gJ&ESIST
Energy Savings and
Impacts Scenario Tool
Energy Savings and
Impacts Scenario Tool
(ESIST)
Version 1.2 User
Manual
EPA 430-B-24-002
oEPA
United States
Environmental Protection
Agency
December 10, 2024
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Acknowledgments
ESIST was developed by Synapse Energy Economics, Inc., under contract with the U.S.
Environmental Protection Agency (EPA)'s State and Local Climate and Energy Program and under
the direction of Cassandra Kubes of EPA's Office of Atmospheric Protection's Climate Protection
Partnerships Division.
EPA thanks the staff at Synapse who developed ESIST and the user manual, including Patrick
Knight and other support staff. Eastern Research Group, Inc. (ERG) provided production and
logistical support with thanks to Charlie Goff and other support staff.
EPA thanks the following individuals for assessing the technical aspects of ESIST during a formal
technical peer review of the model: Michael Gartman, Rocky Mountain Institute; H. Scott Mathews,
Carnegie Mellon University; James E. McMahon, Energy and Resources Group, University of
California Berkeley; Steven Nadel, American Council for an Energy-Efficient Economy; and Heidi
Peltier, Political Economy Research Institute, University of Massachusetts, Amherst. The
information and views expressed in ESIST and the user manual do not necessarily represent those
of the peer reviewers, who also bear no responsibility for any remaining errors or omissions. In
addition, EPA thanks Marti Frank, Efficiency for Everyone, staff members at the U.S. Department of
Energy, Lawrence Berkeley National Laboratory, National Renewable Energy Laboratory, and our
EPA colleagues in the Office of Air Quality Planning and Standards, Clean Air Markets Division,
and Climate Protection Partnerships Division for their extensive assistance, input, and review
during the development of ESIST.
Version Changes
This user manual describes version 1.2 of the ESIST model and the ESIST: Pilot Gas Version.
Changes in version 1.2 include:
• New data reflecting the most recently available information on historical sales, utility
revenue, energy efficiency, and electricity demand projections.
• Updates to historical emissions rates.
• Updates to energy burden analysis data.
• Updates to demographics data.
• Adjustments to the analysis period allowing for future scenarios from 2023-2045 (the
previous version of ESIST analyzed 2022-2040).
• Other minor bug fixes and changes.
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Contents
ACKNOWLEDGMENTS Ill
VERSION CHANGES Ill
ABBREVIATIONS VI
1. INTRODUCTION 1
About the Energy Savings and Impacts Scenario Tool and This Manual 2
Potential Uses and Users 3
Considerations for Applying ESIST 4
Default Values 4
Alternate Data Sources 4
Key Notes and Alerts 5
Limitations 5
2. ESIST OVERVIEW 8
View User Manual 11
Summary of Costs and Benefits in ESIST 11
Comparing Scenarios 12
3. ESIST STEP-BY STEP INSTRUCTIONS 13
Step 1: Set Study Area 13
Step 2: Set Baseline Electricity Sales 14
ESIST Approach 15
Future Baseline Sales Growth 16
Embedded Savings 20
Step 3: Set Target Type 21
Annual Budget 21
Annual Incremental Savings 22
Cumulative Savings 22
Steps 4 and 5: Annual Budget 23
Step 4: Set Program Cost Assumptions 23
Step 5: Set Budget Assumptions 26
Steps 4 and 5: Annual Incremental Savings 26
Step 4: Set Savings Trajectory 27
Step 5: Set Program Cost Assumptions 28
Step 4 and 5: Cumulative Savings 28
Step 4: Set Savings Trajectory 28
Step 5: Set Program Cost Assumptions 29
Step 6: Set Multiple Benefits and Other Settings 30
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Emissions Impacts 30
Public Health Impacts 35
Energy Burden Impacts 38
Other Functionality 41
Demographic Data 43
Peak Demand Impacts 45
Customer Information 47
Savings Expiration 49
Step 7: Review Outputs 52
Reviewing Outputs 52
Charts 53
APPENDIX A: DATA SOURCES 56
APPENDIX B: ESIST USE CASES 58
Illustrative Use Case 1: Establish and Support Energy Efficiency Targets 58
Illustrative Use Case 2: Establish or Review the Role of Energy Efficiency in Pollution
Reduction Plans 59
Illustrative Use Case 3: Establish or Review Energy Efficiency Plans 60
APPENDIX C: ESIST: PILOT GAS VERSION 61
Default Data Sources 61
Functionality Limited or Excluded in ESIST: Pilot Gas Version 1.2 63
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Abbreviations
AAGR annual average growth rate
ACEEE American Council for an Energy-Efficient Economy
ACS American Community Survey
AEO Annual Energy Outlook
AVERT AVoided Emissions and GeneRation Tool
CAGR compound annual growth rate
CO2 carbon dioxide
COBRA Co-Benefits Risk Assessment tool
CRF capital recovery factor
DSM demand-side management
EE energy efficiency
EERS energy efficiency resource standard
ESIST Energy Savings and Impacts Scenario Tool
eGRID Emissions & Generation Resource Integrated Database
EIA Energy Information Administration
EMM Electricity Market Module
EPA U.S. Environmental Protection Agency
GHG greenhouse gas
GW gigawatt
HVAC heating, ventilation, and air-conditioning
IRP integrated resource plan
ISO independent system operator
kWh kilowatt-hour
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LBNL Lawrence Berkeley National Laboratory or the Berkeley Lab
MW Megawatt
MWh megawatt-hour
NGO nongovernmental organization
NOx nitrogen oxides
NYISO New York Independent System Operator
PM2.5 fine particulate matter
PUC public utility commission
SEO state energy office
SO2 sulfur dioxide
T&D transmission and distribution
VII
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1. Introduction
Energy efficiency, or EE, programs and policies are a proven and cost-effective strategy for helping
to meet customer electricity demand and reducing generation that would otherwise occur at electric
generating units. In the United States, all 50 states currently administer some type of energy
efficiency program, while many states have mandated a certain level of savings through energy
efficiency resource standard (EERS) policies.1 Some states also have requirements for electricity
suppliers to implement cost-effective energy efficiency. State and local governments also
implement energy efficiency policies such as building energy stretch codes, building performance
standards, building electrification, financing programs, energy efficiency plans targeting low-income
and multifamily residents, and energy efficiency data sharing and transparency.
States and local governments often seek analytical tools to help inform their efforts to adopt new
energy efficiency policies and programs, or to maintain or expand their existing activities. These
activities help jurisdictions stay current with market trends, meet regulatory or legislative
requirements, and ensure that customer-funded energy efficiency programs are cost-effective and
meet customer needs.2 Timely, reliable, and readily available information about the range of
impacts of proposed energy efficiency programs and policies can help states, communities, and
their stakeholders make informed choices. These impacts may include quantified electricity
savings, spending requirements, emissions and health impacts, and equity impacts specific to low-
income customers.
The benefits from state, local, and utility investments in energy efficiency are diverse. They include
customer savings on electricity bills, as well as electricity system benefits such as stabilizing or
reducing electric loads, improving electric supply reliability, and deferring or avoiding investment in
costly new infrastructure. Other drivers include benefits such as reductions in criteria air pollutants
and greenhouse gases (GHGs), improvements to public health, support for economic development,
progress toward energy equity, and an increase in jobs. Quantitative estimates of these multiple
benefits can support planning and investment decisions consistent with the jurisdiction's energy
efficiency policy and program goals. Such estimates can also help demonstrate the value of energy
efficiency to a broad spectrum of officials and stakeholders responsible for supporting a reliable
electricity system, strong economy, and healthy environment.3
See: ACEEE. "State Energy Efficiency Resource Standards (EERS)." (2019). See
https://www.aceee.org/sites/default/files/state-eers-0519.pdf for more information.
Throughout this document, "customer-funded energy efficiency" is used as to describe the types of energy
efficiency programs that are administered by utilities or third-party organizations and that stakeholders are most
likely to model in the Energy Savings and Impacts Scenario Tool. While these programs may be funded by a
combination of electric rate charges, grants, and other sources of revenue, they typically exclude energy efficiency
savings that result from mandated codes and standards.
For more information on quantitative estimates of these impacts, see EPA's 2018 edition of Quantifying the
Multiple Benefits of Energy Efficiency and Renewable Energy: A Guide for State and Local Governments,
available at https://www.epa.aov/statelocalenerav/guantifvina-multiple-benefits-enerav-efficiencv-and-renewable-
enerav-guide-state.
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About the Energy Savings and Impacts Scenario Tool and This
Manual
The Energy Savings and Impacts Scenario Tool (ESIST) Version 1.2 is a customizable and
transparent Excel-based planning tool for analyzing the energy savings and costs from customer-
funded energy efficiency programs and their impacts on emissions, public health, and equity.
ESIST enables users to develop, explore, and share energy efficiency scenarios between 2010
and 2045. ESIST users first select a study area based on states, utility types, specific utilities, or
difference customer sectors within a utility.4 Users can adjust inputs, including electricity sales
growth forecasts, energy efficiency savings goals, program budgets, savings expiration schedules,
discount rates, and first-year costs.5 The tool allows users to compare levels of energy efficiency
savings, annual costs, and levelized costs of saved energy. ESIST users can then estimate
multiple benefits that could result from the energy efficiency scenario—including avoided
emissions, public health benefits, peak demand impacts, and energy burden reductions—and
review customer demographic data.
ESIST combines several publicly available and peer-reviewed data sets to support analyses that
would otherwise be significantly more time-consuming and resource-intensive. It also provides
users with the flexibility to rely on default values or to customize input assumptions, allowing
policymakers, practitioners, and others to use ESIST as a comprehensive tool for generating
scenarios and informing decision-making. ESIST can be used by itself or in tandem with other tools
and analyses for further exploration of results.
To streamline and simplify analysis for users, ESIST integrates and aggregates publicly available
datapoints on the electricity sector from the U.S. Energy Information Administration (EIA).6 These
data include information on historical utility-level electricity sales, energy efficiency savings, number
of customers served, and revenue, among other values.7 Users may apply ESIST to aggregate
these data at different geographical resolutions, ranging from national- or state-level analysis to
different customer sectors within a specific utility service territory.
These historical data are then used to estimate future trajectories of electric customer-funded
energy efficiency savings and associated costs using pre-loaded electricity demand projections
from ElA's 2023 Annual Energy Outlook (AEO).8 Historical and projected trajectories of savings are
then combined with data from other sources, including the Lawrence Berkeley National Laboratory
ESIST can model the United States as a whole, each of the 50 states, Washington, D.C., and Puerto Rico. Other
territories are not currently available in ESIST.
Electricity sales growth forecasts should be inclusive of effects from electrification measures.
The primary source of savings and sales data is ElA's Form 861, available at
https://www.eia.gov/electricitv/data/eia861/. with the most recent data for 2022 downloaded in May 2024. Savings
data reported in EIA is in the form of "adjusted gross savings," which account for realization rates and in-service
rates, but not free ridership or spillover. For more information on these terms, see: Northeast Energy Efficiency
Partnerships (NEEP). "Glossary of Terms Version 2.1(July 2011). Available at
https://neep.org/sites/default/files/resources/EMV Glossary Version 2.1.pdf.
The primary focus of ESIST is on electricity use and efficiency savings in the electric power sector. For more
information on modeling energy efficiency impacts for direct fuel consumption, see Appendix C: ESIST: Pilot Gas
Version.
AEO is an annual projection published by EIA, containing information on electricity sales, energy prices,
emissions, and many other metrics through 2050. For more information, see https://www.eia.gov/outlooks/aeo/.
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(the Berkeley Lab or LBNL) and the U.S. Environmental Protection Agency (EPA), to calculate
additional energy efficiency impacts.
This manual is intended to help users understand and use ESIST. It introduces the stepwise input
process—either by applying defaults or entering user-specified values—for generating results
within ESIST. The manual also describes how users can identify and generate user-specified
values and contextualize ESIST outputs.
If you have questions or need assistance using ESIST, please contact us at esist@epa.gov.
Potential Uses and Users
ESIST is intended to inform a wide range of potential uses. For example, ESIST can:
• Estimate the historical or future (2010-2045) electricity impacts of customer-funded energy
efficiency programs and assess their contribution toward achieving existing, expanded, or
new energy efficiency targets.
• Inform utility benefit-cost analyses by modeling benefits and costs in alternate energy
efficiency scenarios.
• Generate different efficiency scenarios to facilitate stakeholder comparison and discussion
of future costs and benefits, such as during integrated resource plan (IRP) processes.
• Aggregate the results from analyses across multiple utilities or program administrators to
illustrate the combined effects of energy efficiency programs in a single state, or
nationwide.
• Assess the multiple benefits of electric customer-funded energy efficiency in terms of
emissions, public health, peak demand, or energy burden impacts.
• Evaluate the impacts of energy efficiency and monetary assistance on low-income
household energy burdens and calculate the total annual program costs of reducing energy
burdens for low-income customers.
• Review a set of demographic data describing the number of households in a utility service
territory based on household income, including ownership status, race and ethnicity, and
other criteria.
• Use electricity savings impacts from ESIST as an input to emission quantification tools,
such as EPA's AVoided Emissions and GeneRation Tool (AVERT).9 While ESIST provides
high-level information on long-term annual avoided emissions resulting from an energy
efficiency program across an entire region, AVERT provides more granular data (both
spatially and temporally) associated with savings from an energy efficiency program.
• Use electricity savings impacts from ESIST as an input to public health quantification tools,
such as EPA's Co-Benefits Risk Assessment (COBRA) tool.10 While ESIST provides high-
level information on long-term monetized public health benefits across the entire country
9 More information on AVERT is available at https://www.epa.aov/statelocalenerav/avoided-emissions-and-
generation-tool-avert.
10 More information on COBRA is available at https://www.epa.gov/cobra.
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resulting from an energy efficiency program, COBRA provides more granular data (both
spatially and categorically) associated with emission reductions resulting from an energy
efficiency program.
Some potential users of ESIST include:
• State agency officials examining the policy implications of changes to energy efficiency
investments.
• Public utility commission staff evaluating the costs and benefits of proposed efficiency
programs.
• Analysts and academics exploring different resource planning scenarios with varying levels
of energy efficiency investment.
• Nongovernmental organizations (NGOs) assessing the energy burden impacts of efficiency
programs targeted to reach low-income households.
• Air quality planners and public health officials seeking to quantify the emissions and public
health benefits of efficiency programs.
• Utilities reviewing demographic data of households in their utility service territory to reach
with energy efficiency programs.
See Appendix B for more information on potential use cases for ESIST.
Considerations for Applying ESIST
ESIST can support flexible and streamlined analysis of energy efficiency impacts, but users should
take care to consider its limitations and determine whether pre-loaded default values or user-
specified inputs are most appropriate for local circumstances and conditions. This section
highlights some key issues to consider when using ESIST and provides guidelines for identifying
solutions.
Default Values
Wherever possible, ESIST provides default values and explanatory text for a broad range of inputs
and assumptions. These include, but are not limited to, sales projections, projected costs, avoided
emissions rates, and public health impact factors. These default values allow users who do not
have access to jurisdiction-specific or other information sources to generate results quickly and
easily for energy efficiency costs, energy savings, and other key impacts. This simplifies use of
ESIST and generates reasonable values that are specific to the geography of interest. When
applying default values, users should take care to understand, assess, and appropriately adjust
these default inputs, assumptions, and calculations.11
Alternate Data Sources
Where locally preferred data sets exist, default values can be overwritten. Users may apply
electricity demand futures from other sources, such as a local utility, independent system operator
(ISO), state energy office (SEO), or public utility commission (PUC). ESIST allows users to choose
Refer to the "Library" tab in ESIST Excel workbook for more detail on default input assumptions used in ESIST.
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one of three methods to project energy savings, thereby providing users with flexibility for
jurisdictions that may structure energy efficiency goals in different ways (e.g., annual incremental
savings as a percent of sales, cumulative savings, energy efficiency budget levels). Users can
input alternate values (e.g., costs of implementing energy efficiency, avoided emission rates)
throughout most steps in ESIST.
Key Notes and Alerts
Throughout this manual, users should also observe "Key Notes" that highlight topics of special
interest or that deserve additional explanation. These notes include:
Reasonable projections for energy efficiency savings.
Development of state-specific sales projections.
Different cost perspectives (i.e., first-year versus levelized costs).
Different program and measure portfolios (i.e., the types of measures or technology that
the program seeks to encourage).
Lifetime of the analysis (i.e., implications of short-term versus long-term results).
Adjusting emissions rates and using ESIST data in AVERT.
Assumptions related to low-income weatherization savings.
Considerations for reducing energy burden.
Retail versus wholesale savings.
ESIST also includes several "Alerts" to help users select parameters that are realistic or achievable
in practice. For example, an alert is provided in cases where a level of annual incremental savings
is specified that approaches or exceeds the maximum level regarded as achievable for most
jurisdictions.
Limitations
ESIST is intended as a planning tool that balances complexity and relative ease of use. Users
should be aware of its limitations and appropriate uses and describe these when discussing
scenarios and communicating results with stakeholders. One important caveat is that ESIST
provides information on costs and benefits for some but not all categories that are commonly
assessed in benefit-cost testing of energy efficiency resources. For example, ESIST does not
estimate values of avoided energy generation or capacity costs.
Jurisdictions vary in the types of costs and benefits that are included in their benefit-cost tests.
While ESIST may be used to estimate some costs and benefits, users who are composing a
comprehensive benefit-cost analysis of energy efficiency programs should consult with literature,
such as the National Standard Practice Manual, for information on best practices for developing the
benefits and costs of categories not calculated by ESIST.12 Table 1 depicts the lists of benefits and
12 NESP. National Standard Practice Manual for Benefit-Cost Analysis of Distributed Energy Resources. (August
2020). Available at https://www.nationaleneravscreeninaproiect.orq/wp-content/uploads/2020/08/NSPM-
DERs 08-24-2020.pdf.
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costs that are described in this manual. Categories that can be estimated with ESIST are marked
with an V" and the rest are left blank. This table also describes the scope of these impacts, as
they are calculated in ESIST. Note that ESIST only monetizes public health benefits; all other
captured benefits in ESIST are quantified in some other way.
Demographics and customer data in ESIST is compiled using the best-available matching of
utilities to counties. This data (based on information provided in EIA Form 861) identifies which
utilities operate in each county, but does not identify how many customers, sales, or households
within each county are allocated to each utility. This means that users conducting ESIST analyses
that use this data should be careful to identify situations where double-counting may occur (for
example, situations where users are modeling multiple scenarios, each focused on a single utility
that has a service territory adjacent to the other utilities being modeled). Future versions of ESIST
may utilize other data sources that minimize or eliminate these double-counting situations.
Additional detail on ESIST's limitations—including data availability and sources, data applicability,
time scale, geography, and level of detail—are discussed where applicable in the sections that
follow. This manual also includes citations for all data sources and assumptions, considerations for
applying default values, and suggested sources of alternate local data.
Table 1. Categories of costs and benefits described in the National Standard Practice Manual
Benefit and „ Estimated „
Category . Scope of ES ST estimate
cost type 6 y in ESIST? K
Societal
Resilience
GHG emissions
Regional changes in GHG emissions
resulting from energy efficiency
programs implemented in states or
utility territories
Other environmental
Regional changes in criteria pollutant
emissions resulting from energy
efficiency programs implemented in
states or utility territories
Economic and jobs
Public health
National public health benefits are
calculated based on regional emission
changes
Low-income: society
Energy burden impacts help users
estimate the level of investment in low-
income programs needed to overcome
energy burden disparities
Energy security
Host
Customer
Host portion of DER costs
ESIST calculates participant costs of
energy efficiency
Interconnection fees
Risk
Reliability
Resilience
Tax incentives
Host customer non-energy
impacts
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Benefit and Estimated frc.cT 4.- *
Category . Scope of ES ST estimate
cost type 6 1 in ESIST? K
Low-income non-energy
impacts
Generation
Energy generation
Capacity
Environmental compliance
RPS/CES compliance
Market price effects
Ancillary services
Transmission
Transmission capacity
Transmission system losses
Users can enter a transmission and
distribution (T&D) loss value (in
percent) to estimate the losses avoided
by energy efficiency
Distribution
Distribution capacity
Distribution system losses
Users can enter a T&D loss value (in
percent) to estimate the losses avoided
by energy efficiency
Distribution operations and
maintenance
Distribution voltage
General
Financial incentives
ESIST calculates utility costs of
implementing energy efficiency,
including financial incentives
Program administration costs
ESIST calculates utility costs of
implementing energy efficiency,
including administration costs
Utility performance incentives
ESIST calculates utility costs of
implementing energy efficiency,
including performance incentives
Credit and collection costs
Risk
Reliability
Resilience
Source: For more information on categories or definitions of terms used, see the National Standard Practice Manual,
available at httos://www.nationaleneravscreeninaoroiect.ora/national-standard-oractice-manual/.
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2. ESIST Overview
Upon opening ESIST, users see a "Welcome" screen that summarizes ESIST (see Figure 1).
Users will encounter the following types of interactive buttons in ESIST:
• Red and buttons guide the user through the tool; most users
will wish to rely on these buttons to follow ESIST's suggested series of steps.
• Buttons with green text on a green field that allow the user to advance
through the tool to subsequent steps.
Buttons with gray text on a gray
future steps
field indicate that the user has not
yet reached; clicking red "Next" buttons allows the user to go to these steps.
Buttons with yellow text on a yellow field may be clicked to
reset the current
to its
default settings; users may wish to click these buttons on datasheets where they have
previously entered user-specific information or otherwise altered the default value or
formula.
• ESIST features cells with that indicate users can make changes to these cells without
harming blue text on a blue field the tool's functionality.
In general, all other cells should not be edited or altered in order to preserve ESIST's functionality.
Figure 1, ESIST "Welcome" screen Get Started
Resist
Energy Savings and
Impacts Scenario Tool
Energy Savings and Impacts Scenario Tool
(ESIST)
Vers/on 1.2, Updated December 2024
ESIST is a customizable and transparent Excel-based planning tool for analyzing the energy savings and costs from customer-
funded energy efficiency programs and their impacts on emissions, public health, and equity. ESIST enables users to develop,
explore, and share energy efficiency scenarios between 2010 and 2045. ESIST users first select a study area based on states,
utility types, specific utilities, or different customer sectors within a utility. Users can adjust inputs, including electricity sales
growth forecasts, energy efficiency savings goals, program budgets, savings expiration schedules, discount rates, and first-year
costs. The tool allows users to compare levels of energy efficiency savings, annual costs, and levelized costs of saved energy.
ESIST users can then estimate multiple benefits that could result from the energy efficiency scenario—including avoided
emissions, public health benefits, peak demand impacts, and energy burden reductions—and review customer demographic
data.
We strongly recommend that new users:
• Review the User Manual prior to using ESIST.
• Use the red "Next" and "Back" arrows to progress through the tool.
Users who wish to analyze different scenarios (e.g., different levels of savings in several states, or the same level of investment in
different utility service territories) should save multiple copies of this file, each containing a different scenario. These users may
find it helpful to click the "Restore default Excel functionality" button (lower-right) to easily copy data from each workbook and
paste it in a separate comparative workbook.
If you have questions or need assistance using ESIST, please contact us at esist@epa.gov.
NOTE: Cells formatted in blue are intended to be editable
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This first datasheet also includes four buttons. First, "Get started" brings the user to the next step in
ESIST. ESIST requires users to progress in sequence through the following seven steps, which are
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briefly described here. In ESIST version 1.2, all costs and prices are displayed in 2022 dollars.
Additional details on each of the steps are provided in the following sections.
1. Set study area: Users may set the study area to include:
a. Geographic areas, including the United States as a whole, each of the 50 states,
Washington, D.C., or Puerto Rico.
b. Sectors, including residential, commercial, industrial, or all sectors.
c. Utility types, including investor-owned utilities, retail power marketers, cooperative
utilities, municipal utilities, other types of utilities, or all utility types.
d. Specific utility service territories.
2. Set baseline electricity sales: Step 2 calculates a level of sales that serves as the
baseline for quantifying and displaying the impacts of energy efficiency investments over
time. By default, the historical baseline sales level has been adjusted to exclude the
impacts of energy efficiency programs from the historical period of 2010-2022. This
adjustment allows users to explicitly observe the impacts of energy efficiency, including
historical impacts and future impacts from past programs, separately from electricity sales.
Users can choose a growth rate that will apply to the default sales or input their own
historical baseline sales. A selected growth rate then projects future sales from 2023-
2045.
a. Set target type: ESIST allows users to choose from three different target types,
including:
b. Annual Budget: Set up an energy efficiency trajectory based on potential program
budget.
c. Annual Incremental Savings: Set up an energy efficiency trajectory based on the
annual incremental savings to be implemented in each year, measured in megawatt-
hour (MWh), or the annual incremental savings as a percent of previous-years' sales
(in %).
d. Cumulative Savings: Set up an energy efficiency trajectory based on a future-year
target for cumulative energy efficiency (in MWh or %).
Depending on the target type chosen, users progress through the next steps in the tool differently.
If Annual Budget is chosen:
1. Set program cost assumptions: Users set an estimated dollar-per-kilowatt-hour (kWh)
cost for incremental energy efficiency measures.
2. Set budget assumptions: Users set annual budgets (measured in million dollars spent)
for energy efficiency spending.
If Annual Incremental Savings is chosen:
1. Set savings trajectory: Users set an energy efficiency trajectory based on annual
incremental savings (measured in MWh).
2. Set program cost assumptions: Users set an estimated dollar-per-kilowatt-hour (kWh)
cost for incremental energy efficiency measures.
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If Cumulative Savings is chosen:
1. Set savings trajectory: Users set an energy efficiency trajectory based on a cumulative
target for savings in some future year (measured in MWh).
2. Set program cost assumptions: Users set an estimated dollar-per-kilowatt-hour (kWh)
cost for incremental energy efficiency measures.
Under all three target types, users have an option to progress to the final two steps:
1. Set multiple benefits and other settings: Users can choose whether to adjust or view
the assumptions that affect emissions impacts, public health impacts, energy burden
impacts, demographic data, peak demand impacts, customer information, and savings
expiration.
2. Review outputs: Users may view outputs including sales, savings, costs, avoided
emissions, public health impacts, energy burden impacts, and peak demand impacts.
Users may then compare results to other geographies and other scenarios if desired.
Figure 2 provides a graphical representation of these steps.
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Figure 2. ESIST steps
Set EE Target Based
on Annual Budget
Step 4.
Program Costs
Step 5.
Budget Assumptions
Step 2.
Set Baseline
Electricity Sales
Set EE Target Based on
Annual Incremental Savings
Step 4.
Annual Incremental
Savings Trajectory
Set EE Target Based
on Cumulative Savings
Step 4.
Cumulative Savings
Trajectory
Emissions
Impacts
Peak Demand
Impacts
Step 6. Set Multiple Benefits and Other Settings
Public Health
Impacts
Customer
Information
Energy Burden
Impacts
Savings
Expiration
Demographic
Data
Step 7.
Review Outputs
View User Manual
The second button, "View the User Manual" links the user to this document.
Summary of Costs and Benefits in ESIST
The third button, "Click here to see summary of costs and benefits" brings the user to a page
showing the same information shown in Table 1.
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Comparing Scenarios
The fourth button, "Restore default Excel functionality" resets the workbook to show typical Excel
features that are otherwise hidden from view. When users first open the tool, Excel features such
as the formula bar, column and row headings, and tab list are hidden from view to streamline the
user interface. Users who wish to access the supporting datasheets, or users who wish to export
datasheets to other standalone Excel workbooks, may wish to click this button. After clicking this
button, users may click it again to re-hide these same features.
This may be a particularly useful feature for users looking to conduct analyses for various
scenarios. For example, users may be interested in evaluating the costs, emissions, and public
health impacts associated with a scenario that models an energy efficiency savings level of
one percent annual incremental savings and a second scenario that doubles this level of savings.
Other users may be interested in evaluating the impacts of savings in some, but not all, utilities
within a state. Still other users may wish to evaluate each sector (residential, commercial,
industrial) separately within the same utility.
In these cases, users may save new copies of ESIST, each named to reflect the energy efficiency
parameters being chosen. In each saved copy, users may click the "Restore default Excel
functionality" to easily jump between tabs of different workbooks saved with different ESIST
scenarios and copy results to standalone workbooks for comparison. For example, users may then
compare outputs from Step 7 or charts between the ESIST scenarios. With default Excel
functionally restored, users can also export the results to a new workbook by right-clicking on the
tab name (e.g., "S7_Outputs"), selecting "Move or Copy," ticking the box labeled "Create a copy,"
and moving the tab to a new workbook. Users can then press CTRL+A, CTRL+C, and CTRL+V to
replace all linked formulas with static values. In this separate workbook, users can then perform
any additional comparative analysis.
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3. ESIST Step-by Step Instructions
The next seven sections of this manual provide details on each step in using ESIST. Following
these sections, the three appendices provide additional detail on methodology and usage of
ESIST:
• Appendix A: Data sources used in ESIST.
• Appendix B: Highlights several potential use cases of ESIST.
• Appendix C: Pilot Gas Version used for exploring impacts associated with reducing
consumption of natural gas.
Step 1: Set Study Area
The first step with ESIST is setting the study area. The default options within ESIST allow users to
define the area of interest in terms of geography (e.g., nationwide, statewide, or utility service
territory), utility type, and customer sector. These options align with the geographic areas for which
jurisdictions are most likely to explore setting savings targets. For example, each of the states that
currently has an EERS sets its targets on a statewide or utility service territory basis. ESIST allows
users to explore the implications of setting energy efficiency targets for different geographic areas,
such as jurisdiction-wide or for one or more utility service territories.
First, users select either a single state (including all 50 states, the District of Columbia, and Puerto
Rico) or the nation as a whole.
Second, users can select a customer sector within the chosen state or nation as a whole.
Customer sectors are defined according to EIA, which divides savings and sales into residential,
commercial, and industrial categories.13 Users may also select Total to analyze all three sectors
together.
Third, users can select a utility type. Utility types are defined as cooperative, investor-owned,
municipal, retail power marketer, and other.14 Users may also select Total to analyze all five utility
types together.
Fourth, users can select a specific utility. This option is only available after they have specified a
utility type (e.g., other than Total). Users can then choose from a list of utilities within that type and
state.
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13 ElA's Form 861 does not separately report data for the low-income residential sector. All data for low-income
residential customers is included within the residential sector. ESIST users who are interested in analyzing
detailed energy efficiency trajectories for the low-income residential sector should review the Energy Burden
Impacts module described in in Step 6.
14 "Other" utilities include political subdivisions, third-party demand-side management administrators, federal entities,
and other miscellaneous utility types. Some entities included in this data set have historical energy efficiency
savings, but no historical sales. For guidance on which utilities are active in which territories, see
https://www.eei.ora/about-eei/us-investor-owned-electric-companies and https://www.electric.coop/our-
oraanization/nreca-member-directorv. Users may also wish to consult with the website for their public utilities
commission (sometimes called a department of public utilities or public services commission) for more information
on the types of utilities that exist within their state of interest.
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After the study area is set, ESIST automatically loads all relevant historical data and default
projections for the selected area.15
Depending on the geography you have selected, ESIST may not load all the savings and sales
germane to your jurisdiction. In some parts of the country, energy efficiency is administered by third
parties, whereas electricity continues to be sold by electric utilities. In these cases, if a user selects
only the third-party demand-side management (DSM) administrator (denoted in the tool as "Other"
utility type), ESIST will not load any relevant data on sales.16 Conversely, if a user selects only the
utility, ESIST will not load any relevant data on savings. Users should take care to note whether
their selected region contains any substantial third-party administrators. In these cases, users may
be better served by setting their selected area to the entire state (thereby ensuring all sales and
savings get pulled from ESIST's data library), rather than selecting one utility or third-party
administrator at a time. Table 2 lists states where there are substantial savings reported by a third-
party administrator.
Table 2. States with substantial savings reported by a third-party DSM administrator
State
Name of third-party DSM administrator
DC
DC Sustainable Energy Utility
DE
Delaware Sustainable Energy Utility
HI
Hawaii Energy Efficiency Program
MA
Cape Light Compact
ME
Efficiency Maine Trust
NY
NYSERDA
OR
Energy Trust of Oregon
VT
Vermont Energy Investment Corporation
Wl
Focus on Energy
Note: This is not an exhaustive list. Many states have other governmental or semi-governmental organizations that
administer energy efficiency programs in all or part of the state.
Step 2: Set Baseline Electricity Sales
The next step within ESIST is to identify a baseline sales forecast for the user-defined study area.
The purpose of this step is to determine a baseline against which incremental energy efficiency
investments in future years can be applied.17
15 Due to the way dropdown selectors in Excel work, it is possible for a user to create non-existent geographies. For
example, a user could select a fine level of geographic resolution (e.g., "City of Aspen"), then change earlier
dropdowns such that an implausible selection is shown (e.g., the user changes the state from "CO" to "AK" where
no City of Aspen exists). In these situations, ESIST will display zeros for savings and sales and will display an
alert at the top of the page, since it will be unable to find any historical data that corresponds with the new,
implausible selection. Users should take care to only create plausible combinations of geographies.
16 Users may also observe the presence of utilities titled as "Adjustment." These rows reflect the sales associated
with non-respondents to ElA's Form 861.
17 For more information about developing a baseline from which to assess energy efficiency savings, see: EPA.
Including Energy Efficiency and Renewable Energy Policies in Electricity Demand Projections. (2015). Available at
https://www.epa.aov/statelocalenerav/including-enerav-efficiencv-and-renewable-enerav-policies-electricitv-
demand.
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ESIST Approach
The default baseline sales forecast in ESIST represents historical years without the impacts of
historical energy efficiency (2010-2022) and forecasted electricity sales based on an annual
electricity sales trajectory from ElA's AEO.18 ESIST uses data from EIA to estimate the cumulative
energy efficiency that was in place in the historical years.19 ESIST uses these data to adjust
historical sales, resulting in a value that represents sales that would have occurred were there no
historical energy efficiency implemented. This step is performed so that users can explicitly
observe the impacts of existing energy efficiency programs, both on past year sales, and on future
year sales. Without this step, there is a risk that users could double-count the impact of energy
efficiency in ESIST. Equation 1 shows ESIST's default equation for calculating baseline sales.
Equation 1. Demonstrative calculation of "baseline sales" for the year 2022
Baseline sales2022
= Reported sales2022 + Reported EE savings2022
+ Reported savings2021 + Reported savings2020 + ¦¦¦
+ Reported savings2011 — (Expired savings2021
+ Expired savings2020 + —I- Expired savings2011)
To estimate sales for future years (i.e., 2023-2045), the adjusted baseline sales value from the
most recent historical year (i.e., 2022) is multiplied by 1 plus the selected growth rate, as shown in
Equation 2.
Equation 2. Demonstrative calculation of "baseline sales" for the year 2023
Baseline sales2023 = Baseline sales2022 x (1 + Selected growth rate)
The columns in Step 2 of the tool are as follows:
• The first column describes the information contained in each associated row.
o There are three separate rows, each displaying information for annual sales growth,
reported sales (i.e., a sales trajectory that includes the impacts of energy efficiency
savings), and baseline sales (i.e., a sales trajectory that has had the user-defined
impacts of past energy efficiency savings removed from past years).
• The second column details the unit measurement of the row (% or MWh).
• Columns 3-13 present the historical data.
• The 14th and subsequent columns (to the right on each sheet) present projected data for
future years, based on the input assumptions chosen by the user.20
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18 Specifically, ESIST relies on data from AEO 2023's Reference case.
19 This process also accounts for whether any historical measures have expired in past years, based on the savings
expiration schedule defined in Step 6. Sales are tabulated from EIA 861, relying on data from "delivery" and
"bundled" service providers, but not "energy" service providers. The quantity of electricity sold by these "energy"
service providers is already captured in the "delivery" and "service" datapoints.
20 Data are provided through 2045 for users who wish to assess the impact of energy efficiency over a long time
horizon. This timeframe in ESIST is consistent with analyses such as ElA's AEO and utility integrated resource
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Alternatively, users may apply a historical baseline sales data set from their local utility, ISO, or
another source.21 To override the default annual sales values and import external sales (in MWh),
simply select the user input option and replace the default historical savings pre-populated by
ESIST with user-defined values. Users are encouraged to take steps to understand and
communicate whether and how historical energy efficiency program impacts are represented in
user-specified baseline data sets. If the effects of historical energy efficiency programs are
embedded in these values, users can consider making appropriate adjustments to their scenario to
avoid double-counting incremental energy efficiency savings.
Future Baseline Sales Growth
The next decision users make in Step 2 is the level of growth in electricity sales over the future
analysis time horizon. Users can choose annual or cumulative growth rates that apply to the most
recent historical year (2022) within ESIST. The chosen growth rate forecasts annual baseline sales
for all future years. ESIST's default option is the compound annual growth rate (CAGR) from ElA's
AEO Reference case.
Users can select one of the following annual growth rate options by clicking on the blue dropdown:
• Compound Annual Growth Rate (CAGR)
• Annual Average Growth Rate (AAGR)
• Historical CAGR
• User Input (%)
• User Input (MWh)
Table 3 compares each of these growth rate options and indicates recommended uses. The details
provided in this table and the next section are intended to help users understand the data sources
and assumptions that affect the future growth of baseline electricity sales so they can make an
informed choice within the tool. Note that growth rates from AEO (including CAGR and AAGR) are
region-specific, based on the region selected by the user, but do not change based on selections to
utility or sector.22
Users should check whether their sales projections take into account sales increases from
electrification. In situations where modeled energy efficiency programs contain electrification
measures, the sales projection should not include sales increases related to these measures to
avoid double-counting. In other cases, users may want their projections to account for sales
increases related to electrification measures unlikely to be covered by energy efficiency programs
(e.g., vehicle electrification).
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plans that extend several decades into the future to inform electricity system planning and investment decisions.
While extended planning horizons are necessary for these purposes, such values are inherently more uncertain
than near-term projections. Users interested in analysis over the short term (e.g., within the range of a single
energy efficiency measure's lifetime) can ignore years beyond the study period.
21 For more information on ISOs, see page 19.
22 See Figure 3 for more information on the regions modeled in AEO.
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Table 3. Comparison of baseline electricity sales options
Baseline
sales growth
rate
Includes
embedded
effects of
historical
energy
efficiency
Includes
embedded
effects of
some future
energy
efficiency
rebates
Includes
embedded
effects from
building
codes,
lighting, and
appliance
standards
Type of
average
Basis of
growth
rate
Recommended use
CAGR
Yes
Yes
Yes
CAGR
AEO
Reference
case
Best for capturing the
long-term trends in
electricity demand as
modeled in the AEO when
no other geographically
specific data are available
AAGR
Yes
Yes
Yes
Year-on-
year
AEO
Reference
case
Best for capturing the
near-term, year-to-year
variations in electricity
demand as modeled in
the AEO when no other
geographically specific
data are available
Historical
CAGR
No
No
No
CAGR
Historical
data from
EIA Form
861
Best when future growth
trends in baseline sales
are assumed to not
change substantially from
the past
User Input
(%)
Users should
determine
Users should
determine
Users should
determine
N/A
Custom
projection
Best when the user
already possesses a
geographically specific,
year-on-year growth rate
User Input
(MWh)
Users should
determine
Users should
determine
Users should
determine
N/A
Custom
projection
Best when the user
already possesses a
geographically specific
annual MWh change in
electricity sales
CAGR (Default)
CAGR applies a single compound annual growth rate to each future year. These values are from
ElA's AEO Reference case.23 The annual growth rates are specific to the selected state's
associated electrical area, as modeled in AEO (see Figure 3). These electrical areas are also
called Electricity Market Module (EMM) regions and are calculated using the formula shown in
Equation 3. With this selection, a single unchanging growth rate is applied to every year in the
23 Each year, EIA publishes a new version of the AEO, which contains projections of future energy use, prices, and
emissions that generally account for existing, "on-the-books" policies. AEO contains a number of different
modeled futures; ESIST uses the Reference case.
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analysis. For example, for the United States as a whole, the default value is about one percent per
year, applied in each future year. This selection is most useful if the user is less concerned with
year-on-year variations in electricity sales and instead wishes to apply an average growth rate to
the study period.
Equation 3. Equation for calculating CAGR in AEO
AEOCAGR=
fLast year electricity sales for census division y Number of years
\First year electricity sales for census division
vision y
ivision)
- 1
AAGR
The AAGR selection applies a growth rate that is specific to each future year. This selection allows
users to apply unique changes to sales in each year; in contrast, the CAGR applies a single
average change in sales over the entire time period. The AEO annual growth rate is most useful if
the user is attempting to capture the near-term, year-on-year variations in electricity sales modeled
in the AEO.24 These are best used when more geographically specific data (i.e., from a local ISO or
utility) are not available.
Figure 3. Map of EMM regions modeled in AEO
w\
FRCC
Source. https://www. eia. aov/outlooks/aeo/assumptions/Ddf/electricitv. odf
Note: This figure does not display Alaska, Hawaii, or Puerto Rico, which are assigned a default value equal to
the national average.
24 Series modeled in the AEO (including electricity sales) tend to observe larger variations in near-term years (i.e.,
through the next two to five years), as the AEO modeling takes into account near-term expectations for commodity
price changes, power plant construction, and weather. Over the longer term (i.e., from the mid-2020s through
2045), there tends to be less dramatic year-to-year variation in modeled values.
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Key Note—Allocating growth rates to states: AEO calculates sales projections for EMM regions that
are not aligned with state borders. In AEO, sales information is reported at the EMM region level. ESIST
assigns sales projections to each state based on which EMM region that state is most located in. For
example, Maine is in the "New England" census division; if Maine (or a constituent sector or utility) is
selected, ESIST automatically applies the growth rates for that region to that state.
Historical CAGR
The Historical CAGR selection is a CAGR based on the adjusted baseline sales for the selected
geography, calculated over the period from 2010-2022. This average is then applied to every
future year in this analysis. This selection is useful if the future growth rate is not expected to
change substantially from recent observed historical data. This may also be a good selection if
users wish to investigate the incremental impacts of future codes or standards aimed at reducing
energy consumption, as these future codes and standards by definition are not included in
historical sales.
User Input (%)
User input (%) allows users to input their own annual sales growth rate (on a percent basis) in
place of the default annual sales growth rates. Upon choosing this option, future values in the
"Annual sales growth rate" cells are shown in blue and can be modified. Any positive or negative
value can be entered in these cells.25 This is a useful selection if users have access to sales growth
rates (year-on-year or average) from an outside source. Users interested in identifying an
alternative to ESIST default rates—either for comparison to the default values or to replace the
defaults—are encouraged to consult one or more of the following:
• Utility integrated resource plans CIRPs'): Many utilities across the country are required by
statute or state regulators to submit an IRP detailing their plans for power plant
construction, electricity procurement, environmental compliance, and related matters.
Typically, these plans include one or more projections of electricity sales. Plans may be
released yearly or on a multi-year schedule.
• ISOs: ISOs frequently release their own projections of electricity sales, separate or in
addition to any sales projections made by utilities that are a constituent of the ISO's
geographical region. These sales forecasts may be developed for the ISO as a whole or for
more discrete constituent regions within the ISO, such as individual states or specific
electricity control areas.26
• Other sources: Users may also choose to rely on sales forecasts developed by
organizations other than EIA. These sales forecasts may be modified or adjusted versions
of forecasts found in utility IRPs or ISO studies, or they may have been independently
developed by state agencies, NGOs, or other entities.
25 Note that in cells formatted to be percentages, if a user enters ".2" Excel may interpret this entry as "20 percent." If
a user intends for ".2" to instead represent "0.2 percent," then he or she should enter "0.2" in the cell.
26 Two examples of ISO-developed sales projections include ISO New England's CELT forecast (available at
https://www.iso-ne.com/svstem-planning/svstem-plans-studies/celt') and New York ISO's Gold Book (available at
https://www.nviso.com/gold-book-resources').
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User Input (MWh)
The User input (MWh) selection functions similarly to the User input (%) selection, except with a
different format. This option allows users to input their own annual sales (in MWh format) in place
of the default annual sales. After making this selection, the future values in the "Baseline sales" row
are shown in blue and can be modified. Users can enter any positive value in these cells. Once the
sales are entered, ESIST will automatically show the user an implied annual sales growth rate. As
in the User input (%) selection, this is a useful selection if users have specific sales numbers from
an outside source such as an IRP or ISO, as described above.
Embedded Savings
Being clear on which energy efficiency program impacts are embedded in a baseline sales forecast
may be important to avoid double-counting and properly account for energy efficiency targets set in
Step 2. The term "embedded" is used to describe the energy efficiency savings that are included in
a baseline electricity sales forecast. Depending on what source is used for an electricity sales
forecast, embedded savings may be explicitly described, or may be accounted for in some way that
is difficult to discern. For example, both ISO New England and New York Independent System
Operator (NYISO) publish forecasts of electricity consumption each year that clearly identify the
amount of energy efficiency that is assumed in an expected or baseline future.27 Meanwhile, AEO
2023 does not explicitly note the quantity of energy efficiency modeled in its Reference case.28 As
a result, the default selections in ESIST (which are based on AEO 2023) do not explicitly remove
embedded savings from the baseline sales forecast for future years.
In situations where users are relying on an outside source for baseline electricity sales, users
should assess how their data source addresses the following items to avoid double-counting:
27 For information on ISO New England's 2024 forecast, see https://www.iso-ne.com/static-
assets/documents/100011/lf2024-forecast-data.xlsx. For information on NYlSO's 2024 forecast, see
https://www.nyiso.eom/documents/20142/2226333/2024-Gold-Book-Public.pdf/170c7717-1e3e-e2fc-0afb-
44b75d337ec6.
28 In 2018, EIA released a white paper (see: Fickling and Jarzomski. Modeling the Effects of Historical and Projected
Energy Efficiency Incentives. [2018], Presented at 2018 ACEEE Summer Study on Energy Efficiency in Buildings.
Available at https://www.aceee.ora/files/proceedinas/2018/index.html#/paper/event-data/p275) where it outlined
the methods used in AEO 2018 to model the impacts of ratepayer-funded energy efficiency. EIA modeled two
scenarios: one in which future energy efficiency rebates are implemented (as in the AEO 2018 Reference case),
and one in which these future energy efficiency rebates are not implemented. In AEO 2018, EIA modeled just one
component of energy efficiency savings—energy efficiency rebates. It did not project savings from other energy
efficiency strategies such as low-interest loans, behavioral programs, and market transformation activities. This
paper illustrated that the modeled savings in the AEO is relatively small; between 2020-2050, the cumulative
annual impact of customer-funded energy efficiency rebates is estimated to range from 0.2 to 0.4 percent of sales,
suggesting very small annual incremental impacts in any one year. In emailed communication dated September
2020, EIA described that their methodology has not changed substantially, but that they do not plan to regularly
release updates to this analysis. Our understanding is that this has not changed with the most recent AEO release
used in ESIST, AEO 2023. For these reasons, we have assumed that embedded energy efficiency savings in the
AEO projection are zero. Likewise, we observe that EIA has not materially updated assumptions related to
electrification, as the nationwide 2022-2040 CAGR for electricity sales has not varied substantially in any of the
nine AEO Reference cases released since AEO 2015 (with the CAGR for AEO 2023 of 0.60 percent compared to
CAGRs as low as 0.51 percent in AEO 2017 to a high of 0.77 percent in AEO 2016, and an average across all
nine AEO studies of 0.65 percent). Users interested in modeling the impacts of energy efficiency in the context of
ambitious levels of electrification may want to rely on other, region-specific load forecasts as inputs to ESIST
rather than projections from AEO.
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1. Savings from historical energy efficiency programs funded by utility customers.
2. Savings from some future energy efficiency rebates that lower the costs to customers of
certain energy-efficient equipment.
3. Savings from existing building energy codes, as well as existing federal appliance and
lighting efficiency standards.
4. Savings from future changes to building energy codes, as well as federal appliance and
lighting standards that are scheduled to take effect during the forecast period.
In many situations, it is possible or even likely that the source being used does not address any of
these four points.29 In these cases, users should be careful to caveat that their analysis may over-
estimate savings. Users may want to perform sensitivity analyses to determine the impacts of
potentially overcounting these savings.
Step 3: Set Target Type
Many states, local governments, and utilities around the country set energy efficiency targets to
ensure they achieve expected savings levels and associated policy objectives. Common policy
objectives include saving homeowners and businesses money, ensuring a robust electricity grid,
and achieving air quality and health benefits, among other benefits.30 Jurisdictions typically set
energy efficiency targets in terms of a quantity of annual incremental savings or cumulative
savings, or an annual budget level. ESIST supports each of these prevailing approaches to setting
targets.
After setting a study area and developing a projection for baseline sales, users must then set an
energy efficiency target type that serves as the basis for modeling savings and cost trajectories.
Users may select one of the three target types described below. Users may wish to make a
selection that most closely aligns with the locally preferred approach and available data from
sources such as energy efficiency plans, energy efficiency potential studies, or state and local
energy policies. In addition, tools such as the State and Local Planning for Energy (SLOPE)
Platform, can be used to reference energy efficiency potential by sector and state.31
Annual Budget
In this approach, users set annual budgets for energy efficiency spending (in constant dollars) and
inputs for energy efficiency costs (in dollars-per-kWh). The tool will calculate the savings trajectory
based on these parameters. This target type is likely to be most relevant for jurisdictions that are
analyzing the impacts of year-on-year changes in energy efficiency budgets.
29 The NYISO and ISO New England examples described earlier tend to be outliers on this issue.
30 For more information on quantitative estimates of these impacts, see EPA's 2018 edition of Quantifying the
Multiple Benefits of Energy Efficiency and Renewable Energy: A Guide for State and Local Governments,
available at https://www.epa.aov/statelocalenerav/guantifvina-multiple-benefits-enerav-efficiencv-and-renewable-
enerav-guide-state.
31 For more information on SLOPE, see https://maps.nrel.gov/slope/.
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Example: An energy efficiency plan filed with a PUC by a utility or third-party program administrator
has been capped at $40 million annually. Following state policy directives encouraging further
investment in energy efficiency, the SEO staff and NGOs, in addition to other stakeholders, may wish to
model the savings that accrue from raising the budget allowed for energy efficiency programs.
Annual Incremental Savings
In this approach, users set a trajectory for annual incremental savings. This is also sometimes
referred to as "first year savings" because the target quantifies the savings from new measures in
the first year the measures are installed. Users can create a trajectory by entering in an annual
quantity (measured in MWh) or a percent (measured in annual MWh saved divided by the previous
year's baseline sales). This target type is likely to be most relevant for jurisdictions that are
interested in analyzing the impacts of year-on-year changes in savings.
Example: An energy efficiency plan filed with a PUC by a utility or third-party program administrator
may include a savings target of 1.0 percent per year (measured as a percent of total electricity sales) in
2023 and 2024. PUC staff, utilities, and others can use ESIST to examine a scenario where this target is
maintained through 2045. In a jurisdiction where air quality is an issue, stakeholders may also compare
criteria pollutant emissions under the baseline scenario (e.g., 1.0 percent savings per year) to another
scenario where pollution limits are met (e.g., where the utility ramps up to a savings target of 2.5
percent in 2025 and maintains this higher target through 2045).
Cumulative Savings
Users set a cumulative savings target to achieve for some future year. Cumulative savings differ
from annual incremental savings (see Figure 4). Annual incremental savings are the new first-year
savings from measures that are installed in any one year. Cumulative savings are the sum total of
new and persisting savings in a given year, net of expired savings.32
32 We note that other organizations may use different terms for these definitions. For example, the American Council
for an Energy-Efficient Economy (ACEEE) uses the term "annual total savings" in place of what we consider
cumulative savings, and uses the term "cumulative savings" to refer to what would be a multi-year sum of what we
consider cumulative savings (see, for example, page 9 of Gold, R. and Nowak, S. Energy Efficiency Over Time:
Measuring and Valuing Lifetime Energy Savings in Policy and Planning. ACEEE. (February 2019). Available at
https://www.aceee.ora/sites/default/files/publications/researchreports/u1902.pdf). Other jurisdictions may use
other terms or may apply these terms to other definitions.
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Figure 4. Annual incremental savings versus cumulative savings
This target may be a cumulative quantity (measured in MWh) or a percent (measured in cumulative
MWh saved divided by the previous year's baseline sales). This target type is likely to be most
relevant for jurisdictions that are revisiting existing energy efficiency goals or considering new
energy efficiency goals that are established on a cumulative level of savings achieved in some
future year.
Example: A state may have a goal to reduce GHG emissions by 90 percent by 2050. An energy efficiency
potential study may identify a cumulative achievable savings potential of 25 percent in 2030. In
assembling a multi-sector plan for how to achieve the state's GHG reduction goal, the SEO staff NGOs,
and other stakeholders may wish to model the reductions attained by ramping up energy efficiency
programs to the 25 percent level.
Next, ESIST leads users through two steps (Steps 4 and 5) to set a savings or budget, and a cost
trajectory. These steps are presented in a different order depending on the target type selected in
Step 3. Under the annual and cumulative savings target types, users first set a savings trajectory
and then a cost trajectory. Under the annual budget target types, this is reversed, and users first
set a cost trajectory and then a budget trajectory. The next section breaks out the discussion of
setting a budget or savings trajectory by the three target types listed above.
Steps 4 and 5: Annual Budget
Under the annual budget target type, users establish a budget or spending level for energy
efficiency in each modeled year. First, users set an expected program cost for energy efficiency,
measured in terms of dollars-per-kWh. Second, users set an annual budget, measured in dollars.
ESIST then calculates annual incremental savings, measured in MWh.
Step 4: Set Program Cost Assumptions
In this step, users have four choices for setting the cost of energy efficiency, measured in dollars-
per-kWh:
• Constant
• Historical
• User Input
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Within ESIST, "costs" are defined as total program costs—i.e., the cost to the utility and program
participants to implement an energy efficiency program. These total costs are distinct from "net
costs," which account for energy efficiency benefits (such as in the context of an energy efficiency
program cost-effectiveness evaluation). Program costs are a key metric of interest to state and
local governments, utilities, and other stakeholders. These outputs have implications for the overall
magnitude of the target that the jurisdiction sets, how rapidly the jurisdiction wishes to ramp up the
full target level, and whether the ramp up is constant or varies overtime.
The following sections discuss each of the above choices in more detail.
Constant
When Constant is selected, ESIST loads a default cost of saved energy for the first year of the
energy efficiency program.33 This first-year cost of saved energy is calculated by dividing the total
program cost by the annual incremental savings (i.e., achieved in the first year). For a default cost,
ESIST relies on a 2020 Berkeley Lab study that finds a national, savings-weighted average
levelized utility cost of $0,026 per kWh (in 2018 dollars).34 Under the Constant setting, ESIST
assumes that this cost (or another cost chosen by the user) remains flat for future years. For the
historical years, ESIST reports actual historical cost data unique to the geography chosen. Users
may also update the utility share of costs input to more accurately represent the allocation in their
jurisdiction. The utility share of costs indicates how much of the total cost per kWh is allocated to
utilities versus the costs born exclusively by energy efficiency program participants.35
The 2018 Berkeley Lab study also describes energy efficiency costs for different states and
different sectors. Users have the option to rely on national-level data, state-specific data, sector-
specific data, or both state- and sector-specific data.36
33 The first-year cost of saved energy reflects the fact that funds collected via electricity rates from customers are
typically spent in the same year they are collected. The levelized cost of saved energy spreads the initial cost over
the expected lifetime of the efficiency measures, enabling comparisons between energy efficiency measures and
other traditional energy resources.
34 See: Lawrence Berkeley National Laboratory. "Still the One: Efficiency Remains a Cost-Effective Electricity
Resource." (2021). Available at https://eta.lbl.gov/publications/still-one-efficiencv-remains-cost. Values reported in
this document have been modified to reflect changes in dollar years (using data on inflation from the U.S. Federal
Reserve), splits in utility/participant spending, and a conversion from levelized costs to first-year spending (based
on ESIST's assumptions on dollar years and capital recovery factors, developed using data from this Berkeley Lab
study).
35 As a default, ESIST assumes a utility/participant split of 54%/46%, based on analysis in: Lawrence Berkeley
National Laboratory. "The Cost of Saving Electricity Through Energy Efficiency Programs Funded by Utility
Customers: 2009-2015." (2018). Available at https://eta-
publications.lbl.gov/sites/default/files/cose final report 20200429.pdf.
36 We rely on Berkeley Lab (2021) for utility cost data for all sectors together, individual sectors, and geographic
regions. We rely on Berkeley Lab (2018) for data on the utility share of cost input, and for information on state-
specific costs. We note that depending on the selected geography, users may observe a discontinuity between
historical costs of saved energy and the costs projected for future years from Berkeley Lab. This may be due to
inconsistencies in selected geography. For example, a discontinuity may appear if the user has selected a specific
utility, but chooses to base future costs on state-level estimates from Berkeley Lab. The discontinuity may instead
be due to differences in how these two sets of numbers are calculated. This is because historical costs represent
the utility-reported costs in any one year, while the Berkeley Lab estimates are calculated with information from a
number of years.
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Users may also update the discount rate, which is applied to calculate the levelized cost of saved
energy.37 Discount rates can be used to value future-year costs (or savings) by being combined
with a measure lifetime to calculate a "capital recover factor," which can then be used to convert
first-year costs of saved energy into levelized costs. All else being equal, higher discount rates
result in higher levelized costs of saved energy, while lower discount rates lead to lower levelized
costs of saved energy. ESIST automatically converts first-year costs to levelized costs using the
discount rates chosen by the user.
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Key Note—First-year versus levelized costs: To facilitate "apples-to-apples" comparisons between
energy efficiency and other energy resources, the first-year cost of saved energy can be reframed as the
levelized cost of saved energy (i.e., amortized cost). This spreads the initial cost over the expected
lifetime of the efficiency measures. ESIST does this by multiplying the costs by a capital recovery factor
(CRF):
1. Levelized Cost of Saved Energy = CRF * Total Costs / Annual Savings
The CRF is defined by a standard amortization formula:
2. CRF = r * (1 + r)n / [(1 + r)n - 1]
This second equation highlights the dependence of the levelized cost of saved energy on r, the discount
rate, and n, the estimated lifetime of the measure in years.
Historical
When users select Historical, the "First-year cost of saved energy" row uses the reported cost for
the selected geography in 2022 for all future years. Historical first-year costs of saved energy are
provided for all past years. These values are provided as a reference point, enabling users to view
and compare trends overtime. The calculation of historical costs uses total savings and energy
efficiency expenditure data from EIA Form 861,38
User Input
When users select User input, the "First-year cost of saved energy" row turns blue for future years,
allowing users to overwrite the cost of saved energy in every year of the study period. Because of
the variability in the types of costs used by different jurisdictions, ESIST presents costs from
several perspectives. ESIST automatically converts this first-year total cost into first-year utility
37 Discount rates commonly used in federal government decision-making related to energy efficiency include both
three percent and seven percent (see https://www.reainfo.gov/public/isp/Utilities/circular-a-4 reoulatorv-impact-
analvsis-a-primer.pdf). Seven percent represents the average cost of capital investments by corporations and
other entities, while three percent represents the societal discount rate based on the rate the average saver uses
to discount future consumption. The National Standard Practice Manual (available at
https://nationalefficiencvscreenina.org/wp-content/uploads/2017/05/NSPM Mav-2017 final.pdf) recommends
using a discount rate in the range of 0-3 percent for most purposes related to energy efficiency. For these
reasons, ESIST uses three percent as a default value.
38 For years before 2013, EIA includes a category of expenditures called indirect costs that are shared between
energy efficiency and load management programs. To determine the magnitude of indirect costs to include, ESIST
calculates the fraction of total savings from the combined energy efficiency and load management that was
achieved through energy efficiency by each utility for each sector. ESIST then includes these fractions of the
indirect costs in the total cost of energy efficiency for each utility and sector.
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costs using the utility share of costs chosen by users. ESIST then converts first-year utility costs to
utility levelized costs using the discount rates chosen by the user.
Step 5: Set Budget Assumptions
After setting the cost of energy efficiency (measured in dollars-per-kWh), users next set the
program cost or budget (measured in dollars). Users have three options within this selection:
• Constant Budget
• Percent Change
• User Input
The following sections discuss each of the choices in more detail.
Constant Budget
When users select Constant budget ESIST assumes that the annual program budget observed in
2022 is extended at the same level in each future year. Users may overwrite this annual program
budget with any number greater than or equal to 0. This option is most useful for users who wish to
analyze an unchanging level of expenditures in each year of the study period.
Percent Change
When users select Percent change, ESIST applies an annual year-on-year percent change in
program budgets, beginning with 2023 and continuing through 2045. This option is most useful for
users who wish to analyze a budget that grows or shrinks overtime.
User Input
When users select User input, all values in the "Annual Program Admin. Budget (2022 $ Million)"
row turn blue, indicating that the user may overwrite numbers in all future years. Users may
overwrite this annual program budget with any number greater than or equal to zero. This option is
most useful for users who wish to analyze a changing level of budgets throughout the study period.
These budget inputs are then used to calculate a savings trajectory. ESIST also uses this
information to calculate the total energy efficiency expenditures (i.e., inclusive of both utility and
participant costs), as well as the implied level of annual incremental savings (measured in both
MWh and savings as a percent of previous-year sales).
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Key Note—Reasonably achievable savings levels (annual budget): As with the annual and cumulative
savings target types, if users set an annual utility budget trajectory that implies annual incremental
savings greater than three percent in any given year, ESIST prompts the user to closely examine this
value. It is possible that some jurisdictions may achieve or exceed three percent annual incremental
savings for a limited set of years, although literature review indicates that this level represents the high
end of the achievable annual incremental savings range.
Steps 4 and 5: Annual Incremental Savings
Under the annual incremental savings target type, users establish an expected level of savings for
energy efficiency for each modeled year. Annual incremental savings are the new savings from
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energy efficiency expenditures in a given year and are sometimes referred to as "first-year
savings."
First, users set an expected level of annual incremental or first-year savings, measured in terms of
MWh. Second, users set an expected program cost for energy efficiency, measured in terms of
dollars-per-kWh. The model then calculates annual costs measured in dollars.
Step 4: Set Savings Trajectory
In ESIST, annual incremental savings can be framed in two ways: (1) in MWh or (2) in MWh as a
percentage of the previous years' MWh sales. Users should note that ESIST provides historical
data on savings for 2011-2022. (2010 is considered the baseline year, and energy efficiency
savings are not provided for this year.)
Under the "Savings Trend" dropdown, users can select from three options: Increase to Goal (the
default), User Input (savings as a % of sales), and User Input (MWh savings).
• Selecting Increase to goal (the default option) allows users to input an "Annual Savings
Goal (% of sales)" in percentage terms and a target year to start the increase. Users also
enter a "ramp rate," which is the percentage that sales increase in each year. Utilities that
are implementing increasing levels of energy efficiency typically only gradually increase
energy efficiency; it is rare for utilities to change from having no energy efficiency programs
in one year to being a leading energy efficiency utility in the following year.
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Example: Users can set a target savings level of 2.5 percent per year, a starting year of 2023, and
a ramp rate of 0.2 percent. In this example, the selected area will begin increasing savings in the
year 2023 at a level of 0.2 percentage points above recent historical levels, and will continue to
increase by 0.2 percentage points in each year until a level of 2.5 percent is reached at some
point in the future.1 After the goal is reached, the goal will be maintained for the remaining years
of the analysis period.
• With the User input (savings as a % of sales) option, future values in the "Annual
incremental savings" row turn blue, indicating to users that they can edit values for all
future years. These cells accept any value greater than or equal to zero percent.
• User input (MWh savings) allows users to edit annual incremental savings in MWh terms
instead. If this option is chosen, future values in the "Annual incremental savings" row turn
blue, indicating to users that they can edit values for all future years. These cells accept
any value greater than or equal to 0 MWh.
Key Note—Reasonably achievable energy efficiency savings levels (annual incremental savings): As
with the annual budget and cumulative savings target type, if users set an annual incremental savings
trajectory that implies annual incremental savings greater than three percent in any given year, ESIST
suggests that this value be re-examined. It is possible that some jurisdictions may achieve or exceed
three percent annual incremental savings for a limited set of years, although literature review indicates
that this level represents the high end of the achievable annual incremental savings range.
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Step 5: Set Program Cost Assumptions
In this step, users have the same options to set program costs, measured in dollars-per-kWh, as
are available in the Annual Budget target type (see page 23):
• Historical (default)
• Constant
• Savings-Dependent
• User Input
A description of the Savings-Dependent selection follows. For more information on Historical,
Constant, and User Input selections, see page 23.
Savings-Dependent
When users select Savings-dependent, ESIST prompts the user to adjust a specified first-year
cost of energy efficiency by three different adjustment factors that are each associated with a level
of savings achieved. For example, a user may specify a first-year cost of energy efficiency of $0.50
per kWh. They may then specify a level of savings of 0.5 percent, and a cost adjustment factor of
zero percent. ESIST would interpret this to mean that costs would remain unadjusted (e.g., $0.50
per kWh) up until a level of annual incremental savings of 0.5 percent were achieved. The user
would then enter a second tier of adjustments: perhaps a threshold of one percent annual
incremental savings and a cost adjustment factor of-10 percent. ESIST would then decrease the
specified cost by 10 percent (creating a new cost of $0.45 per kWh) for all years where savings are
greater than 0.5 percent but less than one percent. Users may specify three different cost
adjustment thresholds.
This approach is most useful in situations where users anticipate costs to vary according to
program level. For example, users may assume that costs decrease as programs get larger (e.g.,
as a result of economies of scale) or that costs increase as programs move through the energy
efficiency supply curve (e.g., as a result of lower cost opportunities being realized before higher
cost opportunities).
Step 4 and 5: Cumulative Savings
Under the cumulative savings target type, users establish a cumulative level of energy efficiency
savings for some future modeled year. Cumulative savings are the sum total of new and persisting
savings in a given year, net of expired savings.
First, users set an expected level of cumulative savings in some future year, measured in terms of
MWh, which ESIST then transforms into first-year or annual incremental savings also measured in
terms of MWh. Second, users set an expected program cost for energy efficiency, measured in
terms of dollars-per-kWh. The model then calculates annual costs measured in dollars.
Step 4: Set Savings Trajectory
In ESIST, cumulative savings can also be framed in two ways: (1) in MWh or (2) the MWh as a
percentage of the baseline MWh sales in some future year. For user reference, ESIST provides
historical data on savings for 2011-2022 (2010 is considered a baseline year).
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Under the "Savings Trend" dropdown, users can select from three options: Increase to Goal (the
default), User Input (savings as a % of sales), or User Input (MWh savings).
• Selecting Increase to Goal (the default option) allows users to input a "Cumulative
Savings Goal (% of sales)," a year in which this goal needs to be achieved, and a year in
which the ramp begins. This option is intended for users who wish to analyze a target level
of cumulative savings, but are not necessarily interested in year-on-year changes in
savings. For example, a user may choose to analyze a scenario in which savings reach 20
percent by 2030, on a cumulative basis. In this case, ESIST increases the cumulative
savings level beginning in the user-specified year (e.g., 2023). Savings levels from 2023-
2029 are then interpolated based on the 2030 goal and the level of savings modeled in
2020.39 After the goal is reached in 2030, the cumulative savings will be held constant at
this level for all future years.
• With the User input (savings as a % of sales) option, the "cumulative savings (% of
sales)" row turns blue, illustrating to users that they may input values for cumulative
savings (measured in savings as a percent of baseline sales) in each year for all future
years. Users may enter any value greater than zero percent. This option is intended for
users seeking to customize cumulative savings (on a percentage basis) in each year of the
study period.
• With the User input (MWh savings) option, the "cumulative savings (MWh savings)" row
turns blue, illustrating to users that they may input values for cumulative savings
(measured in MWh) in each year for all future years. Users may enter any value greater
than 0 MWh. This option is intended for users wishing to customize cumulative savings (on
a MWh basis) in each year of the study period.
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Key Note—Reasonably achievable energy efficiency savings levels fcumulative savings): As with the
annual budget and annual incremental savings target type, if users set a cumulative savings trajectory
that implies annual incremental savings greater than three percent in any given year, ESIST suggests that
this value be re-examined. It is possible that some jurisdictions may achieve or exceed three percent
annual incremental savings for a limited set of years, although literature review indicates that this level
represents the high end of the achievable annual incremental savings range.
Step 5: Set Program Cost Assumptions
In this step, users have the same options to set program costs (measured in dollars-per-MWh) as
are available in the Annual Incremental Savings target type (see page 28):
• Historical (default)
• Constant
• Savings-dependent
• User input
39 Under this option, ESIST maintains savings at historical levels from 2023 through the year before the "year to start
ramp." Savings are maintained at the target level in the "target year," as well as in all following years.
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Step 6: Set Multiple Benefits and Other Settings
State and local governments and utilities that are exploring options for setting new, expanded, or
revised energy efficiency targets are typically motivated by numerous objectives. Key objectives
include energy savings and costs (as described above), as well as multiple other priorities. These
priorities may be formally specified in legislation or regulation, or they may be indicated in the
platforms and agendas of governors, mayors, or their agencies' leadership. These objectives for
energy efficiency investments may include avoiding emissions, improving public health, advancing
energy equity, and achieving peak demand savings and associated grid reliability effects. ESIST
supports quantification of these impacts and facilitates analyzing savings expiration for energy
efficiency measures, customer demographic data, and other utility customer information. Users are
encouraged to carefully review the assumptions, data sources, and default values within the tool for
appropriate use in their jurisdiction.
After setting savings and first-year costs of saved energy, users can then adjust assumptions in the
six modules described in Figure 5. Outputs from Steps 1-5 are automatically passed through to
each of the modules within Step 6. These data are combined with the default assumptions within
these modules to generate the results in Step 7: Review Outputs. For all assumptions within Set
Multiple Benefits and Other Settings, if the user does not view or modify any information, ESIST
automatically applies default values.
While ESIST automatically calculates results from the assumptions within Set Multiple Benefits and
Other Settings, users may wish to adjust the default values in these modules. Changes to some of
the assumptions may impact the level of savings and total costs (e.g., modifying the trajectory of
savings expirations can impact annual incremental savings or leveiized costs).
Figure 5. ESIST interface for setting multiple benefits and other settings
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Step 6. Set Multiple Benefits and Other Settings
Set other inputs for the modules described below. If these modules are not edited, default values will be used.
Note that some modules may contain detailed results, rather than inputs that are meant to be edited by the user.
Estimate impacts on greenhouse gas and criteria pollutant emissions, and estimate losses to transmission and distribution (T&D).
Examine impacts on outdoor air quality and public health. This sheet is informational only; there are no inputs to edit.
Analyze energy burden and investment in energy efficiency, bill assistance, and other programs for low-income customers.
Examine demographic data for households in the selected service territory. This sheet is informational only; there are no inputs to edit.
Set up the expected relationship between annual electricity sales and peak demand, and annual incremental savings and peak avoided MW.
Examine historical information on number of customers, average rates, and average per-customer consumption.
This sheet is informational only; there are no inputs to edit.
Determine the schedule on which savings from energy efficiency measures expire.
Emissions Impacts
Public Health Impacts
Energy Burden Impacts
Demographic Data
Peak Demand Impacts
Customer Information
Savings Expiration
Emissions Impacts
ESIST calculates avoided emissions (i.e., the pollutants that would otherwise be emitted in the
absence of energy efficiency programs) associated with carbon dioxide (CO2), fine particulate
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matter (PM2 5), sulfur dioxide (SO2), and nitrogen oxides (NOx). ESIST calculates avoided
emissions at the regional level; it multiplies savings (which occur in a state or service territory from
the user-generated energy efficiency scenario) by emission rates that describe the estimated
regional change in emissions resulting from energy efficiency savings installed in a single state or
utility.
Default Assumptions
To estimate avoided emissions, ESIST relies on avoided emission rates data from three sources.
The data source used differs depending on the geography the user has selected, and the time
period the user is examining:
• If users have selected the United States as a whole, or a state or utility service territory
within the contiguous United States:
o Data from EPA's AVERT model is used for historical emission rates.40 AVERT
calculates avoided emissions for 14 different regions of the country; each state (or
utility service territory) is assigned to a single AVERT region and utilizes the
avoided emissions rates associated with that region.41 See Figure 6 for a map of
the AVERT regions.
o Data from the Integrated Planning Model (IPM) is used for marginal emission rates
in 2045. IPM is a capacity expansion and dispatch model that EPA routinely uses
to examine future changes to the electric power system. It models projections of
the electric power sector in the contiguous United States through 2050, with
individual power plants and load quantities mapped to 67 power regions.42 In
40 More information on AVERT is available at https://www.epa.aov/statelocalenerav/avoided-emissions-and-
generation-tool-avert.
41 Avoided emissions rates are calculated for each AVERT region, assuming that an energy efficiency portfolio
reduces regional fossil demand the same amount in all hours of the year. The assumed AVERT emission rates
are calculated by examining marginal emission rates in scenarios where fossil fuel load is decreased by 0.5
percent, roughly equivalent to recent observed levels of annual incremental energy efficiency savings at the
national level. These emissions rates are in line with the emissions rates described in EPA's "Emission Factors
from AVERT" fact sheet (most recent version available at https://www.epa.aov/avert/avoided-emission-factors-
generated-averf). Note that emissions rates resulting from higher levels of energy efficiency penetration (including
one percent and five percent) were also examined, although little variation was found in terms of typical emissions
rates. Note also that these are gross emissions rates (i.e., they have not been increased to reflect T&D losses).
The values for the United States as a whole are calculated using a weighted average based on sales. Emissions
rates assumed for 2010-2016 were calculated using AVERT version 2.3; emission rates assumed for 2017-2023
were calculated using AVERT version 3.0 and version 4.0.
42 Avoided emissions rates were calculated using IPM by running two scenarios: a base case and a scenario with a
2 percent reduction in demand in each modeled year. For each region, pollutant, and year, we calculated the
change in emissions between the two scenarios and divided that quantity by the change in generation to calculate
marginal emission rates in the modeled scenario. Because IPM models energy transfers across regions larger
than AVERT regions, we aggregate the 14 AVERT regions to 6 larger modeling regions, and estimate marginal
emission rates at that level. As a result of modeling noise, 10 percent of the resulting emission rate data points are
negative (with just 3 percent of data points more negative than -0.1 Ib/MWh). These data points are rounded to 0
Ib/MWh. See U.S. EPA. "Documentation for EPA's Power Sector Modeling Platform v6 Using the Integrated
Planning Model." (January 2020). Available at https://www.epa.aov/power-sector for more information. We
observe that marginal emission rates from these IPM runs resemble marginal emission rates calculated by ElA's
2023 Annual Energy Outlook (see https://www.eia.aov/outlooks/aeo/'). When compared to the National Renewable
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ESIST, we relied on data for 2028, 2030, 2035, 2040, and 2045. These regions are
aggregated to AVERT regions and assigned to the geography selected by the
user.
o For 2024-2027, the emission rates are linearly interpolated between the AVERT
and IPM modeled values. For all other years, values are linearly interpolated using
the most relevant IPM-derived values (for example values for 2031-2034 are
linearly interpolated using 2030 and 2035 values from IPM).
o Users can enter annual emission rates more specific to the region or year they are
interested in modeling, if those rates are available. Note that the benefit-per-ton
values ESIST uses to calculate public health impacts are linked to AVERT regions.
Users should take care to ensure that all user-input emission rates are consistent
with the topology used in ESIST when estimating public health impacts for a
scenario. See the following Public Health Impacts section for more information.43
• If users have selected a non-contiguous state or territory (i.e., Alaska, Hawaii, and Puerto
Rico):
o ESIST displays information on non-baseload emission rates from EPA's Emissions
& Generation Resource Integrated Database (eGRID) database for 2022.44 These
data are displayed for all years between 2010-2045. Users in these states and
territories are encouraged to utilize more regionally and temporally specific
emission rates if they are available.
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Energy Laboratory's 2022 Standard Scenarios (see https://www.nrel.gov/analvsis/standard-scenarios.html') the
marginal emission rates form IPM are lower. These differences can be attributed to impacts from differing
assumptions on air regulations, differing trends in renewable costs, and differing assumptions related to
electrification and load.
43 If users have questions on how to ensure topological consistency for user-inputted emission rates, please contact
EPA at esist@epa.gov.
44 Non-baseload emission rates are used because these are the eGRID emission rates that are most frequently
compared to AVERT or other marginal emission rate sources. For example, see the Greenhouse Gas Emissions
Technical Reference documentation for EPA's Energy Star Portfolio Manager (available at
https://portfoliomanager.energvstar.gov/pdf/reference/Emissions.pdf) or: Diem A., and Quiroz, C. How to use
eGRID for Carbon Footprinting Electricity Purchases in Greenhouse Gas Emission Inventories. (July 2012).
Available at https://www3.epa.gov/ttnchie1/conference/ei20/session3/adiem.pdf.
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Figure 6. Map of AVERT regions
Source: Figure 3 of the AVERT User Manual, version 4.3. Available at
https://www.epa.gOv/system/files/documents/2024-04/avert-user-manual-v4.3.pdf
ESIST provides default emission rates for CO:. PM2 5, SO2, and NOx. For all pollutants, ESIST
provides emission rates at an annual level (i.e., annual tons emitted per total annual MWh). For
NOx, we also provide emission rates for the ozone season.45
Future marginal emission rates in ESIST are best used as screening-level estimates of regional
avoided emissions due to energy efficiency programs. There is substantial uncertainty in future
marginal emission rates. Marginal emission rates can vary widely depending on assumptions that
change quickly year-to-year (e.g., costs of renewable resources, information on planned
conventional resource retirements). They also vary based on the local information available (e.g., a
utility may have more granular, up-to-date data on resource costs or retirement dates), and the
scenario being considered (e.g., a state energy office may be considering two different scenarios:
one with high amounts of renewables and one without, each of which would have different marginal
emission rates).
Users looking to assess the range of uncertainty in the default emission rates can examine different
sets of emission impacts. With the User input setting, users could apply either the 2023 or 2045
emission rates over the entire study period (for example). Users can then compare this with the
results calculated using the Default setting for emission rates to evaluate a low, medium, and high
estimates of emission rates over the study period. Because public health impacts (see page 35)
are directly linked to the assumed emission rates, this sensitivity analysis may be of special interest
to users focused on those impacts.
45 We note that these ozone season emission rates are used to calculate public health benefits related to avoided
ozone (see the Public Health Analysis section below). Total avoided NOx for the ozone season should not be
added total annual avoided NOx as this will result in some double-counting of NOx emissions. However, benefit-
per-ton values used in ESIST do not double-count the impact of NOx emission rates for the purposes of ozone and
particulate matter benefits.
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ESIST also analyzes the effect that the transmission and distribution (T&D) loss factor has on
emission impacts. This loss factor refers to the amount of electricity that is lost in the form of heat
as it travels from electricity generating units through the transmission and distribution wires to
homes and businesses. Modeled EIA data for 2010-2022 indicate that on a national basis, the
T&D loss factor is about seven percent.46 Effectively, this means that for every 1 MWh of energy
efficiency installed at the retail level, 1.07 MWh of generation at the wholesale level (i.e., the power
plant level) are displaced.47 The adjustment allows users to fully account for additional emissions
benefits from avoided inefficiencies on the grid, in addition to avoided electricity used at the retail
level. ESIST displays the avoided annual generation, as well as the avoided generation for the
ozone season of May 1 through September 30 (for the purposes of calculating ozone-related
benefits).
Other Data Sources
Users can also adjust avoided emissions factors using their own data sets if they prefer information
from organizations such as a regional planning organization, state department of environmental
quality, or from other modeling platforms that project emissions and emission rates. Importantly,
ESIST uses benefit-per-ton values to calculate public health impacts that are linked to AVERT
regions (see the following section on Public Health Impacts for more information). If users input
their own emission rates, users should take care to choose a topology that is aligned with the
AVERT topology used in ESIST. Otherwise, it is likely that the public health impacts reported by
ESIST will not align with the user-selected emission rates. To submit questions on how to ensure
topological consistency for user-inputted emission rates, please contact EPA at esist@epa.gov.
Similarly, users can adjust T&D loss factors if they have specific information from regional utilities,
ISOs, or regional transmission organizations. Adjusting these factors allows users to calculate
avoided emissions—i.e., the pollutants that would otherwise be emitted in the absence of energy
efficiency programs.
Users may wish to use EPA's AVERT model to calculate emission rates for specific energy
efficiency measures or portfolios.48 AVERT allows users to set up 8,760 hourly demand reduction
profiles and examine their impact on one of 14 regions throughout the United States. AVERT
leverages statistical analysis of generation and emissions data from a recent historical year to
allows users to estimate changes in generation and pollutants (including SO2, NOx, primary PM2 5,
volatile organic compounds, and NH3) at specific power plants throughout the country. Unlike
ESIST, AVERT allows users to examine more detailed emission impacts (from both a spatial and
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Key Note—Using ESIST data in AVERT: An alternate use of ESIST is to develop a trajectory for annual
energy efficiency savings and analyze these savings in AVERT. With this approach, users can manually
input the MWh savings outputs from ESIST into AVERT to generate additional data on avoided emissions
(including locations of decreased emissions, as well as hourly emissions profiles associated with that
specific region's power plants) for a year in the recent past or near future.
46 All T&D loss factors are calculated using modeled data from ElA's AEO, with the latest year referencing AEO
2023. These are the same T&D loss factors used in the AVERT model.
47 Generally, 1 MWh of energy efficiency installed at the retail level displaces a quantity of wholesale generation
equal to retail MWh / (1 - T&D loss factor).
48 For more information, see https://www.epa.aov/avert.
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temporal perspective), but is limited to analysis for a year in the recent past or near future (i.e.,
within the next 5 years). It also includes mapping and emissions analysis functionality not present
in ESIST.
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Procedure for Making Changes in ESIST
Avoided emissions factors can be adjusted by selecting User input. This turns avoided emissions
values for all future years blue, indicating that users may overwrite these values. These cells
accept any "lb per MWh" value; positive numbers are associated with emissions decreases,
whereas negative numbers (if entered) are associated with emissions increases. In addition, if
Default is selected, users are encouraged to enter their own avoided emissions rates for all future
years, either for each year individually, or for all future years together under the selection cell.
To adjust the T&D factor input, users select User input instead of the default EIA option. Selecting
this option turns all values for future years blue, indicating that they can be overwritten. Users may
enter any value between zero percent and 15 percent.49
Key Note—Changing emissions rates: Users that expect their modeled electricity system to become
cleaner over time (e.g., through retirements of higher-emitting plants or through the addition of low- or
zero-emitting plants) should note that the avoided emissions resulting from energy efficiency will
decrease over this period. Like all values in ESIST, users can replace default values with their own values
if available. For example, users may choose to replace the default avoided emissions rates with avoided
emissions rates produced by an electric sector dispatch and capacity expansion model, which itself
models the change in resource mix over time, leading to different avoided emissions rates than what
exists today.
Public Health Impacts
The ESIST Public Health Impacts module estimates the dollar value of avoided air pollution-related
health impacts associated with reducing electricity demand.50 Increased levels of energy efficiency
lower electricity demand, lowering the amount of electricity produced and pollutants emitted by
power plants. Improved air quality benefits human health in the form of avoided cases of premature
death, hospital visits, cases of aggravated asthma, and other adverse outcomes. These outcomes
can be "monetized" by estimating the dollar value that society places on avoiding these effects.
These estimated dollar values may in turn be used to calculate a "benefit-per-ton" metric, which
expresses the total economic value of the health benefits of reducing emissions on a per-ton basis.
ESIST calculates health impacts at the national level; it multiplies the emission impacts (which
occur at a regional level and result from savings changes at a state or utility level) by standard
49 Data from AEO suggests average nationwide T&D losses are on the order of seven percent. Different regions may
have different loss rates, with some higher and others lower. We observe that in the 2018-2019 Puerto Rico
Integrated Resource Plan (Prepared for Puerto Rico Electric Power Authority, published June 2019, and available
at https://eneraia.pr.gov/wp-content/uploads/sites/7/2019/06/2-IRP2019-Main-Report-REV2-06072019.pdf).
expected losses were 12 percent (see Table 3-11). ESIST rounds up and assumes a maximum T&D loss value of
15 percent for anywhere in the United States based on this value.
50 All public health impacts estimated in ESIST are related to outdoor air quality impacts. Other public health impacts
related to indoor air quality may be substantial for certain populations but are not estimated in ESIST. For more
information on this topic see https://www.epa.aov/indoor-air-aualitv-iag.
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benefit-per-ton factors. These factors describe the national health benefits resulting from regional
changes to emissions.
The section below summarizes the methodology used for calculating effects from air pollution and
how these effects are quantified and monetized. This section simplifies an inherently complex
procedure. Readers interested in learning more about the adverse health effects associated with
poor air quality and the procedure for quantifying and monetizing these impacts may refer to other
resources, including EPA Technical Support Documents and the peer-reviewed literature.51
Default Assumptions
ESIST estimates the dollar benefits of reducing ground-level ozone and PM2 5 precursor emissions
from power plants located throughout the United States. The benefit per ton reflects the value of
avoided cases of PM2 5 and ozone-related premature death and illnesses. Benefits include
reductions of premature mortality, hospital admissions, cases of new onset asthma, and other
adverse outcomes.52
Calculated values are based on previously conducted air quality modeling simulations paired with
the environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE).
First, air quality simulations were conducted to characterize the relationship between emissions
and ozone or PM2 5 changes. The modeling tracked the impacts of state-level power plant NOx
emissions to national changes in summertime ozone and the impacts of state-level power plant
NOx, SO2, and primary particulate matter (PM) emissions to national changes in annual average
PM2 5.53 Second, BenMAP-CE was used to quantify and monetize the benefits of improved air
quality.54 Finally, after estimating the dollar value for each scenario, the estimated benefits were
then divided by the number of tons of precursor emissions reduced. PM2 5 precursors include NOx,
SO2, primary PM2 5 and the precursor for ozone is ozone-season NOx. Benefits and emissions
51 U.S. EPA. Technical Support Document Estimating the Benefit per Ton of Reducing Directly-Emitted PM2.5,
PM2.5 Precursors and Ozone Precursors from 21 Sectors; Research Triangle Park, NC, September 2023.
https://www.epa.gov/svstem/files/documents/2021 -10/source-apportionment-tsd-oct-2021 O.pdf.: U.S. EPA.
Technical Support Document (TSD) for the Final Revised Cross-State Air Pollution Rule Update for the 2008
Ozone Season NAAQS Estimating PM 2.5-and Ozone-Attributable Health Benefits; Research Triangle Park, NC,
2021.; Fann, N.; Baker, K. R.; Fulcher, C. M. Characterizing the PM2.5-Related Health Benefits of Emission
Reductions for 17 Industrial, Area and Mobile Emission Sectors across the U.S. (2012). Environment International,
49, 141-151. Available at https://doi.ora/10.1016/i.envint.2012.08.017: Wolfe, P.; Davidson, K.; Fulcher, C.; Fann,
N.; Zawacki, M.; Baker, K. R. Monetized Health Benefits Attributable to Mobile Source Emission Reductions
Across the United States in 2025. (2019). Science of The Total Environment, 650, 2490-2498. Available at
https://d0i.0rg/l0.1016/J.SCITOTENV.2Q18.09.273: Sacks, J. D.; Lloyd, J. M.; Zhu, Y.; Anderton, J.; Jang, C. J.;
Hubbell, B.; Fann, N. The Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-
CE): A Tool to Estimate the Health and Economic Benefits of Reducing Air Pollution. (2018). Environmental
Modelling & Software, 104. Available at https://doi.org/10.1016/i.envsoft.2018.02.009.
52 Note that other public health benefits may exist but are unqualified. For a complete list of the public health
benefits accounted for in ESIST, see: EPA. Integrated Science Assessment for Particulate Matter (Final Report).
(2019). Research Triangle Park, North Carolina; and EPA. Integrated Science Assessment of Ozone and Related
Photochemical Oxidants (Final Report). (2020). Research Triangle Park, North Carolina.
53 U.S. EPA. Regulatory Impact Analysis for the Repeal of the Clean Power Plan, and the Emission Guidelines for
Greenhouse Gas Emissions from Existing Electric Utility Generating Units. Research Triangle Park, NC, 2019.
EPA-452/R-19-003
54 U.S. EPA. Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE). (2018).
Research Triangle Park, North Carolina.
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associated with particular power plants were calculated by state and for the nation as a whole.
These were then allocated to the 14 AVERT regions to create a topology consistent with ESIST's
avoided emission rates.55 A high-level illustration of how emissions are translated into public health
benefits is shown in Figure 7,
Figure 7. Illustration of how pollutant changes are translated into public health impacts
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Pollutant change Population
Baseline incidence
Effect
estimate
Health
impact
ESIST then multiplies the change in total precursor emissions against the health benefit-per-ton
values (measured in terms of dollars per short ton of avoided emissions) to yield the total estimated
health benefits associated with reducing emissions from each pollutant. Benefits are categorized
into (a) those linked to reduced PM emissions and (b) those linked to reduced ozone emissions.
The "Total" row represents the total monetized benefits related to both PM and ozone.
To simplify the presentation of results in ESIST, we present values calculated at a three percent
discount rate, short-term (i.e., day-to-day) exposure to ozone (rather than long-term, or the
cumulative exposure over the course of a year), and "low" impacts related to PM2 5 (which
correspond to a wider range of ages assumed to be impacted by PM rather than the "high" impacts
also calculated). These assumptions are relatively conservative. For these last two variables in
particular, which use relatively conservative default assumptions, different assumptions would likely
produce larger health impacts.56
Other Data Sources
Should users wish to calculate public health impacts using other data sources, they may wish to
export the avoided emissions calculated by ESIST to an alternative model, such as EPA's COBRA
model and the BenMAP-CE model.57 Users may observe differences in total public health benefits
due to differences in model assumptions and methodologies. COBRA allows users to calculate the
public health impacts related to reductions in PM2.5 emissions (including emissions from related
pollutants such as SO2, NOx, and primary PM2.5). Unlike ESIST, COBRA allows users to calculate
public health impacts related to emission changes in other sectors than the electric power sector
and allows users a finer degree of control over where emission changes are taking place (i.e., at
the county level). It also includes mapping functionality not present in ESIST.
55 Emissions and benefits were allocated to AVERT regions based on the quantity of emissions modeled from power
plants in each AVERT region.
56 For more information on the distinctions between these different settings, see the BenMAP-CE user manual at
https://www.epa.gov/benmap/benmap-ce-manual-and-appendices.
57 For more information, see https://www.epa.gov/cobra.
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We do not recommend that users use alternate values for benefits per ton in ESIST, as the current
values have been calculated to be as consistent as possible with other parameters calculated in
ESIST.
Energy Burden Impacts
The Energy Burden Impacts module in ESIST models the impact of low-income energy efficiency
and monetary assistance programs on household energy and electricity burdens.58 Specifically,
users can estimate the level of investment required to reduce the disparity between low-income
households' electricity burden (the proportion of income spent on electricity) and the electricity
burden of non-low-income households.59 Nationwide, low-income households spend a greater
share of their income on electricity than higher-income households, producing disparities in energy
and electricity burdens.60 Users can observe disparities between low-income and non-low-income
households in their selected geography, and model energy efficiency and monetary assistance
programs that help close the gap in electricity burden between low-income and non-low-income
households. Users may use this module to examine goals for energy efficiency programs targeted
to reach low-income households.
The Energy Burden Impacts module is not functional when the user has chosen a sector other than
Residential in Step 1,61 If the user has selected the Residential sector, they have two options: (a)
model low-income savings as a component part of the Residential sector (i.e., within Steps 2
through 5) or (b) model low-income savings separately from the residential sector in the Energy
Burden Impacts module. If a user is following approach (a), they should not use the Energy Burden
Impacts module to model additional low-income savings. Instead, they should model Residential
savings as if low-income savings were included in a broader pool of energy efficiency savings
installed at all residences. If a user is following approach (b), they should assume that the savings
modeled in the Energy Burden Impacts module are distinct from the savings modeled elsewhere in
the analysis. Note that emissions impacts and public health impacts are calculated solely based on
the savings modeled in Steps 2 through 5 and that any savings modeled in the Energy Burden
Impacts module do not produce additional emissions or public health impacts. In addition, note that
all savings modeled in the Energy Burden Impacts module are assumed to be additional to any
existing savings from low-income energy efficiency programs in the user-specified geography.
Default Assumptions
ESIST primarily relies on energy burden data from the U.S. Department of Energy's Low-Income
Energy Affordability Data (LEAD) Tool.62 The LEAD Tool was created to help states, communities,
58 In this module, low-income households are defined to be any household below 200 percent of the Federal Poverty
Level.
59 "Energy burden" refers to the sum of a household's expenditures on energy (i.e., electricity as well as natural gas,
oil, and/or gasoline) divided by its income. "Electricity burden" refers to a household's expenditures on electricity
alone, divided by household income.
60 ACEEE. How High Are Household Energy Burdens? An Assessment of National and Metropolitan Energy
Burdens Across the United States. (2020). Available at https://www.aceee.org/enerav-burden.
61 We note that the Energy Burden Impacts module is also available in the ESIST: Pilot Gas Version. See Appendix
C: ESIST: Pilot Gas Version for more information.
62 For more on the LEAD tool, see https://www.enerav.aov/scep/slsc/lead-tool.
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and other stakeholders create better energy strategies and programs by improving their
understanding of low-income housing and energy characteristics. Data in the LEAD Tool come
from the U.S. Census Bureau's American Community Survey 2020 Public Use Microdata Samples.
More information on how low-income energy efficiency programs are reported in this data set, and
how these programs may be subject to some double-counting, can be found in the Other Data
Sources section on page 42.
County-level income and energy expenditure data from the LEAD tool have been compiled into a
library table in ESIST. ESIST automatically selects the appropriate counties relevant to the user-
specified geography. For example, if a user selects a single state, it will retrieve all the LEAD data
for all the counties in that state. If a user selects a single utility, then ESIST will retrieve data for all
the counties in which that utility serves at least one customer.63 ESIST will then load data from
LEAD on the number of households, average energy burden, average electricity burden, and
average income. Data are provided both for low-income households (defined in ESIST as any
household below 200 percent of Federal Poverty Level) and all other households defined as non-
low-income.64
This module also relies on a number of parameters specified from the user or calculated elsewhere
in ESIST. All the following values are specified in the User inputs section, except for the "Low-
income program cost multiplier," which is specified in the Scenario calculations and outputs
section.
These include:
• Electricity burden target: This is the target electricity burden envisioned for low-income
households. This value must be less than or equal to the current low-income electricity
burden (as calculated by ESIST) and must be greater than zero percent. Based on this
input, ESIST will then calculate the electricity cost reduction needed at average low-income
households to meet this new electricity burden. Each year, this reduction is applied to the
number of households specified by the user (see following bullets for more information).
The default assumption in ESIST is set equal to the selected region's current electricity
burden for low-income households.
Year to begin program: This is the first year that the low-income program scenario (as
envisioned in the Energy Burden Impacts module) would be deployed. Users may enter
any value for all future years. The default value is set to 2023.
% of households to reach annually: This is the number of new households the program
scenario will reach each year. The default value is set to 10 percent. In effect, this
assumes that each year, 10 percent of all low-income households in the area being
analyzed have their electricity burdens adjusted to the parameter entered in Electricity
63 This matching is done using data from EIA Form 861. Utility-specific data on the number of customers or kWh
sales on a county-by-county basis are not available. Note that there are situations in which the vast majority of a
utility's customers are in County X, but a small minority of customers exist in County Y. ESIST will report
household income and energy expenditure data from both counties, as if the utility served an equal number of
customers in both counties. Users should cross-reference other data sources for information on which counties
are most representative of the service territory relevant to their interest.
64 For detail on the poverty guidelines as defined by Health and Human Services, see
https://aspe.hhs.aov/topics/povertv-economic-mobilitv/povertv-auidelines.
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burden target. With an assumption of 10 percent of households per year, a 2023 starting
year, and the default low-income measure expiration (described below), by 2045, the
program scenario will have been deployed to almost 90 percent households in the selected
geography. Not all programs may be able to reach 90 percent of customers overtime.
Traditional energy efficiency programs may not successfully reach some customers due to
issues regarding language, rental status, health and safety barriers, and interest in
participation, among other reasons. To reach shares approaching 100 percent, programs
targeting low-income households would have to be designed to overcome these barriers.
• Measure expiration: This parameter allows users to select a measure expiration trend to
apply to energy efficiency savings modeled in the Energy Burden Impacts module. The
default setting assumes that energy savings from efficiency measures expire consistent
with how savings have been observed to expire from past low-income energy efficiency
programs. Users may also choose a "Linked" option, which links the measure expiration
trend to the one specified by users in the "Savings Expiration" module (which includes
further options—including user inputs—beyond the default low-income trend). See the
Savings Expiration section on page 49 for more information.
• Monthly monetary assistance per household: This parameter provides an option for
users to model monetary pay assistance to low-income households. Users can specify any
value greater than or equal to $0. The default value is set to $0 per household per month.
The user is encouraged to look into the data for their specific geography and identify a
suitable value.65 This input is called "monetary assistance" because it can be used to
model any form of low-income monetary assistance, including bill pay, reduced customer
rates, or direct rebates. It can also be used to model bill assistance from outside the utility
(e.g., the Low Income Home Energy Assistance Program or LIHEAP). This value is applied
to all low-income households for every year in the analysis, including and following the
"year to begin." It is not customized to apply to only a percentage of households receiving
assistance. Therefore, users should set this parameter equal to the average amount of
monetary assistance across all low-income households. For example, if 50 percent of low-
income households are expected to receive monetary assistance of $20 per month and all
other low-income households are expected to get no monetary assistance, the user should
enter a value of $10.
• Low-income program cost multiplier: This parameter specifies the difference in the cost
of saved energy for programs delivered to low-income customers, relative to the cost of
delivering these programs to non-low-income residential customers. These cost differences
may be due to the type of programs that are offered to low-income customers, versus non-
low-income residential customers (e.g., whole-home improvements). The default value,
provided by Berkeley Lab in its analysis presented in Still the One: Efficiency Remains a
Cost-Effective Electricity Resource, is 337 percent, indicating that the cost of electricity
savings in low-income households is about 3.4 times greater than in non-low-income
households.66
65
For more information on possible values to use, see the LIHEAP Data Warehouse at
https://liheappm.acf.hhs.gov/datawarehouse/.
For more information on this data set, see https://emp.lbl.gov/news/still-one-new-studv-finds-efficiencv.
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With this information, ESIST then calculates the gross annual electricity cost reduction needed to
overcome the electricity burden gap observed between low-income households and non-low-
income households (see Equation 4). ESIST also calculates the net annual electricity cost
reduction, after taking any monetary assistance into account (see Equation 5). Third, in the
Scenario calculations and outputs section, ESIST estimates the cost to administer these
programs, calculated by multiplying the number of households in a jurisdiction by the share of
households planned to be reached each year and the user-specified cost to administer energy
efficiency in low-income households. Finally, in the Energy burden outputs section, ESIST
estimates the impact that the program has on the average low-income customer's electricity burden
and energy burden over time. This last section also estimates the electricity burden for a household
participating in the first year of the program. Typically, these households will have the target
electricity burden value in the first year of the program. Then, overtime, this electricity burden will
increase as energy savings from efficiency measures expire.
Equation 4. Estimation of gross annual electricity cost reduction
Gross annual electricity cost reduction
= (.Average Electricity BurdenLl Househoids
- Target Electricity BurdenLI Househoids)
x Average Household IncomeLI Househoids
Equation 5. Estimation of net annual electricity cost reduction
Net annual electricity cost reduction
= Gross annual electricity cost reduction
— (Monthly Monetary Assistance x 12 months)
Other Functionality
The Energy Burden Impacts module contains a number of other calculations that give the user
additional insight into how investments in energy efficiency could impact energy burden:
• In the User inputs section, users may choose to "Assess programs that reduce gas use."
This is a useful option for users interested in the impacts of low-income weatherization
programs or other programs that reduce consumption of fossil fuels in addition to
electricity.67 If users select this option, they will be prompted to enter or modify defaults for
gas therm savings per kWh savings (i.e., the number of therm savings that are expected in
a weatherization program, divided by the number of kWh savings expected from the same
program) and retail gas rate (in dollars per therm).68 A new section will appear at the
bottom of the page titled Scenario calculations: Gas savings. This section uses EIA and
LEAD data to perform analogous calculations related to gas savings and program costs.
• The Scenario assumptions section includes two subsections. It is hidden by default, but
users may view additional rows by clicking the "+" icon on the left side of the screen.
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67 In ESIST, weatherization measures are those that provide both electricity savings (in kWh) and gas savings (in
therms). This includes measures like insulation, pipe wrap, and deep energy retrofits. These are in contrast to
measures that provide electricity savings only (or primarily), such as lighting and refrigeration measures.
68 See Key Note on low-income weatherization assumptions for more information on the default assumptions used.
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o The Household calculations section describes background calculations related to
electricity burden.
o The Other assumptions section describes background data used for calculating
electricity burden for both low-income customers and non-low-income customers.
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Key Note—Low-income weatherization assumptions: If users wish to assess programs that reduce gas
use in addition to electricity consumption, ESIST provides users with two key default assumptions.
The first, therms saved per kWh saved, refers to the quantity of gas savings that users can expect
alongside electricity savings in a low-income weatherization program. The default—0.23 therms saved
per kWh saved—is calculated based on a simple average of values observed in recent low-income
weatherization programs in Colorado, Maryland, Massachusetts, Minnesota, Idaho, and Washington.
This value varies widely across states in programs: it is as low as 0.03 therms saved per kWh saved in
recent low-income weatherization programs in Maryland, and is as high as 0.83 therms saved per kWh
saved in recent programs in Minnesota. These datapoints may differ in terms of program scope,
reporting requirements, or other aspects. Users should use data specific to their selected geography
wherever possible.
The second key assumption is the residential rate for natural gas (measured in dollars per therm). Data
for this assumption are based on information from the 2019 edition of ElA's Natural Gas Annual report
(available at https://www.eia.gov/naturalgas/annual/). Users should take note (a) if they have more
locally specific data and (b) whether they expect this value to vary substantially over time.
For more information on gas energy efficiency savings, see Appendix C: ESIST: Pilot Gas Version at the
end of this document.
Other Data Sources
ESIST contains limited historical data on existing low-income energy efficiency programs. Data on
energy consumption and energy savings reported by EIA and DOE typically report impacts from
low-income energy efficiency programs as contained within the same data columns as residential
programs. As a result, it is not usually possible to identify the impact of low-income energy
efficiency programs in isolation, or to identify where and how the impacts from these programs may
overlap with the savings modeled in the Energy Burden Impacts module. In general, users should
think of the savings modeled in the Energy Burden Impacts module as being additive, incremental,
or separate from any existing low-income energy efficiency programs in the selected jurisdiction.
Users can adjust the user input values and annual targets related to program spending and
participation by referring to specific utility program filings or other resources. ACEEE's state-by-
state database, Guidelines for Low-Income Energy Efficiency Programs, also lists existing
requirements for state and utility support of low-income energy efficiency programs that can assist
users in understanding current program offerings.69 In addition, the Urban Energy Justice Lab's
Energy Efficiency Equity Baseline Map provides a comparative framework to examine the equity of
utilities' investments into low-income energy efficiency programs.70 The tool estimates equitable
utility investment proportionate to the low-income population in the service territory and as a
percentage of the total residential energy efficiency investment portfolio. Users may also wish to
69 See https://database.aceee.ora/state/guidelines-low-income-proarams.
70 See https://umich.maps.arcais.com/apps/Cascade/index.html?appid=28f6792ea2134ffba888413e70647c0c.
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consult with data sources such as University of Michigan's Energy Equity Project Report, which
provides a standardized framework for measuring energy equity, and LBNL's 2024 report
Distributional Equity Analysis for Energy Efficiency and Other Distributed Energy Resources: A
Practical Guide.7'1 This resource will provide a practical, how-to guide on designing and
implementing distributional equity analyses for energy efficiency programs.
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Key Note—Considerations for reducing energy burden: Policymakers have a number of strategies to
tackle high energy burdens for low-income households. In ESIST, energy burdens can be reduced through
energy efficiency savings and through a combination of bill pay assistance, rate reduction, and direct
payments. Beyond ratepayer funds, policymakers can also leverage additional tools such as financing and
partnerships with private entities and other state and local governments to reduce household spending
on energy. It is important to note that ESIST does not account for other strategies to reduce energy
burden, such as policies that reduce income disparities for low-income populations.
Procedure for Making Changes in ESIST
Users may use this module to examine energy efficiency programs targeted to reach low-income
households and to understand the level of investment in energy efficiency and monetary assistance
needed to overcome gaps in energy burden. As described above, users may make changes to
their program parameters (e.g., year to begin, monetary assistance, percent of households to
target annually) and unique features (e.g., low-income rate, low-income usage, cost of energy
efficiency for low-income customers) and examine how the resulting trajectories compare with their
input goals (e.g., annual spending, participation, low-income energy efficiency spending as a
percentage of residential energy efficiency spending).
Demographic Data
Users can use the Demographic Data module in ESIST to view relevant data related to the
demographic makeup of households in their selected geography. Unlike most other modules in
ESIST, the Demographic Data module does not include inputs or interactive choices by the user.
Default Assumptions
As a default, the Demographic Data module relies on data from 2022 American Community Survey
(ACS) 5-Year Estimates.72 Data are shown for the geography selected. For example, if a user
selects "US Total" as a geography, demographic data will be shown for the entire United States. If
a user selects a single state, demographic data will be shown for that state. If a user selects an
electric utility, ESIST will use data from EIA Form 861 to identify all the counites (or county-
71 See https://eneraveauitvproiect.com/wp-content/uploads/2022/08/22Q174 EEP Report 8302022.pdf and
https://emp.lbl.gov/publications/distributional-eauitv-analvsis.
72 U.S. Census Bureau. American Community Survey, 2022 American Community Survey 5-Year Estimates.
Available at https://www2.census.aov/proarams-survevs/acs/summarv file/2022/seauence-based-
SF/data/5 year by state/
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equivalents) in which that utility provides electric service to customers.73 Demographic data will
then be shown for that set of counties.
Information included in the Demographic Data module include data in the following categories:
• Ownership status: ESIST sums the number of households that are occupied by either
owners or renters.
• Race and ethnicity: ESIST sums the number of households where the householder (i.e.,
the respondent to ACS) identifies as a particular race and ethnicity (i.e., American Indian or
Alaska Native alone, Asian alone, Black alone, Hispanic, Native Hawaiian or Pacific
Islander alone, White alone, and some other race or more than one race). Note that
householders may select both Hispanic and an ACS race option.
• Structure type: ESIST sums the number of households that are single-family, multi-family,
or some other housing type (e.g., mobile home, boat, RV).
• Primary heating fuel type: ESIST sums the number of households that rely on natural
gas, electricity, or some other fuel as the primary means for heating. The number of
households without heating is also displayed.
• Household age: ESIST sums the number of households that were built in 1979 or earlier,
and the number of households that were built after 1979.74
• Housing size: ESIST sums the number of households where the structure has fewer than
four bedrooms, and the number of households where the structure has four or more
bedrooms.
• Household language: ESIST sums the number of households according to language
proficiency. Data are shown for (a) households where English is the primary language, (b)
households where English is not the primary language but where there is English
proficiency, (c) households where Spanish is the primary language and where there is
limited English proficiency, and (d) households where some language other than English or
Spanish is the primary language and where there is limited English proficiency.
Wherever possible, we display information from the ACS on low-income customers and non-low-
income customers separately.75 These data are displayed both in absolute terms (i.e., number of
households) and in percentage terms (i.e., share of households that are low-income, relative to the
total number of households in the relevant metric). In categories where absolute number of
73 In practice, this means that if a utility serves 99 percent of its customers in County X and one percent of its
customers in County Y, ESIST will include full data from both counties. This is also true in cases where multiple
electric utilities may serve customers in a single county.
74 1979 represents a useful break point for several reasons: lead paint was banned in 1978 (although it is rare in
houses built after 1959); the U.S. Department of Housing and Urban Development implemented codes for
manufactured mobile homes in 1976; spray foam began to gain wide usage in the early 1980s, changing the type
of insulation found in homes after this era; general awareness of harmful asbestos impacts took place in this era,
leading to its gradual phase-out for new building construction material around this time period; and the oil crisis
occurred in 1973-80, which sparked general shifts in thinking about energy consumption.
75 As in the Energy Burden Impacts module, the definition of "low-income" on this page refers to customers with
household incomes below 200 percent of the Federal Poverty Level. "Non-low-income" are all other customers.
Because FPL varies according to household size, we calculate this threshold for each county separately based on
average household size reported for that county.
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households by income data are unavailable, we instead rely on data from LEAD to describe the
shares of households that are low-income or not. Two categories—housing size and household
language proficiency—do not have any available data related to income levels.
Other Data Sources
Demographic data included in ESIST is intended to describe a general portrayal of the
demographics of households within the selected geography. Displayed data were chosen because
of the expected relevance and usefulness to ESIST users interested in analysis of energy
efficiency impacts on low-income or environmental justice communities. However, users may wish
to consult other data sources for more detailed information about the demographics in their region
of interest, particularly with respect to energy efficiency program implementation (rather than high-
level planning).
For example, all demographic data in ESIST come from either the ACS or the LEAD tool.76 Data
displayed on the Demographic Data module represent just a subset of the detailed information
available in either of these two sources. Users may wish to investigate data in these sources
further for more information on the demographics of their selected geography.
Users may also wish to wish to confer with EPA's EJSCREEN tool.77 This tool allows users to
access high-resolution demographic and environmental information on a specified location. A key
feature of EJSCREEN is the environmental justice indices, which combine the environmental and
demographic information in the tool. EJSCREEN includes 11 environmental indicators, six
demographic indicators, and 11 corresponding environmental justice indexes at a detailed level of
mapping not present in ESIST.
Procedure for Making Changes in ESIST
Unlike most other pages in ESIST, the Demographic Data module does not include inputs or
interactive choices required by the user. Users can look to the Other Data Sources section (above)
for more information on potential additional analyses.
Peak Demand Impacts
While most of the calculations in ESIST concern annual MWh savings, estimating the demand
impact of energy efficiency can be a key analytic question in jurisdictions. Electricity demand is the
amount of electricity required by the electrical grid at any moment in time—also sometimes referred
to as load. This demand is measured in megawatts (MW) and must be met instantaneously by an
equivalent amount of MWs of generating resources (referred to as "generating capacity" or
"capacity"). "Peak system demand" refers to the largest quantity of MW demand that a system
experiences over the course of one period of time, typically a day, season, or year. This is often
also referred to as "peak load".
For an electric grid, peak system demand may establish how many power plants the grid needs to
provide reliable electric service. Energy efficiency can be added to the grid and help decrease the
peak system demand, thereby deferring or even avoiding investment in new generating capacity.
76 See: U.S. Census Bureau. American Community Survey, 2022 American Community Survey 5-Year Estimates.
Available at https://www2.census.aov/proarams-survevs/acs/summarv file/2022/seauence-based-
SF/data/5 year by state/: LEAD tool, available at https://www.enerav.gov/scep/slsc/lead-tool.
77 For more information on EJSCREEN, see https://www.epa.gov/eiscreen.
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Policymakers implementing energy efficiency may wish to know both a portfolio's "peak savings,"
which refer to the largest instantaneous MW quantity of energy efficiency savings observed in any
hour for a given portfolio, and the "coincident savings," which refer to the MW quantity of energy
efficiency savings observed in the same hour as peak system demand. Depending on the region,
the hour when peak system demand occurs may be in the summer or the winter. Peak demand in a
summer season is usually during the hottest day of the year when air conditioner use is highest for
the season. Peak demand in the winter season often occurs during periods when it is especially
cold, and electric heaters are turned high. ESIST is able to model any type of peak demand (for
more on ESIST's default settings, see the following section "Default Assumptions").
Users may be interested in knowing an energy efficiency portfolio's peak savings or coincident
savings for a variety of reasons outside of ESIST. For example, a user may be interested in
knowing the impacts their energy efficiency program has on peak system demand for resource
planning purposes; or industrial customers may wish to learn about the impact that coincident
savings could have on the demand charge component of their electric bill.
Default Assumptions
In ESIST, users first choose how to estimate peak system demand and then how to estimate peak
savings. Users have two choices for estimating peak system demand:
• Estimate peak system demand values using a ratio of peak system demand to annual
electricity sales by selecting the Ratio option. If users choose the Ratio option, they have
two further choices. They can either:
o Enter in their own ratio by selecting User input.
o Or they can use ESIST's default setting by selecting EIA. These default values reflect
data reported for the user-selected geography to EIA Form 861.
• Enter peak system demand values manually by selecting MW input. If this is selected, the
initial set of values shown will be those generated by using the EIA selection. Users can
then modify this value.
Next, users estimate peak savings. As with estimating peak system demand, users can choose to:
• Estimate peak savings using a ratio of peak savings to annual incremental savings by
selecting Ratio. As with peak system demand, if users choose this second option, they
have two choices:
o Enter in their own ratio by selecting User input.
o Use ESIST's default setting by selecting EIA. These default values reflect data
reported for the user-selected geography to EIA Form 861. EIA does not report
whether peak savings occur during winter or summer, or whether the peaks are
coincident. ESIST assumes that savings are coincident with the higher of the two
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Key Note—Retail versus wholesale savings: In ESIST, all peak values are entered as "retail" values. Here,
"retail" values refer to the quantity of energy measured at the customer meter for which customers are
charged retail rates, rather than the quantity of energy measured at the wholesale level, which is
sometimes called "at the generator" energy. Quantities of energy measured at the wholesale level are
larger than retail values, as they have not been adjusted to exclude transmission and distribution losses.
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seasonal system peaks. Users may wish to consult local data to ensure the default
peak savings assumptions are accurate.
• Enter peak savings values manually by selecting MW input. If this is selected, the initial
set of values shown will be those generated by using the EIA selection. Users can then
modify this value.
Other Data Sources
Users may wish to consult other sources for data on both peak system demand and peak savings.
For example, utility IRPs or state energy plans may include projections for future peak system
demand, which users may either enter into ESIST (as MW values) or transform into their own MW-
per-MWh ratios. Since the MW-per-MWh ratio may vary with the mix of energy efficiency measures
being deployed in a given state, users may wish to consult their state or utility's technical reference
manual or planning documents for this factor.
Procedure for Making Changes in ESIST
Users may use this module to estimate the amount of capacity potentially avoidable by investing in
energy efficiency. ESIST translates peak savings values into avoided expenditures on conventional
generating resources and reduced costs for electricity customers. As indicated above, users may
change both peak system demand and peak savings by selecting either a Ratio approach or a MW
input approach. If users choose a MW Input approach, they may simply enter in MW quantities for
peak system demand or peak savings. If users choose a Ratio approach, they may use ESIST
default (EIA) values to estimate peak system demand from annual electricity sales and peak
savings from annual incremental savings using two different MW-per-MWh ratios, or they may
enter in their own ratio (User input).
Customer Information
ESIST's customer information module allows users to examine historical data on number of
customers, utility revenue, electricity rates, and average annual usage. Unlike most other modules
in ESIST, the Customer Information module does not include inputs or interactive choices by the
user.
Default Assumptions
As a default, the Customer Information module relies on data from ElA's Form 861 data set.
Historical data are shown for the following parameters:
• Customer count: This is the number of customers in the selected jurisdiction. A
"customer" roughly corresponds to a household or a business. As with historical sales
shown in ESIST, this only includes customers reported under "bundled" and "delivery"
service providers in EIA 861, and not "energy" service providers. Customers associated
with "energy" service providers are already counted under the reported "bundled" and
"delivery" data points.
• Total utility revenue: This is the total amount of revenue (measured in million dollars)
collected by the utility from ratepayers. This includes revenue collected by all entities listed
in EIA 861, including "energy" service providers as well as those reported under "bundled"
and "delivery" service providers. Unlike electricity sales (see below), revenue associated
with "energy" service providers is not already counted within the "bundled" and "delivery"
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data points and must be counted across all three service providers to arrive at the grand
total utility revenue.
• Sales after energy efficiency: This is the total amount of electricity sales reported by the
utility. As with other historical sales shown in ESIST, this only includes sales reported
under "bundled" and "delivery" service providers in EIA 861, and not "energy" service
providers. Sales associated with "energy" service providers are already counted under the
reported "bundled" and "delivery" data points.
• Average rates: ESIST estimates historical average rates (measured in cents per kWh) for
each historical year from by dividing total utility expenditures by the total number of retail
sales associated with the selected geographic area from EIA Form 861 (see Equation 6).
Equation 6. Calculating average electricity rates
Total utility revenue
Average electricity rate =
Sales after EE
• Year-on-year change in rates: ESIST estimates the annual average change in rate by
using the formula in Equation 6.
Equation 7. Calculating year-on-year change in rates
Average electricity rateYearN
Year on Year change in rates =
Average electricity rateY
Average annual usage: ESIST estimates the annual average usage (measured in kWh
per year) for any one customer with the formula in Equation 6. In reality, customer usage
varies. Customers with smaller homes, newer homes, fewer people per household, more
moderate climates, lower incomes, and more dependence on fossil fuels for heating tend
to use less electricity than customers in the same utility service territory with opposite
characteristics.78
Equation 8. Calculating average annual usage
Sales after EE
Average annual usage =
Customer count
Generally, rates and usage data calculated in ESIST are likely to be most useful for the residential
sector, and less useful for the commercial, industrial, or all ("Total") sectors.79 Even for the
residential sector, ESIST simplifies rates by calculating all rates in dollar-per-kWh terms (most
residential rates have at least some fixed dollar-per-month component). Non-residential bill and
rate structures are typically even more different from residential bill and rate structures, and may
78 See ElA's Residential Energy Consumption Survey at https://www.eia.gov/consumption/residential/data/2015/ for
more information on how electricity consumption varies by household characteristic.
79 In ESIST, the "residential" sector includes all residential customers, including low-income customers. This is
because data reported to EIA (the basis for much of ESIST's calculations) do not differentiate between low-
income customers and other types of residential customers. In reality, low-income customers may have different
usage patterns or rate structures than other residential customers. Users interested in assessing how rate and bill
impacts differ across different residential customers should consult with more locally specific data if they are
available.
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include more complex rate types or other types of charges not often present in residential rates.
They also tend to vary considerably from utility to utility, and they may in some cases even be
facility specific. Usage varies widely across customers as well. However, usage patterns among
residential customers are often more similar than usage patterns between residential, commercial,
and industrial customers, or even between two different commercial or industrial customers (e.g.,
the likely differences in electricity usage at a corner market compared to a big-box store).
For these reasons, users should take caution when referencing these data if they selected a sector
other than residential. We note that even for the residential sector, these numbers should be relied
on for high-level estimates only and may not be detailed enough for users interested in program-
level (rather than portfolio-level) impacts.
Other Data Sources
Users may wish to consult utility filings or IRPs for more detailed information on historical rates and
usage, or information on projected future rates. Note that this information is typically only available
for several years into the future (i.e., one to three years beyond the present day).
Procedure for Making Changes in ESIST
Unlike most other modules in ESIST, the Customer Information module does not include inputs or
interactive choices required by the user. Users can look to the Other Data Sources section for more
information on potential additional analyses.
Savings Expiration
Users can view and adjust assumptions regarding savings expiration. This refers to the concept
that, like all equipment, energy efficiency measures age (i.e., they have a "measure life") and
eventually fail and cease to produce electricity savings.80 ESIST applies the chosen savings
expiration schedules to all program years. For example, measures installed in 2020 will expire
along the same schedule as measures that are installed in 2025.
Default Assumptions
Users have six options for measure life trends, including: LBNL default, LBNL manual, Simple,
Average, Average after delay, and User input.
• The LBNL default selection is an empirical estimate of the share of the savings and
spending in an electric energy efficiency portfolio that expire each year after investment.
The analysis draws from Berkeley Lab's Cost of Saved Energy database, which tracks
program spending, savings, and lifetimes for customer-funded energy efficiency programs
that operated between 2009 and 2019.81 The spending, savings, and lifetimes provided are
those claimed by utilities in energy efficiency program filings. To focus on recent program
design and equipment efficiency, Berkeley Lab drew on data from programs that operated
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81
Expiring measures may be replaced with measures from a customer-funded energy efficiency program or by a
customer on their own. See the following section on "non-program replacement" for more on how to model
expiring measures in ESIST.
See https://emp.lbl.aov/proiects/what-it-costs-save-enerav for more information. A technical brief describing the
lifetime assumptions used in ESIST is available at https://emp.lbl.aov/publications/enerav-efficiencv-lifetimes-how.
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between 2014 and 2019.82 For each reported program lifetime from one to 30 years,
Berkeley Lab calculated its share of reported savings and spending relative to all reported
program savings and spending.83 Berkeley Lab provided these shares for a total portfolio
and also by program sector: Residential, Commercial & Industrial, Low-Income, and Cross-
Cutting (i.e., programs that are not specific to either of the other sectors). Under the "LBNL
Default" selection, ESIST will automatically select the measure expiration schedule that is
linked to the selected sector.
Users should be aware that efficiency standards and program designs will likely evolve
over the time frame of their analysis. Berkeley Lab's values are drawn from claimed
impacts in the recent past and do not account for these potential changes. Utilities or
program administrators also make different assumptions about savings and lifetimes. The
data presented are national, and do not reflect any location-specific factors that might
affect measure lifetimes. There is no guarantee that these lifetimes and savings
distributions overtime will be realized in specific field settings.
Berkeley Lab also calculated typical lifetimes for common program types, which are
summarized in Table 4. Note that these lifetimes are program-level averages, as reported
by utilities or program administrators, and reflect a mix of individual measure lifetimes in a
given program. The values, therefore, are not integers (e.g., 10 years), as may be
expected for a single measure. Program average measure lifetimes vary both across and
within measure categories because of differences in installed measures and engineering
estimates of measure lifetimes. These lifetimes may be useful for users seeking to model
specific programs, rather than typical portfolios.84
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82 The database includes more than 7,500 program years of data for the years 2014-2019 from investor-owned
utilities, third party administrators (e.g., Efficiency Vermont), and state administrators (e.g., NYSERDA). Not all
utilities and program administrators report lifetimes, so the final sample size for this analysis was about 2,900
program years from 32 states and 71 utilities and program administrators.
83 In rare cases, utilities and program administrators will report lifetimes beyond 30 years. Berkeley Lab excludes
these lifetimes as they do not correspond with prevailing estimates of efficiency measure lifetimes.
84 For more detail on the definitions of these program categories, please refer to Berkeley Lab's energy efficiency
program typology at https://emp.lbl.aov/publications/enerav-efficiencv-proaram-tvpoloav.
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Table 4. Measure lifetimes of different program categories, according to Berkeley Lab's Cost of Saved
Energy database
25th 50th Percentile 75th
Sector Program Category Percentile (Median) Percentile
Commercial &
Industrial
Commercial-MUSH
(Municipal/University/Schools/Hospitals)
9.6
11.8
14.0
Commercial & Industrial Custom
12.0
14.2
15.8
Small Commercial Prescriptive
10.6
12.1
13.1
Commercial Lighting
9.1
12.8
15.3
Residential
Residential Lighting
7.9
10.0
13.8
Residential HVAC (Heating, Ventilation,
and Air Conditioning)
11.0
14.9
18.0
Whole Home Retrofit
11.2
14.2
16.5
Residential Behavioral
1.0
1.0
2.0
Low-Income
Low-income
9.3
13.4
16.4
• Under the LBNL manual selection, users may select a specific sector from among those
described in Table 4. Users may select All Programs, Commercial & Industrial, Residential,
Low-Income, and Residential and Low-Income. This setting is intended for users who wish
to rely on the Berkeley Lab data but exercise a greater degree of control on what values
are used.
• Under the Simple selection, measures expire entirely as of a specified year. This setting is
intended for users interested in modeling the portfolio of savings as if it "turns off in a
single future year. Users must set a "measure life" that tells ESIST when this should occur.
For example, if users input "10" for a measure life, 100 percent of savings will expire in the
10th year after installation. Users may enter any measure life greater than zero years and
less than 100 years.
• Under the Average selection, users set an average measure life. The default is 11.0 years,
based on the weighted average measure life observed in the EIA Form 861 data. This
setting is intended for users interested in analyzing a savings portfolio composed of a static
measure mix and therefore stable measure-life assumptions overtime. Users may enter
any measure life greater than zero years and less than 50 years. In ESIST, the "average"
measure life refers to the length of time that it takes the average measure within the
portfolio to expire. Mathematically, this implies that the number of years necessary for all
measures in the portfolio to be completely expired is two times the measure life. Under this
selection, savings expire in equal quantities, as calculated in Equation 9.
Equation 9. Annual fraction of expired savings
Annual fraction of expired savings
= 100 percent / (Average measure life x 2)
• Under the Average after delay selection, users set an average measure life and a delay
parameter. Under this selection, savings will persist at their full amount through the number
of delay years. After the delay, savings will begin to expire according to the methodology
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described for the Average selection, above. This setting is intended for users interested in
analyzing a savings portfolio where savings are expected to persist at or near 100 percent
levels for some time, before decaying.
• Under the User input selection, the row labeled "% of measures expiring each year" turns
blue, indicating to users that they may overwrite values in these cells. This setting is
intended for users who wish to customize the savings expiration in each year of the study
period. Users may enter any value between zero and 100 percent. A value of 100 percent
implies that all measures will expire in a given year, while a value of zero indicates that no
measures expire in that year. These values may vary widely depending on the type of
energy efficiency measure or portfolio; users should confer with state or utility technical
reference manuals or energy efficiency plans for more information.
Users may also specify a percentage for Non-program replacement. This describes the
percentage of measures that are replaced by customers on their own, rather than through
customer-funded energy efficiency programs. Some customers may wish to replace expiring
measures with a similar—or better—measure without participating in an energy efficiency program.
There are different reasons why a user may want to model this phenomenon: federal or state
standards for lighting, appliances, or building codes may change; reductions in technology costs
may drop faster than expected; or customer preferences may change. To represent how the user
foresees customers replacing energy efficiency in the tool, users may enter any value from zero to
100 percent in the "Non-program replacement" cell of ESIST. ESIST uses a default value of zero
percent, effectively assuming that all measures are only replaced through energy efficiency
programs.
Other Data Sources
Users may wish to consult energy efficiency lifetimes present in their state or utility's technical
reference manual or planning documents. Note that lifetimes vary by energy efficiency measure
(e.g., an efficient lightbulb does not typically have the same measure life as an efficient motor), and
a portfolio comprises many measures installed in each year.
Procedure for Making Changes in ESIST
Users may select either Simple or User input to change the measure expiration from the default
assumption. Users should select Simple if they have a generic lifetime for the entire portfolio. They
should select User input if they have specific information about how much of the portfolio savings
are estimated to expire in each future year. Users should enter their new information in the blue
highlighted cells that appear.
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Step 7: Review Outputs
Reviewing Outputs
After progressing through the core part of ESIST and making any desired changes to the optional
assumptions, the user may review outputs. These are categorized and organized on a single page.
ESIST organizes outputs in the following categories:
• Sales and Savings
• Costs
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• Emissions Impacts
• Public Health Impacts
• Energy Burden Impacts
• Wholesale Peak Impacts
These output categories in ESIST provide a comprehensive set of the outputs likely to be most
useful to most users' analyses.
Charts
Each section on the outputs sheet features a green button stating "Click here to view charts." Each
one of these links brings users to a separate section on a charts sheet. Each section on the charts
sheet includes a relevant set of charts (see Figure 8 for an example). Table 5 provides a list of
each chart type, along with a brief description. Note that all charts are time-series charts, displaying
continuous values from 2010-2045.
Figure 8. Example of figure on ESIST's "Charts" page
<-> z—
200,000,000
180,000,000
160,000,000
140,000,000
120,000,000
100,000,000
80,000,000
60,000,000
40,000,000
20,000,000
0
2010
2015
2020
2025
•Baseline sales
Sales after EE
2030
2035
2040
2045
Table 5. List of charts in ESIST
Section
Chart Name
Description
Figure 1. Electricity Sales Before and
after Energy Efficiency (EE)
Electricity sales trajectories, both before and after
the cumulative impacts of energy efficiency.
Savings and
Sales
Figure 2. Incremental Savings
Annual incremental energy efficiency, displayed in
terms of MWh.
Figure 3. Cumulative Savings
Cumulative energy efficiency, displayed in terms of
MWh.
Costs
Figure 4. Annual Costs
Annual first-year costs of energy efficiency; values
are displayed both for the utility cost and the
participant cost.
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Chart Name
Description
Figure 5. Annual Utility First-Year
Cost
Annual first-year unit cost of energy efficiency to
utilities, measured in cents per kWh.
Figure 6. Avoided C02 Emissions
Avoided C02 emissions. Displayed below this figure
is the total avoided C02 emissions from 2010-
2045.
Emissions
Figure 7. Avoided PM2.5 Emissions
Avoided PM2 emissions. Displayed below this
figure is the total avoided PM2emissions from
2010-2045.
Impacts
Figure 8. Avoided S02 Emissions
Avoided S02 emissions. Displayed below this figure
is the total avoided S02 emissions from 2010-
2045.
Figure 9. Avoided NOx Emissions
Avoided NOx emissions. Displayed below this
figure is the total avoided NOx emissions from
2010-2045.
Public Health
Impacts
Figure 10. Public Health impacts
Annual monetized public health benefits; values
are displayed both for the PM benefits and ozone
benefits. Below this figure is shown the cumulative
total health benefits.
Figure 11. Cumulative Low-Income
Savings, Compared to Residential
Cumulative savings (in MWh) calculated for low-
income customers in the Energy Burden Impacts
module, compared to cumulative savings for
residential customers (calculated in the main body
of the tool).
Energy
Burden
Impacts
Figure 12. Low-Income and
Residential EE Spending
Annual spending (in million dollars) calculated for
low-income customers in the Energy Burden
Impacts module, compared to annual spending
for residential customers (calculated in the main
body of the tool). A separate series is shown for
total spending from both low-income energy
efficiency programs and low-income monetary
assistance.
Figure 13. Low-Income EE Program
Spending Relative to Residential
Annual spending (in %) calculated for low-income
customers in the Energy Burden Impacts module
divided by annual spending for residential
customers (calculated in the main body of the
tool).
Figure 14. Low-Income Participation
Annual low-income households (in %) calculated in
the Energy Burden Impacts module.
Figure 15. Low-Income Electricity
Burden and Energy Burden
Electricity burden (%) is equal to the spending on
electricity for average low-income households,
divided by typical low-income household income.
Energy burden (%) is equal to the spending on all
household energy for average low-income
households, divided by typical low-income
household income. Point values are also shown for
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Chart Name
Description
electricity burden and energy burden for non-low-
income customers.
Wholesale
Figure 16. Wholesale Peak Demand
and Avoided Peak
Peak demand trajectories, both before and after
the cumulative impacts of energy efficiency.
Peak Impacts
Figure 17. Cumulative Wholesale
Peak Savings
Cumulative impacts of energy efficiency, displayed
in terms of peak MW.
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Appendix A: Data Sources
All data sources and equations can be found referenced throughout the user manual and ESIST
workbook's "Library" worksheet tab. The Library tab within ESIST contains information on electricity
sales, T&D losses, savings expiration trajectories, emissions factors, inflation factors, topologies,
and resource costs.
The primary data source for electricity sales, energy efficiency savings, peak demand, and other
utility-specific data is EIA Form 861, data set vintages 2010-2022.85 EIA Form 861 is an annually
produced set of Excel workbooks that provides utility-reported data on sales, savings, peak
demand, customer counts, utility revenue, and balancing authorities, among other data points.
Data on projected electricity sales growth rates, fuel prices, and plant operating costs are retrieved
from the 2023 edition of ElA's AEO.86 AEO provides modeled projections of domestic energy
markets through the middle of the 21st century, incorporating assumptions regarding
macroeconomic growth, world oil prices, technological progress, and currently implemented energy
policies.
Data on the first-year cost of saved energy are from two analyses by Berkeley Lab.87
Default data on measure expiration are from Berkeley Lab's Cost of Saved Energy database, which
tracks program spending, savings, and lifetimes for customer-funded energy efficiency programs
that operated between 2009-2020.88
Default data on historical emissions rates and T&D losses are from EPA's AVERT.89 AVERT
calculates avoided emissions for 14 different regions of the country.90 Each state (or utility in a
state) is assigned to an AVERT region and uses the avoided emissions rates associated with the
region.91
Default data on future emission rates are estimated using the IPM power sector model.92
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85 EIA Form 861 is available at https://www.eia.gov/electricitv/data/eia861/.
86 AEO 2023 is available at https://www.eia.gov/outlooks/aeo/.
87 Lawrence Berkeley National Laboratory (LBNL). "The Cost of Saving Electricity Through Energy Efficiency
Programs Funded by Utility Customers: 2009-2015." (2018). Available at https://eta-
publications.lbl.gov/sites/default/files/cose final report 20200429.pdf and Lawrence Berkeley National Laboratory
(LBNL). "Still the One: Efficiency Remains a Cost-Effective Electricity Resource." (2021). Available at
https://eta.lbl.gov/publications/still-one-efficiencv-remains-cost.
88 See https://emp.lbl.gov/proiects/what-it-costs-save-energy for more information.
89 More information on AVERT is available at https://www.epa.gov/avert.
90 Note that older versions of AVERT (version 2.3 and earlier) use a 10-region topology. These earlier data are used
as a basis for emission rates in ESIST for 2010-2016. The 14-region topology from AVERT version 3.0 and newer
is used for 2017-2023.
91 For the default values in ESIST, avoided emissions rates are calculated in AVERT assuming a 0.5 percent
reduction in load for baseload energy efficiency. Note that these are gross emissions rates (i.e., they have not
been increased to reflect T&D losses). The values for the United States as a whole are calculated using a
weighted average based on sales.
92 U.S. EPA. "Documentation for EPA's Power Sector Modeling Platform 2023 Using IPM." (April 2024). Available at
https://www.epa.gov/power-sector-modeling/documentation-2023-reference-case.
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Default data on energy burden are from Department of Energy's LEAD Tool.93 The LEAD Tool was
created to help states, communities, and other stakeholders create better energy strategies and
programs by improving their understanding of low-income housing and energy characteristics. Data
in the LEAD Tool come from the U.S. Census Bureau's American Community Survey 2018 Public
Use Microdata Samples.
All data related to demographics are from the U.S. Census Bureau.94
Data on annual inflation are retrieved from the Federal Reserve Bank of St. Louis.95
Key data sources for the health impacts module of ESIST include:
• U.S. EPA. Technical Support Document Estimating the Benefit per Ton of Reducing
Directly-Emitted PM2.5, PM2.5 Precursors and Ozone Precursors from 21 Sectors;
Research Triangle Park, NC, November 2021.
https://www.epa.gov/svstem/files/documents/2021-10/source-apportionment-tsd-oct-
2021 O.pdf.
• U.S. EPA. Technical Support Document (TSD) for the Final Revised Cross-State Air
Pollution Rule Update for the 2008 Ozone Season NAAQS Estimating PM2 5-and Ozone-
Attributable Health Benefits. (2021). Research Triangle Park, NC, 2021.
• Fann, N.; Baker, K. R.; Fulcher, C. M. Characterizing the PM2 5-Related Health Benefits of
Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors across the U.S.
(2012). Environment International, 49, 141-151. Available at
https://doi.ora/10.1016/i.envint.2012.08.017.
• Wolfe, P.; Davidson, K.; Fulcher, C.; Fann, N.; Zawacki, M.; Baker, K. R. Monetized Health
Benefits Attributable to Mobile Source Emission Reductions across the United States in
2025. (2019). Science of The Total Environment, 650, 2490-2498. Available at
httPs://doi.ora/10.1016/J.SCITOTENV.2Q18.09.273.
• Sacks, J. D.; Lloyd, J. M.; Zhu, Y.; Anderton, J.; Jang, C. J.; Hubbell, B.; Fann, N. The
Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-
CE): A Tool to Estimate the Health and Economic Benefits of Reducing Air Pollution.
(2018). Environmental Modelling & Software, 104. Available at
https://doi.ora/10.1016/i.envsoft.2018.02.009.
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93 For more on the LEAD tool, see https://www.enerav.gov/scep/slsc/lead-tool.
94 U.S. Census Bureau. American Community Survey, 2022 American Community Survey 5-Year Estimates.
Available at https://www2.census.aov/proarams-survevs/acs/summarv file/2022/seauence-based-
SF/data/5 year by state/
95 Available at https://fred.stlouisfed.ora/series/GDPCTPI/downloaddata.
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Appendix B: ESIST Use Cases
The following three "use cases" provide illustrative examples of how different users may apply
ESIST in practice. These examples are not exhaustive or intended to limit ESIST's potential
applications. Potential users of ESIST include:
• Energy office staff and NGOs.
• State air regulators.
• PUCs, PUC staff, investor-owned utilities, municipal utilities, or utility cooperatives.
In all cases, users should take care to understand, assess, and make appropriate adjustments to
default assumptions and calculations, consistent with local requirements, circumstances, and
expectations. In addition, these users are likely to incorporate local utility-specific data and use
state-specific target types or methodologies for calculating savings and associated costs. Note that
specific caveats and other appropriate usage information can be found in "Key Notes" text boxes
throughout this document.
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Illustrative Use Case 1: Establish and Support Energy Efficiency
Targets
Users and user goals: These users are interested in advancing energy efficiency programs as a
strategy for reducing electricity demand and achieving energy efficiency savings targets such as
EERS policies.96 These users may also be interested in evaluating the ability of energy efficiency
programs to meet objectives related to low-income customers or environmental justice
communities. These users may include state energy officials, NGOs, and other electricity sector
stakeholders. Applicable energy efficiency targets might be established in regulation or law, or
alternatively framed within a state- or local-level energy plan or a utility IRP. ESIST can help users
explore different scenarios for achieving EERS policies or other energy efficiency targets (e.g.,
sustaining current levels of savings, doubling current levels of savings, halving current levels of
savings) and understand the impacts of these savings on future electricity sales.
In addition, these users may be interested in understanding how the portfolio of existing and new
energy efficiency programs contribute to related state goals (e.g., air quality improvements,
customer cost impacts). The modules in Step 6 of ESIST quantify metrics of interest, allowing
users to quickly explore the potential range of scenarios. Users can assess demographics and
programs that can help better target savings to reach underserved communities. For users with
expertise in applying advanced sector tools (e.g., capacity expansion models, production dispatch
models, air quality and health impact models), ESIST may serve as a source of key input values for
sales trajectories and program costs.
96 Every U.S. state currently administers some type of demand-side energy efficiency program, while about half have
adopted a statewide EERS policy and others require utilities to deliver all cost-effective energy efficiency. The
resulting energy efficiency savings for homeowners, businesses, and other electricity customers can serve as an
"electricity system resource" on par with power plant generation that states, utilities, and electricity system
planners can use to achieve various objectives. These EERS policies may include achieving annual energy
efficiency savings equal to a specified percentage of total electricity sales, or meeting forecasted electricity
demand over a defined geographic area (i.e., a utility service territory) within a specified timeframe.
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Approach: Users can leverage ESIST to examine scenarios and explore varying levels of energy
efficiency implementation and savings. For example, an ESIST user may closely examine specific
utilities or utility types within their jurisdiction, as well as create different savings goals for municipal
and investor-owned utilities. Users may also develop different savings goals for the residential,
commercial, and industrial sectors, or evaluate impacts on electricity and energy burdens for
programs targeted to reach low-income customers. Users examining a proposed change to an
existing target, such as an EERS policy, start by entering the existing energy efficiency savings
requirements. If savings as a percent of sales is appropriate, the user enters appropriate values for
the duration of the study period (e.g., one percent of sales for 2023-2025). The next step is to save
this Excel workbook, open a new version of ESIST, and then run ESIST again with the proposed
change, allowing the user to compare the savings and cost impacts of the existing and proposed
energy efficiency trajectories (see page 12 for more information on performing scenario analysis in
ESIST). Regardless of the scenario evaluated in ESIST, users are encouraged to confirm that
historical savings data used in the tool were reported correctly to EIA (e.g., savings reported by a
utility in the state match the EIA values within a reasonable margin).
Outputs of interest: The outputs of interest for these users may include energy savings, costs,
emissions, energy burden impacts, and demographic data. In a state energy plan, for example, the
governor may be particularly interested in energy burden impacts and costs to customers. ESIST
can be used to estimate the energy burden impacts associated with implemented programs
targeted to reach low-income customers, and it can be used to contextualize savings and costs
relative to recent historical consumption, utility revenue, and electricity rates. These outputs can be
used to explain to the public the importance of energy efficiency programs. With ESIST, a wide
range of outputs can be analyzed and reported together in one place, which can facilitate
transparent evaluation and stakeholder discussion.
Illustrative Use Case 2: Establish or Review the Role of Energy
Efficiency in Pollution Reduction Plans
Users and user goals: Energy efficiency can reduce criteria pollutant emissions and improve public
health by avoiding generation at fossil fuel-fired power plants. State and local air quality officials,
who are required to plan for and implement measures to meet federal air quality standards, can
use ESIST to help evaluate opportunities for energy efficiency policies to reduce emissions, create
public health benefits, and meet federal regulatory obligations under the Clean Air Act. For
example, ESIST could be used to help a state seek compliance credit for efficiency-related
emission reductions in a state implementation plan for the National Ambient Air Quality Standards.
ESIST energy savings outputs may also be used in conjunction with other models to translate
emissions reductions into public health improvements.
Approach: Beginning with inputs that describe utility energy efficiency policies, ESIST estimates the
impact of those policies on reducing generation within the region. The tool then combines those
results with default data from EPA sources to estimate related reductions in criteria pollutant
emissions from power plants and the impact of those emission reductions on public health.
Because ESIST can evaluate energy efficiency scenarios over a wide range of years, the tool can
quickly determine screening level estimates of long-term emissions and public health impacts for a
range of scenarios. ESIST's energy savings outputs can also be exported to other modeling tools
to refine and explore air quality and public health benefits estimates.
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Outputs of interest: The avoided emissions outputs of ESIST provide a screening level estimate of
the emissions reductions that the energy savings from utility efficiency programs can provide, core
precise and locational emissions estimates, which can be used to seek compliance credit to meet
the National Ambient Air Quality Standards and other obligations under the Clean Air Act, may be
obtained by integrating ESIST's MWh outputs into AVERT (or another emissions model). These
emissions reductions can be analyzed either within ESIST to estimate public health impacts or can
be used in another model (e.g., COBRA) for further analysis.
Illustrative Use Case 3: Establish or Review Energy Efficiency
Plans
Users and user goals: These users are compiling or reviewing electric energy efficiency plans filed
with PUCs by utility or third-party program administrators. These users may include PUCs, PUC
staff, utilities and municipal utilities, or cooperatives, as well as other stakeholders.
In addition to evaluating the benefit-cost ratios of specific energy efficiency plans, stakeholders who
review and comment on these plans often provide perspective on the broader energy planning
landscape in a state. These users may be interested in the costs and benefits of alternate energy
efficiency scenarios and how these scenarios compare to current and historical efforts. These
users may also be interested in how energy efficiency plans meet various objectives related to low-
income customers, such as customers served, share of funding dedicated to low-income
customers, or energy burden impacts.
Approach: Using ESIST, users can compare a single utility's proposed energy efficiency plan with
that utility's own historical energy efficiency savings. Users can also compare the proposed energy
efficiency plan to planned or historical energy efficiency savings for other utilities in other states.
Users can customize ESIST inputs by replacing its default assumptions with other sources of data
as available from utility filings or through discovery. This customization allows the modeled
scenarios to more accurately reflect local characteristics and programs. Customized values may
include utility-specific values on baseline sales projections, estimated first-year costs of energy
efficiency, peak savings, or utility-specific plans for low-income programs.
Outputs and scenarios generated from ESIST can facilitate discussion among members of
collaborative organizations, and between stakeholders and utility commissions. Users can save
multiple copies of ESIST and share impacts by distributing outputs to stakeholders. Scenarios can
be developed and shared—with clearly documented input assumptions and parameters—within a
single, transparent, well-documented Excel workbook.
Outputs of interest: Users engaged in the evaluation of an energy efficiency plan may be most
interested in costs, savings, and electricity sales. ESIST may also serve as a useful platform for
generating multiple benefits, including avoided emissions and energy burden impacts. Because
ESIST outputs are aggregated across two sheets (graphically and in tabular format), they can be
readily shared or displayed in presentations, reports, or testimony.
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Appendix C: ESIST: Pilot Gas Version
ESIST focuses primarily on energy use and energy efficiency savings in the electricity sector. EPA
is issuing a standalone pilot version of ESIST focused on natural gas consumption in the residential
and commercial sectors to allow users to estimate natural gas savings from energy efficiency or
other gas-saving measures. The methodology and functionality of this model is largely identical to
the electricity-focused ESIST. The below sections highlight some of the major differences in
sources used and calculations performed.
As with the electricity-focused ESIST, users of the ESIST: Pilot Gas Version 1.2 should be aware
of the effects of electrification measures on gas consumption. For example, gas consumption
projections made in light of ambitious electrification policies are likely to be lower than gas
consumption projections made without these policies.
Default Data Sources
The pilot gas version of ESIST relies on the following data sources for default data:
• Historical natural gas consumption: Data on historical consumption of natural gas have
been obtained from EIA Form 176.97 This data set is updated each year and contains
detailed, sector-specific natural gas consumption data for thousands of utilities across the
United States. For the purposes of the pilot gas version of ESIST, we have retrieved
"sales" and "transport" data for all utilities in the residential and commercial sectors.98
• Historical gas energy efficiency savings, spending, and measure lifetimes: Data on
historical gas energy efficiency savings, spending, and measure lifetimes were provided to
EPA from Berkeley Lab. These data were compiled by Berkeley Lab through its Cost of
Saved Energy Database and span the years 2012-2017.99 We note the following about
this data set:
o Definitions: Reported savings are 'ex-ante' and for the most part, found in regulatory
filings.100 For some utilities and program administrators, evaluation results and net-to-
gross ratios are used to arrive at an 'ex-ante' number. Reported utility spending
includes the cost of administering, marketing, and evaluating programs, as well as
incentives to customers. It excludes program participant costs and any performance
incentives achieved through the implementation of the efficiency portfolio. The program
97 For more information, see
https://www.eia.aov/naturalaas/naas/#?report=RP1 &vear1 =&vear2=&companv=&sortbv=&items=.
98 In EIA Form 176, "sales" refer to the quantity of gas that is sold directly to an end user. "Transported" gas is gas
that is indirectly sold to the end user via some third party. Other sectors in EIA Form 176 not included in the pilot
gas version of ESIST include the industrial, electric power, and vehicle sectors.
99 For more information on this data set, see Berkeley Lab's May 2020 report Cost of Saving Natural Gas Through
Efficiency Programs Funded by Utility Customers: 2012-2017, available at https://eta-
publications.lbl.gov/sites/default/files/cose natural gas final report 20200513.pdf.
100 "Ex ante" savings are projected savings, rather than evaluated savings. As in the electric ESIST, some of the
selectable organizations in the pilot gas version of ESIST have reported historical savings and spending, but no
reported historical gas consumption.
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average measure lifetime is the savings-weighted average of program lifetimes in each
utility or program administrator's portfolio for a given year.
o Data coverage: This data collection does not include all investor-owned utilities with
gas efficiency programs in the United States. Instead, this data collection covers 50
investor-owned utilities and two third-party administrators in 21 states for the period
2012-2017. Depending on the year, these program administrators account for 50-70
percent of national gas efficiency spending (relative to the Consortium for Energy
Efficiency's estimates of national spending). In instances where utilities report data for
some years but not others (e.g., data is reported for 2012-2016 but not 2017), the pilot
gas version of ESIST automatically extends the last existing data point through the
modeled historical period. This applies to periods where Berkeley Lab has compiled
data (e.g., 2012-2017), as well as other years the pilot gas version of ESIST treats as
the past (such as 2018-2021). For the utilities that have no reported savings, the pilot
gas version of ESIST does not apply any "gap filling" step; users modeling
geographies with no reported savings or spending should take care to ensure that
these values are truly zero, rather than non-zero and not collected by Berkeley Lab.
• Projected gas consumption: As in the electricity-focused version of ESIST, future
projections of natural gas consumption are based on data from AEO 2023.
• Utility cost of energy efficiency: As a default, the pilot gas version ESIST provides first-
year utility cost of energy efficiency data for the U.S. as a whole and for four regions
(Midwest, Northeast, South, and West) from Berkeley Lab's 2020 paper Cost of Saving
Natural Gas Through Efficiency Programs Funded by Utility Customers: 2012-2017.101
• Utility share of energy efficiency costs: No data on the split of costs between utilities
and program participants are currently available for gas energy efficiency measures. As a
result, the pilot gas version of ESIST uses the same default used in the electricity-focused
ESIST (a derivation of data from Berkeley Lab's 2018 paper The Cost of Saving Electricity
Through Energy Efficiency Programs Funded by Utility Customers: 2009-2015).102
• Emissions impacts: Data on emission factors associated with natural gas consumption
are based on information in EPA's AP-42 and the 2017 National Emissions Inventory.103
Users may wish to use the COBRA model to assess public health impacts related to
emission reductions from lower gas consumption.104
• Energy burden impacts: Users may assess energy burden impacts similar to that
described above in Step 6 (see page 38). Energy burden calculations are performed on the
basis of expenditures on natural gas, relative to household income. As in the electricity-
focused ESIST, this analysis is performed for the residential sector only (although other
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101 For more information, see https://eta-
publications.lbl.gov/sites/default/files/cose natural gas final report 20200513.pdf.
102 For more information, see https://eta-publications.lbl.gov/sites/default/files/cose final report 20200429.pdf.
103 See https://www3.epa.gov/ttnchie1/ap42/ch01/final/c01s04.pdf (Table 1.4-2) and https://www.epa.gov/air-
emissions-inventories/2017-national-emissions-inventorv-nei-technical-support-document-tsd.
104 For more information, see https://www.epa.gov/cobra.
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parts of the tool combine commercial and residential data, the Energy Burden Impacts
module pulls data for residential customers only).
• Customer information: Users may examine historical customer information similar to that
described in Step 6 (see page 47). Because of data availability, information is presented
for the residential sector only.
• Savings expiration: No data on measure expiration are currently available for gas energy
efficiency measures. As a result, the pilot gas version of ESIST uses the same default
used in the electricity-focused ESIST (a derivation of data from Berkeley Lab).105
Functionality Limited or Excluded in ESIST: Pilot Gas Version 1.2
The following paragraphs describe the functionality that is present in the electricity-focused version
of ESIST but is excluded from the pilot gas version.
• Study area: Users may currently model the U.S. as a whole, individual states, or individual
utilities. The pilot gas version of ESIST is not yet able to model groups of utilities or sectors
within utilities.
• Other settings: In the pilot gas version ESIST, users can modify parameters relating to
measure expiration and emissions. Modules that are not currently included in the pilot gas
version of ESIST include Peak Demand Impacts, Public Health Impacts, and Demographic
Data. These modules are not included due to current gaps in data.
• Outputs: Broadly speaking, the pilot gas version has the same outputs as the electricity-
focused ESIST, except for the outputs related to the modules excluded from the pilot gas
version of ESIST: public health impacts and wholesale peak impacts. Otherwise, outputs
on energy sales and savings, costs, emissions, and energy burden impacts are shown in
the same format or an analogous format to the electricity-focused ESIST.
105 See https://emp.lbl.gov/publications/enerav-efficiencv-lifetimes-how for more information.
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Energy Savings and
Impacts Scenario Tool
&EPA
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