Analysis of Energy Efficiency Program Impacts Based on Program - EIA

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Analysis of Energy Efficiency Program Impacts Based on Program Spending

May 2015

Independent Statistics & Analysis www.eia.gov

U.S. Department of Energy Washington, DC 20585

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are independent of approval by any other officer or employee of the United States Government. The views in this report therefore should not be construed as representing those of the U.S. Department of Energy or other federal agencies.

U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending

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May 2015

Analysis of Energy Efficiency Program Impacts Based on Program Spending The growth of energy efficiency (EE) programs at utility and state levels affects energy consumption in sectors targeted by such programs. Program spending effects are not uniform in terms of timing, investment trends, affected end uses, customer types and context (specific historical, market, policy and other relevant factors). Improved understanding of the effects of EE programs can improve baseline energy demand projections and enable enriched policy and scenario analysis related to programs that encourage or mandate increased EE program activity. To gain insight into recent EE program activity and related implications for energy consumption, EIA contracted with Leidos Engineering, LLC (Leidos), previously known as Science Applications International Corporation (SAIC) to conduct research on regional differences in energy efficiency programs and spending. The contracted research report is included as Appendix A. The scope of the project was to characterize EE spending related to residential and commercial energy use for each of the nine Census divisions (see map)—the level of geographic detail used by the Residential Demand Module and Commercial Demand Module of the National Energy Modeling System (NEMS). Leidos research focused on rebate programs used by utilities as incentives for customers to purchase higher-efficiency products. The report submitted by Leidos characterizes EE program spending at the end-use level. This information will be used by EIA as an input to the development of modeling assumptions for projections related to incremental EE programs in NEMS. Specifically, these inputs will be used to develop program spending allocations (‘model portfolios’) at the regional level in the NEMS building modules for lighting, heating, air conditioning, water heating, ventilation, refrigeration, and other end uses. The results, including regional variation in spending and efficiency gains, will support EIA’s updates of the analytical and modeling assumptions in NEMS. In selecting representative utilities for analysis, Leidos sorted utilities into five program categories across all Census divisions, based on their 2012 reporting of EE programs and spending on EIA Form EIA-861, Annual Electric Power Industry Report, which collects information on the status of electric power industry participants involved in the generation, transmission, distribution, and sale of electric energy in the United States, its territories, and Puerto Rico. In addition to three categories based on program spending levels (high, medium, and low) there was a per customer category for residential programs, and a per megawatt-hour category for commercial programs. 1 Leidos examined EE program reports filed with state public utility commissions by selected utilities to characterize spending by end-use. In many instances programs can be divided into two groups based on their end-use emphasis: programs that are narrowly focused on lighting and heating, ventilation and air conditioning (HVAC) or broader programs that direct efficiency funds and services to a wider range of end-uses, technologies, and customers. The more investment a given state or provider makes in energy efficiency, the more likely they are to design and implement a broader program. In part this is because

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Two additional categories relate to the way data are reported by certain entities that provide electric service and/or efficiency program services. If an entity reported EE program spending but no electricity sales, Leidos put them in a “no sales” category; if they reported electricity sales but no program spending, they were put in a “no spend” category.

U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending

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May 2015 the initial end use areas, although cost-effective, become saturated and this requires a broader effort if further energy savings are to be achieved. In addition to supporting EIA’s own efforts to enhance NEMS, the information in the Leidos report, including regional variation in spending and efficiency gains, will complement other research that seeks to provide greater public information and analysis about EE program spending and efficiency outcomes. This is important given the growing use of energy efficiency programs, with close to 30 states already having adopted EE goals, pilots, or demand reduction targets. 2 EE programs are also a potential strategy available to states under EPA’s proposed Clean Power Plan rule under Section 111(d) of the Clean Air Act. Figure 1. Map of U.S. Census Regions and Divisions

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Source: U.S. Energy Information Administration 2

Analysis by U.S. Energy Information Administration using data in: American Council for an Energy Efficient Economy (ACEEE), State Energy Efficiency Resources Standards (EERS) (April 2015), accessed May 15, 2015; ACEEE, State and Local Policy Database, accessed May 15, 2015; Database of State Incentives for Renewables and Efficiency (DSIRE); and state public utility commission (PUC) websites. 3 The Leidos report in Appendix A also refers to the Census divisions by number in the following order: 1. New England, 2. Middle Atlantic, 3. East North Central, 4. West North Central, 5. South Atlantic, 6. East South Central, 7. West South Central, 8. Mountain, 9. Pacific.

U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending

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May 2015 When referencing the contract report in Appendix A, it should be cited as a report by Leidos Engineering, LLC prepared for the U.S. Energy Information Administration.

U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending

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May 2015

APPENDIX A

U.S. Energy Information Administration | Analysis of Energy Efficiency Program Impacts Based on Program Spending

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U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

Task Number DE-DT0007965 Subtask 13 Analysis of Energy Efficiency Program Impacts on Energy Consumption Based on Program Spending Final Report 3/6/2015 Submitted by Hilary Grimes-Casey, Leidos Engineering To the U.S. Energy Information Administration

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Table of Contents Page Table of Contents

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Introduction to Scope of Work

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Characterizing Impacts of Program Spending on End-Use Consumption Assumptions Approach

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Utility Sampling Methodology Approach Sample strategy limitations and clarifications Top Sales Threshold Definition Non-electric program coverage

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Efficiency Program Spending Data Collection Data fields Data availability

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Data Analysis and Results Qualitative Data Analysis End-use Consumption Allocations Utility EE Program End-Use Spending

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Conclusions

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References

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Appendices

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Figure 1. Map of U.S. Census Regions and Divisions .................................................................. 5 Figure 2. Comparison of Average Program Year Lighting Budgets by Sector and Census Division .................................................................................................................................... 16

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Table 1. Initial Utility Ranking Strategies ....................................................................... 8 Table 2. Populations of EIA-861 Reporting Utilities for 2012 .......................................... 8 Table 3. Collected EE Data............................................................................................ 10 Table 4. Total (Sample) Electric Spending Per Annualized Program Year, by End-Use ..... 14

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Introduction to Scope of Work This report summarizes an approach to quantifying aspects of state-sponsored energy efficiency (EE) programs and demand side management (DSM) spending in the United States, to assist in developing analytic assumptions and model inputs used by the U.S. Energy Information Administration (EIA) to project reductions in building energy consumption due to EE program activities. The work activity is aimed at continuous quality improvement in EIA’s modeling programs, to maintain relevancy and consistency with changing energy markets. The project scope includes collection and analysis of data to characterize program impacts on end-use energy consumption in the residential and commercial building sectors. The results of the project are to be utilized as inputs to the residential and commercial sector demand modules in EIA’s National Energy Modeling System (NEMS), to account for the regional impacts of EE initiatives on equipment characteristics and energy consumption in the US, at the level of Census divisions (CD), shown in Figure 1 (U.S. Energy Information Administration, 2014). Other regional and U.S. initiatives have attempted or are attempting to characterize energy efficiency program spending at various levels. Lawrence Berkeley National Laboratory is compiling program administrators’ savings and cost data from 31 states in 4 Census regions for their Cost of Saved Energy project (Billingsley, Hoffman, Stuart, Schiller, Goldman, & LaCommare, 2014). The Northeast Energy Efficiency Partnerships started collecting and reporting efficiency program spending and savings data from eight states and the District of Columbia in 2013 via the Regional Energy Efficiency Database (REED) (Northeast Energy Efficiency Partnerships, 2014) A 2011 report summarized electric energy efficiency spending and savings by utility for a sample of 50 utilities, representing two-thirds of total U.S. electric program funding as of 2009 (Jones, Hoffman-Andrews, Liberman, Reynolds, & Whitman, 2011). Leidos relied on similar data sources to derive spending estimates per program year at the regional U.S. level, given programs offered in the 2009-2013 timeframe, and covering both electric and non-electric energy initiatives where available. The report presents Leidos’ approach and methods for characterizing the impact of program spending on end-use energy consumption in the residential and commercial building sectors, and analysis of EE program spending data, by Census division.

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Figure 1. Map of U.S. Census Regions and Divisions

Source: U.S. EIA, Commercial Buildings Energy Consumption Survey (CBECS) website: http://www.eia.gov/consumption/commercial/census-maps.cfm

Characterizing Impacts of Program Spending on End-Use Consumption Assumptions The following assumptions guide the approach to estimating the impact of EE program proposed or actual spending on building end-use energy consumption: • The level of spending on energy efficiency programs in a Census division is primarily a function of utility revenue (sales) and ratepayer charges collected to fund these programs. • Spending on incentives and services that promote energy efficient equipment and activities in the buildings sector varies by region. • Utility budgets and expenditures for efficiency and demand side management programs can be allocated to specific building end-uses. Sources of funding: While many regulated electric and gas utilities have tariff mechanisms (such as system benefit charges) to collect funds from ratepayers to cover the costs of EE program implementation, these charges are not the only funding source used to support EE initiatives available to residents of a state or region. State budget allocations, American Recovery and Reinvestment Act

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(ARRA) funding, revolving loan fund initiatives, regional capacity market auctions, or fees collected from certain industries may supplement or wholly fund programs administered either by a utility or a third party. For example, the Vermont Energy Investment Corporation has funded EE programs by ratepayer fees for electric initiatives, and by Vermont’s Regional Greenhouse Gas Initiative revenues for fossil and other fuel consumption-based initiatives (Vermont Energy Investment Corporation, 2012), plus netted about 10% of their annual program spending in the ISO-New England capacity market in 2012 (Neme & Cowart, 2014). Regulated utilities are likely to separate their customer and outside sources of income in plans and reports, but publicly-available EE budget and expenditure data may omit other market influences. These can include co-funding paid by program partners, or federal, regional, local, or non-profit education and promotion initiatives that have no direct disbursement of funds to that reporting entity. Regional variability: EE budgets and expenditures on direct-to-customer subsidies for energy-saving projects and equipment are often set to cover a fixed portion of the customer’s cost, to ensure fair and cost-effective program delivery to the participants. Those project costs themselves vary based on geography, market availability, and competition among vendors and contractors, while utility rates and sales demographics variation also contribute to the impact of energy conservation programs in a region. Neighboring states’ EE activities can also have cross-influences on each other’s offerings and expenditures. Some utilities operate similar EE programs across Census division boundaries, such as National Grid in CD 1 and 2, and Tennessee Valley Authority in CD 5 and 6. Capturing regional variability (where regions are defined by Census divisions) was a main objective of this research, but regional variability was initially expected to be less apparent in adjacent Census divisions for these reasons, data availability notwithstanding. Program data availability in the form of utility reports or plans may be a factor of geography also, because states apply different program reporting and documentation rules. Lack of available data influences the apparent regional variability of program spending in this study. End-use allocations: Some regulated utilities plan and track spending at the level of end-uses or measures, to watch market penetration and be able to shift spending and program requirements according to market needs. For example, Sacramento Municipal Utility District reported expenditures and savings achieved at end-use level in an annual report on the California public sector programs (California Municipal Utilities Association, 2014). Annual reports required by the public commission don’t always require a measure level of detail, however, and utilities may not consider that data as public information. Evaluation, measurement and verification (EM&V) activities may include verifying claimed savings attributed to end-uses or to measure types. While savings allocations may not translate to equivalent spending allocations, savings allocations by end-use are more prevalent in utility reports and plans, and are used as proxies for budget or spending allocations for the purposes of this analysis. Utilities also vary in how they define their budgets and spending. Some differentiate funds for customer incentives vs. third party implementation vs. internal administration vs. marketing; others roll those various program activities into a single program budget. These differences are likely due to state reporting and funding allocation rules. If a utility presents a program budget with no distinction for how it is to be spent, this study assumes that the budget can be entirely allocated to building end-use energy savings via default allocation assumptions. However, it is noted that less than 100% of a utility program budget is spent to directly subsidize end-use equipment purchases or changes in end-use consumption. Approach The scope of this work is focused on characterizing EE program spending on building end-use consumption, for example estimating energy consumption in residential building lighting. This approach does not differentiate between different equipment technologies that can be employed to

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serve a given end-use, such as incandescent, compact fluorescent, or light-emitting diode (LED) bulbs and fixtures in residential lighting. Later work may seek to allocate regional EE program spending to specific technologies, but technology-specific allocations are not applied here. Leidos performed the following steps for characterizing regional EE program spending by end-use; details on the approach are provided later in this report: 1. Combined available utility sales data and reported EE spending data obtained from the 2012 EIA-861 1 to develop a basis for ranking electric utility spending by residential and commercial sector within Census Divisions. No equivalent EE program spending database for gas and fuel providers was found, although EIA publishes company sales data by state (U.S. Energy Information Administration, 2013). 2. Normalized and ranked utility EE spending and electric sales reported on the 2012 Form EIA861 for the commercial and residential sectors, in each CD. 3. Defined a sample of utilities to represent the typical or most prevalent EE programs in each CD and collect filed plans, commission reports, and/or EM&V reports that provide detail on proposed and actual EE spending and savings. The sample was further categorized by level of program spending per unit of sales. 4. Defined energy efficiency programs offered for the two sectors according to the collected documentation, and determined program spending on each program—for example, utility X offers Low Income, Retail Lighting, Home Performance with ENERGY STAR, ENERGY STAR Appliances for residential customers. Recorded rebate and total spending and savings that each of these programs offers, to make up 100% of the EE spending in the portfolio for that program cycle or program year. Where possible, programs exclusively supporting renewable generation or demand response were excluded as out of scope. 5. For each program defined in step 4, estimated the percent of program spending (incentive only and total) allocated to each end-use, where end-uses are defined as Lighting, Heating, Cooling, Water Heating, Appliances, and Other. This estimation method is discussed further in the data analysis section. 6. The utility’s program spend from step 4, multiplied by the end-use percent spend from step 5, is the end-use program spending for that utility in that program cycle or reporting period. 7. Determined end-use spending per year for each end-use, spending category, sector and Census division. The annualized spending on a reported utility program is calculated given the known program cycle, and then the program annual spending is summed for that Census division and spending category. Annualized spending was then normalized to 2014 dollars using the GDP Chain-Type Price Indices referenced in EIA’s Annual Energy Outlook for 2012 and 2014, Reference Case (U.S. Energy Information Administration, 2012 & 2014).

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http://www.eia.gov/electricity/data.cfm

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Utility Sampling Methodology Approach Leidos compared U.S. utilities’ 2012 data using Form EIA-861 2 for retail electricity sales and energy efficiency programs. Where data fields were filled with “.” rather than values, those entries were replaced with blank cells. Leidos ranked utilities according to the criteria listed in Table 1, in each of nine Census divisions defined in the original scope of work, and for each customer sector. Table 1. Initial Utility Ranking Strategies

Sector Commercial Residential

Sales Metric MWh sales MWh sales

Program Spending Metric Total program $ per MWh sold Total program $ per customer

A limited set of third party EE program administrators submit Form EIA-861 as required by EIA. The 2012 EE program data in Form EIA-861 were found to include respondents who reported program spending but no retail sales (resulting in infinite values for the program metrics), as well as respondents reporting electricity sales but no spending (resulting in 0 value program metrics). Some of the ‘no sales’ entities reporting program spending (such as Vermont Energy Investment Corporation, or the New York Energy Research and Development Authority (NYSERDA) are likely implementing programs on behalf of or supplementing co-located utility respondents (such as the Central Vermont Public Service Corporation and New York utilities). A summary of reported Form EIA-861 utility data from 2012 is shown below in Table 2. Table 2. Populations of EIA-861 Reporting Utilities for 2012

Census Division 1 2 3 4 5 6 7 8 9 Total

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Count of Utilities Reporting Sales (MWH sold) 36 34 152 199 128 27 63 77 73 789

Count of Utilities Reporting EE Incentives, Residential Sector 22 21 55 121 64 14 26 48 56 427

Count of Utilities Reporting EE Incentives, Commercial Sector 20 18 48 103 42 12 21 37 52 353

Form EIA-861, Annual Electric Power Industry Report, at http://www.eia.gov/electricity/data/eia861/

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Utilities were somewhat informally categorized within each sector and CD 3, into five ranked tiers by EE program spending levels for further analysis: having relatively high, medium, or low program spending metric values among the sorted utility population, as defined in Table 1; or being in a ‘no spend’ or ‘no sales’ category 4. Utilities contributing the top 50% 5 of electricity sales in each census division were compared to the utilities appearing in these categorized lists. Where those two lists converged, those utilities were selected for data collection. The resulting sample represented variations in normalized program spending within a region as well as across regions, and also ensured that the sampled programs coincided with the utilities contributing the majority of customer energy demand. The goal was to include at least two utilities in every program spending category, for each sector and CD. The final sample is provided in Appendix 1. Sample strategy limitations and clarifications Top Sales Threshold Definition The top retail sales threshold was initially limited up to 50% because beyond that threshold, adding an individual utility resulted in marginal added sales. However, some Census divisions lacked sufficient numbers of utilities reporting non-zero EE program spending, to assign two utilities to every spending category. In CD 5 and 7 through 9, the top retail sales threshold was extended up to 75% to expand the utility sample. In CD 4 and 6, the limited number of investor-owned utilities reporting EE program spending (and likely to be submitting regular program documentation to state commissions) prohibited the selection of two utilities for every category. However, inherent commonalities often arise in program offerings among regionally co-located utilities, and Leidos would contend that these commonalities may be adequately represented by a single utility per category. Non-electric program coverage Form EIA-861 does not collect sales or EE program spending data for natural gas or other building energy fuel providers. Some sampled electric utilities also administer or co-administer EE funding for natural gas or other fuel conservation initiatives. Where those non-electric programs are noted in comprehensive planning, reporting, or evaluation documents found for the defined utility sample, Leidos included that information in the analysis. Several of these sampled utilities appear in the American Gas Association’s 2012 Top 50 residential and commercial gas market lists 6, as noted in

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No quantitative, common threshold was set for these rankings, because what constituted ‘high’ EE spending per unit sales in one Census division could be nearly an order of magnitude different from the highest spending per unit sales utility in another Census division.

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‘No spend’ refers to utilities reporting zero program spending or not appearing in the Form EIA-861 EE program database; ‘No sales’ refers to utilities reporting zero retail sales or not appearing in the Form EIA-861retail sales database.

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As described further below, this threshold was extended for some Census divisions to 75% to create a larger sample.

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See revenue and volume rankings provided at http://www.aga.org/utility-rankings

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Appendix A1.

Efficiency Program Spending Data Collection Data fields The types of data to be collected were established based on typical contents of EE program documentation available, and on data needed to complete the task. The collected data are defined in Table 3. Table 3. Collected EE Data

Field Name Census Region

Description Numeric Census division

Participation Units

Normalized Program Spending Ranking: High, Medium, Low, No Sales, No Spend Name or names of the utility sampled How the utility refers to a program element Within the defined Residential or Commercial sector, segments of that target customer sector may be grouped together for budget or goal purposes, such as Single Family Residential vs. Multi-Family Residential. However, some reported programs do not differentiate by customer sector. Type of PROPOSED document (e.g. by the utility to the commission) in which data were found. These are often comprised of filed plans, Commission Orders, or sections of Integrated Resource Plans. Year 1 of the proposed program plan for which data were found Number of years duration of the program Proposed plan energy savings Options for recorded unit of savings Estimated quantity of participating entities or rebates Options for recorded or implied units of expected participation, such as measures or customers

Budgeted Direct to Customer

Plan Dollars budgeted for incentives or value to the customer over the plan program year(s)

Ranking Lookup Utility Program (Name)

Subsector Lookup Proposal Document Type Lookup Plan Start Year Plan Years Covered LOOKUP Expected Savings Savings Unit Lookup Expected Participation

Budget Includes Lookup

Plan Dollars budgeted for operating the programs, outside incentives, over the plan program year(s) Total Plan Dollars over the plan program year(s) Options for types of budget items included--somewhat obsolete with addition of budget items above

Actuals Document Type Lookup Achieved Start Year Achieved Yrs. Covered LOOKUP Achieved Savings Achieved Savings Unit Lookup

Type of document recording achievements of a program, such as an evaluation report or a report to the commission Year 1 of the program for which data were found Duration of the program reported Actual recorded energy savings (may be gross, net, or undefined) Options for unit of savings reported

Budgeted, Admin or Indirect Budgeted Program Total

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Field Name Achieved Participation Participation Units

Description Achieved quantity of measures, rebates, or customers in the programs Options for recorded or implied units of actual participation, such as measures or customers

Actual Expenditure Direct to Customer

Program dollars reported as spent on incentives or value to the customer over the reported program year(s)

Actual Expenditure, Admin or Program dollars reported as spent on operating programs other than Indirect incentives over the reported program year(s) Actual Expenditure Program Total Total program dollars reported over the program year(s) Program Offerings Lookup Primary End-uses Targeted Lookup Fuel Lookup

% of Sector Budget/Spend

End-use %

Non-Savings Spend %

Types of services and/or incentive mechanisms proposed or reported for a program Types of building end-uses proposed or reported for a program Types of energy resources proposed to be saved or saved—electricity and/or gas, or other fuels Of a proposed or reported budget for a given program, this item presents how much of that budget was spent on this sector and program, usually 100% if budgets are disaggregated by program/line item. It is only less than 100% if a more aggregated (portfolio) budget is recorded from the documentation, but specific program spending can be otherwise estimated. Fields to record or estimate the percent of proposed or actual spending allocated to typical building end-uses. Lighting, Heating, Air Conditioning, Water Heating, Ventilation, Refrigeration, and Other enduses were defined. This field was used to record proposed or actual spending line items that were attributed to portfolio wide administrative activities with no particular program distributions in the documentation. This field should generally have a value of 100% if its corresponding budget item is designated as non-savings; or blank if the recorded budget item is allocated to end uses.

Data availability Most sampled utilities provide EE program plans, evaluation reports, annual progress reports, and other documentation that are fully disclosed to the public via commission websites. However, some utilities redact budget and savings information provided in those documents. Kansas City Power & Light and Ameren Missouri in the West North Central CD and Duke Energy in the East South Central CD redact budget or cost information to some extent. Utilities which voluntarily offer EE programs less frequently provide formal documentation of their programs. The availability of both proposed and actual spending data for a given program in a given program cycle was less prevalent than expected. Many state commissions require regulated utilities to conduct evaluation of their programs and report on results regularly, but not annually, and not for every program. New York State utilities were found to have irregular and infrequent program evaluation requirements. Publicly-available, annually-reported program achievements were provided on the New

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York Public Service Commission website at a highly aggregated level for utilities. How utilities report budgets varies by utility and state. They may or may not differentiate direct-tocustomer spending for incentives and services, and they may differ in what they consider direct-tocustomer spending; some utilities may call technical assistance provided by an implementation contractor a customer benefit, while others may call it an administrative or implementation cost, separate from the customer incentive. In addition, utilities may designate program-specific budgets as well as line items for portfolio-wide activities such as marketing or activities of utility staff, whose allocation to programs is not obvious. Proposed or achieved participation levels are not consistently reported in plans or annual reports. Where they are reported, the participant definition is not always clear. Some prescriptive programs count individual units of equipment as participation, while more comprehensive incentive programs may count customers or projects. Infrequently, a utility serving electric and natural gas customers may quantify their proposed or actual energy savings in equivalent energy units and/or combine program budgets, without differentiating fuel source. Descriptions of program offerings in plans and evaluation reports are limited—often the plan is written to maximize flexibility in delivering a given program, especially those that cover multiple measures; while evaluation may focus on the programs and measures that bring the majority of savings. More frequently, utility documents report on claimed savings allocations to end-use rather than budget allocations to end-use. These factors contributed to limited data availability for some Census divisions, which may make comparisons among types of budget and spending data collected, or among Census divisions, difficult.

Data Analysis and Results Qualitative Data Analysis A number of assumptions were required to attribute reported EE program spending and savings data to typical program offerings and end-uses, where those attributions were not always explicit in the documents. For example, a utility may describe a budget item as an ENERGY STAR Products program, which may be limited to certified lighting products, or may encompass a variety of certified equipment. Utility websites or evaluation reports often provide the necessary program descriptions. Where those descriptions were not found or not clear, Leidos used professional judgment to determine likely program offerings that could then be assumed to have a certain budget profile by end-use. End-use Consumption Allocations Due to the greater prevalence of planned or reported allocations of energy savings to end-use in EE program documentation, Leidos applied those where available as a proxy for how program spending can be attributed to end-use. Where no such data could be obtained, Leidos applied its experience designing and implementing EE programs for utility clients throughout the U.S. Default allocations were determined for various typical program types, and reviewed by various Leidos Program Managers and senior program staff. A list of typical EE program types and their assumed budget allocations by end-use are provided in Appendix 2. The assignment of default (or reported) budget allocations to utility programs then allowed the calculation of end-use spending per program year, by Census division, sector, energy resource type, and

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spending category (high, medium, low, no spend, no sales). In general, the calculations were performed as follows, where i refers to the individual recorded program offerings, and j refers to the typical building end-uses defined for this analysis. 𝐸𝐸𝐸𝐸𝐸𝐸_𝑈𝑈𝑈𝑈𝑈𝑈_$𝑖𝑖𝑖𝑖 =

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_$𝑖𝑖 ∗ 𝑒𝑒𝑒𝑒𝑒𝑒_𝑢𝑢𝑢𝑢𝑒𝑒%𝑗𝑗 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃_𝑌𝑌𝑌𝑌𝑌𝑌

𝐸𝐸𝐸𝐸𝐸𝐸_𝑢𝑢𝑢𝑢𝑢𝑢_$𝑗𝑗 = ��𝐸𝐸𝐸𝐸𝐸𝐸_𝑈𝑈𝑈𝑈𝑈𝑈_$𝑖𝑖𝑖𝑖 �

Utility EE Program End-Use Spending

𝑖𝑖

Average end-use spending was initially explored as the appropriate metric. However, a number of recorded programs had insufficient spending or savings data to derive real weighted average end-use spending estimates. In addition, program spending for which the utility explicitly claimed zero proposed or actual savings is not correctly accounted for in this approach. It was decided to report sums of enduse spending rather than weighted averages. Because of the stated data availability issues, the most complete set of data for comparison by Census division exists for total EE program budgets (total including incentives and administrative costs, vs. incentives only). A summary of these total program budgets estimated by end-use for CD 1-9 is shown in Table 4, suggesting some differentiation by region in annual program spending. High estimated annualized budgets in CD 2 for residential heating end-use are attributed to multi-million dollar budgets set by NYSERDA for Empower NY, a weatherization program with both electric and gas budgets for 2012-2015; and by the New Jersey statewide gas program for 2012. CD 1, 6 and 9 all show high nonsavings, sector-neutral budget estimates: these are attributed to conservation and load management loan defaults by Connecticut Light & Power that were not explicitly designated as residential or commercial in their filed plan; administrative and educational budgets for Kentucky Utilities and Louisville Gas &Electric; and several sector-neutral budget items among California and Washington utilities, respectively. These data represent budgets provided for any and all reported and recorded 7 program types found in the program documents available for the sampled utilities. Some utility plans or reports include both efficiency offerings and demand response offerings, others separate kWh-saving programs from initiatives that are designed to shift peak loads, or to promote renewable generation. Leidos excluded out of scope load-shifting and renewable programs and budgets where they were documented as completely separate and distinct from an energy conservation portfolio. Some of these budgets are being spent on programs and initiatives that are expected to indirectly influence energy efficiency and building equipment selection, rather than to directly motivate purchase of more efficient equipment

7

In three cases, due to quantity of a utility’s program offerings, Leidos limited the sampled programs to those contributing the majority of overall savings and/or having the most complete data available: Municipal Energy Agency of Nebraska; Tennessee Valley Authority; and San Diego Gas &Electric.

13 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

and building features. Spending data were not normalized to savings or program participants, because the program documentation lacked sufficient data for a consistent comparison among spending categories and Census divisions. Table 4. Total (Sample) Electric Spending Per Annualized Program Year, by End-Use

Lighting Sector & Heating A/C Water Heating Ventilation Refrigeration Other End Use Non Savings Census Division 2014 $/ Prog Yr 2014 $/Prog Yr 2014 $/ Prog Yr 2014 $/Prog Yr 2014 $/Prog Yr 2014 $/Prog Yr 2014 $/Prog Yr 2014 $/Prog Yr Commercial 1,024,316,303 179,304,528 211,212,065 44,141,322 29,838,339 22,016,984 69,864,831 49,351,642 1 250,544,824 44,756,577 53,813,898 9,966,995 7,146,395 4,721,070 6,619,023 4,726,038 2 150,153,511 25,719,853 34,053,308 5,536,261 4,756,737 2,788,818 21,855,278 0 3 131,729,152 47,174,617 22,191,397 14,346,647 6,028,287 3,206,258 12,560,182 19,977,581 4 64,086,403 13,787,258 12,066,727 3,950,863 1,999,586 1,132,442 14,383,437 3,815,275 5 60,323,207 4,576,931 11,264,807 817,849 1,566,224 1,462,013 2,534,051 4,581,258 6 6,892,980 818,588 3,924,753 92,406 175,649 176,222 843,745 63,069 7 12,429,699 743,279 1,783,870 148,656 297,312 297,312 445,967 0 8 14,929,620 1,194,057 5,401,857 276,847 348,619 361,436 535,745 8,143 9 333,226,908 40,533,370 66,711,449 9,004,797 7,519,531 7,871,414 10,087,402 16,180,277 Residential 735,657,414 421,989,413 235,673,672 88,588,250 444,227 34,594,226 56,760,626 46,399,150 1 121,398,057 83,500,981 31,629,943 11,680,778 187,854 5,159,239 9,121,412 6,919,768 2 156,556,722 187,037,661 49,621,508 34,506,378 68,731 5,258,277 1,986,448 1,801,372 3 63,413,512 40,183,499 21,310,316 9,063,494 45,850 4,295,497 10,785,795 8,837,832 4 9,514,543 10,834,862 5,680,162 4,935,994 7,197 323,022 723,456 968,000 5 33,853,354 7,420,728 14,951,481 1,764,965 9,609 2,551,620 5,561,440 14,981,601 6 13,485,117 5,036,985 11,355,514 501,713 0 2,077,970 115,906 0 7 20,904,101 3,660,718 8,902,494 956,637 108,288 221,100 275,244 0 8 26,313,287 7,170,966 33,053,883 2,698,156 0 4,635,677 1,666,489 7,185,812 9 290,218,721 77,143,014 59,168,370 22,480,134 16,696 10,071,824 26,524,436 5,704,764 Sector Neutral 178,278,740 13,524,671 36,354,771 3,151,396 3,421,637 3,888,058 6,158,047 262,476,586 1 0 0 0 0 0 0 0 167,052 2 2,115,149 1,003,338 294,193 226,768 1,444 15,720 16,442 0 0 5,471,347 0 0 588,567 1,294,848 0 3 0 4 738,677 317,605 1,674,537 101,810 0 5,091 7,636 0 6 0 0 0 0 0 0 0 141,309,470 7 0 0 695,056 0 0 0 0 1,284,263 8 0 0 5,141,157 0 0 0 0 407,173 9 175,424,915 12,203,728 23,078,480 2,822,818 3,420,193 3,278,680 4,839,122 119,308,627 Grand Total 1,938,252,457 614,818,612 483,240,508 135,880,968 33,704,203 60,499,268 132,783,505 358,227,377

Figure 2 illustrates the variation in annualized customer incentive budgets among the defined utility spending categories as well as among Census divisions. Incentive budgets and spending attributed to lighting are shown. In general, utilities characterized as “low” EE program spenders per unit of sales appear to budget and spend less on customer incentives than utilities characterized as “high”. A number of utilities categorized as “no spend” utilities – reporting retail electricity sales on EIA-861, but not appearing in the accompanying EE program spending data set – were found to offer programs via review of their websites or state commission documents. The large lighting budget in the CD 1 “no spend” sample category is driven by multi-million dollar budgets and spending by NSTAR, a Massachusetts utility. A number of “no spend” utilities described programs on websites but no documentation could be found to support budget estimates.

14 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

15 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

Figure 2. Comparison of Average Program Year Lighting Budgets by Sector and Census Division

16 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

Conclusions Anecdotal evidence and past research and analysis suggest that regional differences in spending on specific programs and the types of building end-uses served in those programs are likely to exist, if only because efficient equipment cost differences are known to exist geographically, and many EE programs are designed to cover a fixed percent of equipment costs. The results of this project suggest that, without accounting for program maturity, one region of the U.S. may be subject to different utility spending than another for a given building end-use offering. In addition, within a region, there is variation in sales-normalized spending among utilities. Further development of this research is hoped to reveal more definitive regional variations in EE spending across the U.S., of interest for inclusion in EIA’s National Energy Modeling System.

References Billingsley, M. A., Hoffman, I. M., Stuart, E., Schiller, S. R., Goldman, C. A., & LaCommare, K. (2014, March). The Program Administrator Cost of Saved Energy for Utility Customer-Funded Energy Efficiency Programs. Berkeley, CA. Retrieved March 6, 2015, from http://emp.lbl.gov/sites/all/files/lbnl-6595e.pdf California Municipal Utilities Association. (2014). Energy Efficiency in California's Public Power Sector, A 2014 Status Report. Jones, B., Hoffman-Andrews, L., Liberman, I., Reynolds, L., & Whitman, A. (2011). Benchmarking Electric Utility Energy Efficiency Portfolios in the U.S. M. J. Bradley and Associates, Concord MA. Neme, C., & Cowart, R. (2014). Energy Efficiency Participation in Electricity Capacity Markets-The US Experience. Montpelie, VT: The Regulatory Assistance Project. Northeast Energy Efficiency Partnerships. (2014, August). Regional Energy Efficiency Database: Program Year 2012 Annual Report. U.S. Energy Information Administration. (2012 & 2014). Macroeconomic Indicators, Reference Case. Retrieved from http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2012&subject=5AEO2012&table=18-AEO2012®ion=0-0&cases=ref2012-d020112c U.S. Energy Information Administration. (2013, November). Natural Gas Annual Respondent Query System (EIA-176 Data through 2012). Retrieved from Energy Information Administration website: http://www.eia.gov/cfapps/ngqs/ngqs.cfm?f_report=RP4&f_sortby=&f_items=&f_year_s tart=&f_year_end=&f_show_compid=&f_fullscreen= U.S. Energy Information Administration. (2014). Retrieved October 21, 2014, from Energy Information Administration Office of Energy Analysis: http://www.eia.gov/forecasts/aeo/pdf/f1.pdf U.S. Energy Information Administration. (2014). Commercial Buildings Energy Consumption Survey (CBECS). Retrieved from U.S. Energy Information Administration Independent Statistics and Analysis: http://www.eia.gov/consumption/commercial/census-maps.cfm Vermont Energy Investment Corporation. (2012, November 1). Efficiency Vermont Annual Plan 2014. Retrieved from Efficiency Vermont web site: www.efficiencyvermont.com Appendices

A1. Full Sample of Utilities by Census Division

17 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

U.S. ENVIRONMENTAL INFORMATION ADMINISTRATION TASK ORDER # DE-DT0007965 SUBTASK 13

A2. EE Program Types and Assumed End-use Allocations

18 Use or disclosure of data contained on this sheet is subject to the restriction on the title page of this proposal.

UTILITY SAMPLE SELECTIONS 1. ranked utilities by MWh sales (residential vs. commercial)--selected those contributing top 50% of sales (additional top sales are incremental and result in an unmanageably long list of utilities) 2. ranked utilities by EE spend per sales unit (per customer for res, per MWh for comm) 3. joined two lists and selected utilities that met the following 5 category descriptions RESIDENTIAL Utilities in top 50% sales and distributed by program spend per customer

CD1

CD2

CD3

High Spend per customer

(MA) Massachusetts Electric Co; (RI) The Narragansett Electric Co

(PA) PECO Energy Co**; (PA) PPL Electric Duke Energy Indiana; (IL) Ameren Illinois Company Utilities Corp;

Med Spend per customer

(NJ) Public Service Elec & Gas Co ; (NY) (CT) Connecticut Light & Power Co; (NH) Consolidated Edison Co-NY Inc**; (NJ) Public Service Co of NH Jersey Central Power & Lt Co

CDD4

CD5

(IA) Interstate Power and Light Co

(FL) Florida Power & Light Co; (MD) Baltimore Gas & Electric Co**

CD6

CD7

CD8

CD9

(KY) Kentucky Utilities Co; (KY) DukeEnergy Kentucky

City of San Antonio - (TX); (OK) Public Service Co of Oklahoma

(AZ) Salt River Project; (AZ) Arizona Public Service Co

(WA) Puget Sound Energy Inc**; (CA) Sacramento Municipal Utility District

(CO) Public Service Co of Colorado**; (NV) Nevada Power Co

(CA) Pacific Gas & Electric Co**; (CA) Southern California Edison Co

3

Low Spend per customer

No sales (third party)

No spend

(ME) Central Maine Power Co (ME) Efficiency Maine Trust; (VT)Vermont Energy Investment Corporation; (MA) Cape Light Compact

(MA) NSTAR Electric Company

(IL) Commonwealth Edison Co; (MI) Consumers Energy Co** (KS) Westar Energy Inc

(NC) Duke Energy Carolinas, LLC; (MS) Mississippi Power Co; (GA) Georgia Power Co (KY) Louisville Gas & Electric Co

(AR) Entergy Arkansas Inc; (TX) Entergy Texas Inc.

(NY) Niagara Mohawk Power Corp.**; (NY) New York State Elec & Gas Corp

(OH) The Toledo Edison Co, a FirstEnergy company

(KS) Kansas City Power & Light Co

(AL) Alabama Power Co; (VA) Virginia Electric & Power Co (KY) Kentucky Power Co

(TX) Southwestern Public Service Co

NYSERDA

WI Focus On Energy

none selected

Tennessee Valley Authority--GA, Tennessee Valley Authority-VA KY,TN, AL

none selected

none selected

(IA) Municipal Energy Agency of NE; (MN) Great River Energy none selected

CD3

CD4

CD5

(MN) Northern States Power Co*; (IA) MidAmerican Energy Co*

(MD) Baltimore Gas & Electric Co; (MD) Potomac Electric Power Co (KY) Kentucky Power Co

none selected

(NV) Sierra Pacific Power Co; (NM) Public Service Co of NM

none selected

(CA) San Diego Gas & Electric Co**; (WA) Avista Corp

Energy Trust of Oregon

none selected

(TX) TXU Energy Retail Co LP; (TX) Constellation NewEnergy, Inc none selected

none selected

CD6

CD7

CD8

CD9

City of San Antonio - (TX); (OK) Public Service Co of Oklahoma

(AZ) Arizona Public Service Co; (AZ) Salt River Project

(CA) Pacific Gas & Electric Co*; (CA) San Diego Gas & Electric Co*

(CA) Southern California Edison Co; (WA) Puget Sound Energy Inc*

COMMERCIAL Utilities in top 50% sales and distributed by program spend per MWh sold

High Spend per MWh sold

CD2 (NY) Long Island Power Authority; (NY) New York Power Authority; (MA) Massachusetts Electric Co; (RI) The (PA) PPL Electric Utilities Corp; (NY) Consolidated Edison Co-NY Inc Narragansett Electric Co

Med Spend per MWh sold

(CT) Connecticut Light & Power Co; (CT) United Illuminating Co

Low Spend per MWh sold No sales (third party)

No spend

CD1

(ME) Central Maine Power Co; (NH) Public Service Co of NH (ME) Efficiency Maine Trust; (MA) Cape Light Compact (MA) Direct Energy Business1; (MA) Constellation NewEnergy, Inc2; (MA) NSTAR Electric Company; (CT) Constellation NewEnergy, Inc

(PA) Metropolitan Edison Co; (PA) Pennsylvania Electric Co; (PA) West Penn Power Company (NY) Niagara Mohawk Power Corp.* ; (NJ) Public Service Elec & Gas Co; NY State Elec & Gas Corp

(MI) Consumers Energy Co*; (OH) Cleveland Electric Illuminating Co

(IL) Commonwealth Edison Co; (MI) (MO) Kansas City Power & Light Co; The DTE Electric Company (IA) Interstate Power and Light Co*

(FL) Duke Energy Florida, Inc; (FL) Florida Power & Light Co; (KY) Louisville Gas & Electric Co

(TX) Austin Energy

(UT) PacifiCorp; (NV) Nevada Power Co

(IN) Duke Energy Indiana Inc; (IN) Northern Indiana Pub Serv Co*

(MO) Union Electric Co; (SD) Black Hills Power

(GA) Georgia Power Co; (KY) Kentucky Utilities Co; (VA) Virginia Electric & Power Co (KY) Duke Energy Kentucky

(OK) Oklahoma Gas & Electric Co

(NM) Public Service Co of NM; (WY) Pacificorp

(CA) Los Angeles Department of Water & Power; (CA) Pacificorp

NYSERDA

WI Focus On Energy

none selected

TVA (GA, NC)

TVA (KY, MS,Al,TN)

none selected

none selected

(OR) Energy Trust of Oregon

PA Constellation NewEnergy; 4 PA FirstEnergy Solutions ; (NJ) Jersey Central Power & Lt Co

(IL) Constellation NewEnergy, Inc; (WI) Wisconsin Electric Power Co

(IA) Municipal Energy Agency of NE; (MN) Great River Energy none selected

(AL) Alabama Power Co: (MS) Mississippi Power Co

(TX) TXU Energy Retail Co LP; (TX) Constellation NewEnergy, Inc none selected

Footnotes 1. Direct Energy Business does not appear to be a utility; it may be a reporting convention. 2. Program documentation for Constellation NewEnergy in various states could not be found. 3. NJ Statewide programs were reported. 4. FirstEnergy Solutions likely appeared in this category because programs are delivered by individual operating companies that comprise FirstEnergy. Those companies are represented in other categories. * A 2012 Top 50 commercial sector gas seller, according to the American Gas Association ** A 2012 Top 50 residential sector gas seller, according to the American Gas Association

none selected

Fuel

Sector

End Use Allocation Program Types

Commercial

EL_CI_Comprehensive Prescrip/Custom EL_CI_Cooling and Heating EL_CI_Heating and Water Heating EL_CI_New Construction (WB+Prescrip)

EL_CI_Unknown Program Type EL_CI_Whole Building Retrofit EL_Res_Appliance

EL_Res_Comprehensive Prescriptive EL_Res_Cooling and Heating EL_Res_Heating and Water Heating Residential

ELECTRIC RESOURCE CONSERVATION

EL_CI_Not End Use Spending

EL_Res_New Construction (WB+Prescrip)

EL_Res_Not End Use Spending

EL_Res_Sealing and Insulation/Audits EL_Res_Unknown Program Type EL_Res_Whole Building Retrofit

NONEL_CI_Heating and Water Heating NONEL_CI_New Construction (WB+Prescrip)

NONEL_CI_Not End Use Spending NONEL_CI_Unknown Program Type NONEL_CI_Whole Building Retrofit NONEL_Res_Appliance

NONEL_Res_Comprehensive Prescriptive NONEL_Res_Cooling and Heating NONEL_Res_Heating and Water Heating Residential

NATURAL GAS OR FUEL RESOURCE CONSERVATION

Commercial

NONEL_CI_Comprehensive Prescrip/Custom NONEL_CI_Cooling and Heating

NONEL_Res_New Construction (WB+Prescrip)

NONEL_Res_Not End Use Spending

NONEL_Res_Sealing and Insulation/Audits NONEL_Res_Unknown Program Type NONEL_Res_Whole Building Retrofit

Program Description Includes per unit equipment rebates and performance-based incentives that cover a range of end uses Limited to HVAC equipment and service

Lights Heat

75% 0

5% 30%

A/C

12% 70%

Space and water heating equipment and service 0 85% 0 Incentives and technical assistance for new building design 50.0% 10.0% 30.0% Indicates budget items that are attributed to utility or contractor marketing, R&D, or administrative work not specific to programs n/a n/a n/a the efficiency strategy intended by the program is unclear 75% 5% 12% incentives and technical assistance for major renovations 70.0% 15.0% 10.0% incentives covering new equipment or equipment recycling 0 0 20% Includes per unit equipment rebates and performance-based incentives that cover a range of end uses 75.0% 10.0% 10.0% Limited to HVAC equipment and service 0 25% 75% Space and water heating equipment and service 0 85% 0 Incentives and technical assistance for new building design 70.0% 15.0% 10.0% Indicates budget items that are attributed to utility or contractor marketing, R&D, or administrative work not specific to programs n/a n/a n/a Home Performance with ENERGY STAR often promotes air leak sealing and audit of energy savings opportunities 0 75% 25% the efficiency strategy intended by the program is unclear 70.0% 15.0% 10.0% incentives and technical assistance for major renovations 70.0% 15.0% 10.0% Includes per unit equipment rebates and performance-based incentives that cover a range of end uses 0 70% 0 Limited to HVAC equipment and service 0 100% 0 Space and water heating equipment and service Incentives and technical assistance for new building design Indicates budget items that are attributed to utility or contractor marketing, R&D, or administrative work not specific to programs the efficiency strategy intended by the program is unclear incentives and technical assistance for major renovations incentives covering new equipment or equipment recycling Includes per unit equipment rebates and performance-based incentives that cover a range of end uses Limited to HVAC equipment and service Space and water heating equipment and service Incentives and technical assistance for new building design Indicates budget items that are attributed to utility or contractor marketing, R&D, or administrative work not specific to programs Home Performance with ENERGY STAR often promotes air leak sealing and audit of energy savings opportunities the efficiency strategy intended by the program is unclear incentives and technical assistance for major renovations

Water Heating Ventilation

Refrigeration

Other

1% 0

2% 0

2% 0

3% 0

15%

0

0

0

5.0%

0.5%

0.5%

n/a

n/a

n/a

n/a

1%

2%

2%

3%

4.0%

0.0%

0.5%

0.5%

0

0

25%

55%

4.0% 0

0.0% 0

0.5% 0

0.5% 0

15%

0

0

0

4.0%

0.0%

0.5%

0.5%

n/a

n/a

n/a

n/a

0

0

0

0

4.0%

0.0%

0.5%

0.5%

4.0%

0.0%

0.5%

0.5%

23% 0

5% 0

0 0

2% 0

0

85%

0

15%

0

0

0

0

70%

0

23%

5%

0

2%

n/a

n/a

n/a

n/a

n/a

n/a

n/a

0

70%

0

23%

5%

0

2%

0

70%

0

23%

5%

0

2%

0

0

0

99%

0

0

1%

0 0

75% 100%

0 0

24% 0%

0 0

0 0

1% 0

0

85%

0

15%

0

0

0

0

75%

0

24%

0

0

1%

n/a

n/a

n/a

n/a

n/a

n/a

n/a

0

100%

0

0

0

0

0

0

75%

0

24%

0

0

1%

0

75%

0

24%

0

0

1%

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Analysis of Energy Efficiency Program Impacts Based on Program - EIA

Analysis of Energy Efficiency Program Impacts Based on Program Spending May 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of E...

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