/HUD?src=/program_offices/public_indian_housing/programs/p h/phecc/eperformance . Accessed July 28, 201 5. IC F. 2007. Introduction to Energy Performance Contracting. IC F International. Available at http://www.energystar.gov/ia/partners/spp_res/Introducti on_to_Performance_Contracting.pdf . IE A. 2012a. Best Practices in Designing and Implementing Energy Efficiency

Obligation Schemes. International Energy Agency . Available at http://www.raponline.org/document/download/id/7235 . IE A. 2012b. World Energy Outlook 2012. International Energy Agency . Available at http://www.iea.org/publications/freepublicatio ns/publication/W EO2012_free.pdf

. IEEE. 2014. CVR as an Energy Efficiency

Resource. Institute of Electrical and Electronics Engineers. Available at http://sites.ieee.org/ isgt2014/files/2014/03/Day1_Panel1C_KW arner.pdf . IE I. 2014. State of Electric Regulatory Frameworks (December 2014). The Edison Foundation’s Institute for Electric Innovation . Available at http://www.edisonfoundation.net/iei/Documents/IEI_stateEEpolicyupdate_1214.pdf .93

IHS. 2013. Optimize Prime: The Market for Volt/VAR Optimization Set to Transform. IHS Technology . Available at https://technology.ihs.com/480591/optimize - prime - the - market-

forvoltvar- optimization -set- to - transform . Accessed July 28, 2015.

Ital y. 2014. Sixth National Communication under the UN Framework Convention on Climate Change. Available at http://unfccc.int/files/national_reports/annex_i_natcom/submitted _natcom/application/pdf/ita_nc 6_resubmission.pdf

. Jaffe, A.B., and R.N. Stavins. 1994. The Energy Paradox and the Diffusion of Conservation Technology . Resour Energy Econ , 16(2): 91

– 122. Jaffe, A.B., R.G. Newell, and R.N. Stavins. 2003. Chapter 11: Technological Change and the Environment. Handbook Environ Econ, 1: 461

– 516. Available at http://www.hks.harvard.edu/fs/rstavins/Papers/Technological_Change_and_the_Environment_H andbook_Chapter.pdf

. Joskow, P.L ., and D.B. Marron. 1992. What Does a N egawatt Reall y Cost? Evidence from Utility Conservation Programs. Energy Jou rnal , 13(4):41

– 74. Kriegler, E., J. P. Weyant, G. J. Blanford, V. Krey, L . Clarke, J. Edmonds, A. Fawcett, G. Luderer, K. Riahi, R. Richels, S.K. Rose, M. Tavoni, and D.P. van Vurren. 2014. The Role of Technology

for Achieving Climate Policy Objectives: Ov erview of the EMF 27 Study on Global Technology

and Climate Policy

Strategies. Climatic Change , 123: 353

– 367. Kyle, P., L. Clarke, S.J. Smith, S. Kim, M. Nathan, and M. Wise. 2011. The Value of Advanced End - Use Energy

Technologies in Meeting U.S. Climate Policy Goals. Energy Journal , 32: 61

– 87. LBNL. 1996. The Past, Present, and Future of U.S. Utility Demand - Side Management Programs. Lawrence Berkeley

National Laboratory . Available at http://emp. lbl.gov/sites/all/files/39931.pdf .94

LBNL. 2013a. Current Size and Remaining Market Potential of the U.S. Energy Service Company Industry . Lawrence Berkeley National Laboratory . Available at http://emp.lbl.gov/sites/all/files/lbnl- 6300e_0.pdf . LBNL. 2013b. Energy

Efficiency

Program Ty

pology and Data Metrics: Enabling Multi - State Analy ses Through the Use of Common Terminology . Lawrence Berkeley National Laboratory . Available at http://emp.lbl.gov/sites/all/files/lbnl- 6370e.pdf . LBNL. 2014a. Estimating Customer Electricity Savings from Projects Installed by the U.S. ESCO industry . L awrence Berkeley National Laboratory . Available at http://emp.lbl.gov/sites/all/files/lbnl- 6877e.pdf . LBNL. 2014 b . The Program Administrator Cost of Saved Energy for Utility

Customer - Funded Energy Efficiency

Programs. L awrence Berkeley Nat ional Laboratory . Available at http://emp.lbl.gov/sites/all/files/lbnl- 6595e.pdf . LBNL. 2015a. Technical Memorandum to Carla Frisch, U.S. Department of Energy

—Energy Efficiency

Portfolio and Program L ifetimes: Temporal Distribution of Electricity Savings. Lawrence Berkeley

National Laboratory . LBNL. 2015b. The Total Cost of Saving Electricity through Utility

Customer - Funded Energy Efficiency

Programs: Estimates at the National, State, Sector and Program Level. Lawrence Berkeley National Laboratory . Available at http://emp.lbl.gov/sites/all/files/total- cost- of -savedenergy.pdf

. Levine, M.D., A.H. Sanstad, E. Hirst , J.E. McMahon, and J.G. Koomey . 1995. Energy Efficiency

Policy and Market Failures. Annu Rev Energ Env , 20: 535

– 555 . Massachusetts EOEEA. 2010. Massachusetts Clean Energy and Climate Plan for 2020. Massachusetts Executive Office of Energy and Environmental Affairs. Available at http://www. mass.gov/eea/docs/eea/energy/2020 - clean - energy- plan.pdf .95

Massachusetts EOEEA. 2013. Commonwealth of Massachusetts Global Warming Solutions Act: 5 - Year Progress Report. Massachusetts Executive Office of Energy and Environmental Affairs. Available at http://www.mass.gov/eea/docs/eea/gwsa/ma -gwsa - 5yr- progress - report- 1 - 6- 14.pdf . McKinsey . 2007. Reducing U.S. Greenhouse Gas Emissions: How Much at What Cost? McKinsey & Company , Inc. Available at http://www.mckinsey.com/~/media/mckinsey/dotcom/client_serv ice/sustainability/pdfs/reducing %20us%20greenhouse%20gas%20emissions/us_ghg_final_report.ashx . McKinsey . 2010. U . S . S mart Grid V alue at S take: The $130 B illion Q uestion. McKinsey & Company . Available at http://www.mckinsey .com/~/media/McKinsey /dotcom/client_service/EPNG/PDFs/McK%20on %20smart%20grids/MoSG_130billionQuestion_VF.ashx . MJ Bradley . 2011. Benchmarking E lectric Utility Energy Efficiency

Portfolios in the U.S. M . J . Bradley & Associates, LLC. Available at http://www.ceres.org/resources/reports/benchmarkingelectric - u tilities - 2011 . NAESCO. 2011. US Energy Service Company

Industry : History

and Business Models. Presented at the Second U.S. - China Energy Efficiency

Forum. National Association of Energy Service Companies. Available at http://energy.gov/sites/prod/files/2013/11/f4/session_1_financing_track_gilligan_en_1.pdf . NARR. 2010. APX’s North American Renewables RegistryTM Infrastructure Extended. North American Renewables Registry . Available at http://www.narecs.com/2010/03/09/apxs - north american - renewables - registry - in frastructure - extended/

. Accessed July

28, 2015. NARUC. 2012. EL - 2/ERE - 3 Resolution Supporting the Rapid Deploy ment of Voltage Optimization Technologies. National Association of Regulatory Utility Commissioners. Available at http://www.naruc.org/Resolutions/Resolution%20Supporting%20the%20Rapid%20Deployment %20of%20Voltage%20Optimization%20Technologies.pdf .96

NPCC. 2008. Beyond Supply Curves. Northwest Power and Conservation Council. Available at http://aceee.org/files/proceedings/2008/data/papers/8_419.pdf . NPCC. 2010. Sixth Northwest Con servation and Electric Power Plan. Northwest Power and Conservation Council. Available at http://www.nwcouncil.org/media/6284/SixthPowerPlan.pdf . NRDC. 2012. Removing Disincentives to Utility Energy Efficiency

Efforts. Natural Resources Defense Council. Available at http://www.nrdc.org/energy/decoupling/files/decoupling - utilityenergy.pdf

. NRDC. 201 4. EEI -NRDC Joint Agreement to State Utility Regulators. Natural Resources Defense Council. Available at http://docs.nrdc.org/energy/files/ene_14021101a.pdf . NRECA. 2013. Costs and Benefit s of Conservation Voltage Reduction. National Rural Electric Cooperative Association. Available at https://www.smartgrid.gov/sites/default/files/ doc/files/NRECA_TPR2_Costs_Benefits_of_CVR_0 .pdf . NREL . 2008. Considerations for Emerging Markets for Energy Savings Certificates. National Renewable Energy

L aboratory . Available at http://www.nre l.gov/docs/fy09osti/44072.pdf . NSW . 2015. Energy

Savings Scheme . New South Wales. Available at http://www.ess.nsw.gov.au/Home . Accessed July 28, 2015. ORNL . 2015. Performance Contracting by State. Oak Ridge National Laboratory . Available at http://web.ornl.gov/info/esco/legislation/newesco.shtml . Accessed July 28, 2015. Pavan, M. 2008. Tradable Energy Efficiency

Certificates: the Italian Experience. Energy Efficiency, 1: 257

–266 . Available at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.434.1487& rep=rep1& type=pdf . PNNL . 2012. Evaluation of Representative Smart Grid Investment Grant Project Technologies: Summary Report. Pacific Northwest National Laboratory . Available at h ttp://www.pnnl.gov/main/publications/external/technical_reports/PNNL - 20792.pdf .97

RAP. 2011. Electricity

Regulation in the U.S.: A Guide. Regulatory Assistance Project. Available at www.raponline.org/docs/RAP_Lazar_ElectricityRegulationInTheUS_Guide_2011_03.pdf . RAP. 2012. Energy

Efficiency

Cost - Effectiveness Screening: How to Properly Account for ‘ Other Program Impacts ’ and Environmental Compliance Costs. Regulatory Assistance Project. Available at www.raponline.org/document/download/id/6149 . RFF . 2010. Imperfect Competition, Consumer Behavior, and the Provision of Fuel Efficiency in Li ght - Duty

Vehicles. Resources for the Future. Available at http://www.rff.org/RFF/Documents/RFF -DP- 10 - 60.pdf

. RFF. 2011. Supply

Curves for Conserved Electricity . Resources for the Future. Availabl e at http://www.rff.org/RFF/Documents/RFF -DP- 11 - 11.pdf

. Sanstad, A. H., and R. B. Howarth. 1994. ‘ Normal ’ Markets, Market Imperfections and Energy Efficiency . Energy Policy , 22(10): 811

– 818 . Sreedharan, P. 2013. Recent Estimates of Energy Efficiency

Potential in the USA. Energy Efficiency , 6(3): 433

–445. Sterling Planet. 2012. Sterling Planet Completes Nation’s First White Tags® Transaction. Available at https://www.sterlingplanet.com/ResourceCenter/NewsRoom/tabid/59/post/sterlingplanet- completes - n ation - s - first- white - tags - transaction/Default.aspx . Accessed July 28, 2015. Sy napse. 2008. The Sustainability and Costs of Increasing Efficiency Impacts: Evidence from Experience to Date. Sy

napse Energy

Economics, Inc . Available at http://www.aceee.org/files/proceedings/2008/data/papers/8_434.pdf . Sy napse. 2011. A Brief Survey of State Integrated Resource Planning Rules and Requirements. Sy napse Energy

Economics, Inc. Available at www.cleanskies.org/wp content/uploads/2011/05/ACSF_IRP- Survey_Final_2011 - 04 - 28.pdf .98

TCR. 2015. The Cool Planet Project. The Climate Registry . Avai lable at http://www.theclimateregistry.org/cool- planet- project/ . Accessed July

28, 2015. Train, K. 1994. Estimation of Net Savings from Energy- Conservation Programs. Energy , 19(4): 423

–441. UKERC. 2007. The Rebound Effect: An Assessment of the Evidence for Economy- Wide Energy Savings from Improved Energy Efficiency . UK Energy

Research Centre. Available at http://www.ukerc.ac.uk/publications/the - rebound - effect- an - assessment- of

- the - evidence -

foreconomy - wide - energy-savings - from - improved

- energy- efficiency.html . UNLV. 2014. Evaluation of Conservation Voltage Reduction as a Tool for Demand Side Management. University

of Nevada, Las Vegas. Available at http://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=3076 &c ontext=thesesdissertation s . Utility DIVE. 2015. 2015 State of the Electric Utility Survey Results: Here’s What the Utility of the F uture L ooks L ike, A ccording to O ver 400 U.S. E lectric Utility E xecutives. Utility DIVE Brand Studio. Available at https://s3.amazonaws.com/dive_assets/rlpsys/utilitydive_seu_2015.pdf . Wang, Y., and M.A. Brown. 2013. Policy Drivers for Improving Electricity End - Use Efficiency in the USA: An Economic - Engineering Anal ys is. Energy Efficiency, 7(3): 517

– 546. White House. 2007. E xe cutive O rder 13423 : Strengthening Federal Environmental, Energy , and Transportation Management. The White House . Available at http://www.gpo.gov/fdsys/pkg/FR 2007- 01 - 26/pdf/

07- 374.pdf

. White House. 2009. E xe cutive O rder 13514 : Federal Leadership in Environmental, Energy , and Economic Performance. The White House. Available at http://www .archives.gov/federalregister/executive - orders/disposition.html .99

White House. 2015. E xe cutive O rder 13693 : Planning for Federal Sustainability in the Next Decade. The White House. Available at http://www.archives.gov/federal- register/executive orders/disposition.html . Woolley , J., and G. Peters . n.d . Jimmy Carter: National Energy

Program Fact Sheet on the President’s Program (April 20, 1977) . Available at http://www.presidency.ucsb.edu/ws/?pid=7373 . Accessed July 28, 2015. Worrell, E., J.A. Laitner, M. Ruth, and H. Finman. 2003. Productivity Benefits of Industrial Energy Efficiency

Measures. Energy , 28(11): 1081

– 1098.