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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Low-Income Energy Affordability Data (LEAD) Tool

The Low-income Energy Affordability Data (LEAD) tool was created to provide data, such as energy burden, to stakeholders to make data driven decisions. The LEAD Tool provides a starting point for local governments, NGOs, companies, etc. to find areas with higher energy burden and cost to inform decisions about funding distribution and program qualifications. This presentation was given to energy efficiency and conservation block grant program applicants to explain how this tool can help them develop their energy efficiency and conservation strategy.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Low-Income Energy Affordability Data - LEAD Tool - 2018 Update

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

affordability↗

DOE OSTI2024

Low-Income Energy Affordability Data - LEAD Tool - 2022 Update

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

AMI↗

DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Data Set and Analysis to Inform Equitable Investment Prioritization in the United States

community energy planning↗

DOE OSTI2020

National Grid: Using the LEAD Tool Data to Target Energy Affordability Services to Eligible Customers in New York

In the following use case, investor-owned utility company, National Grid, used the Low-Income Energy Affordability Data (LEAD) Tool to support the implementation of its Home Energy Affordability Team (HEAT), a weatherization program for income-eligible residential natural gas customers in its service territory in Long Island, New York. Using LEAD Tool data, National Grid was able to determine how many households may be eligible for the program to inform additional marketing efforts. This use case is an example of how utilities of all sizes and types in the United States can use the LEAD Tool in similar ways to gain insights and better address customer energy affordability needs.

Low-Income Energy Affordability Data Tool, LEAD To↗

DOE OSTI2020

Kentucky: Using LEAD Tool Data to Fund Energy Efficiency Programs

The Kentucky Office of Energy Policy (OEP) applied the U.S. Department of Energy’s (DOE’s) web-based Low-Income Energy Affordability Data (LEAD) Tool to identify areas of their state with energy affordability needs. Kentucky OEP staff used the LEAD Tool to identify counties in Kentucky with the highest energy burden, which is the percentage of household income spent on energy. With this knowledge, Kentucky OEP staff allocated funds to relevant nonprofit organizations that provide home repairs, weatherization upgrades, and other solutions in areas where these services have the highest potential energy saving benefits and can reduce energy burden.

Low-Income Energy Affordability Data Tool, LEAD To↗

DOE OSTI2024

Household Energy Burden in Beacon Hill, Seattle, WA [Slides]

This technical assistance is part of the Communities Local Energy Action Program (CLEAP) for the Beacon Hill, Seattle, Washington community coalition consisting of El Centro de la Raza, Beacon Hill Council and Bethany United Church of Christ with supportive partners, Seattle City Light and the Seattle Office of Sustainability and Environment. This report uses the Low-Income Energy Affordability Data (LEAD) Tool to identify the most energy burdened households by income group, building type, building age and fuel type.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

City Decision Analysis Resources and Tools

The City Decision Analysis at Any Scale Workshop was held February 17-18, 2021. This brochure was created to provide participants with a collection of tools and resources that support planning and implementation of clean energy goals for communities and businesses.

ACES↗

DOE OSTI2026

State Technical Assistance - New Mexico Energy and Conservation Management Division Report [Slides]

The New Mexico Energy and Conservation Management Division (ECMD) sought technical assistance to enhance their ability to evaluate program impacts using the Low-Income Energy Affordability Data (LEAD) tool. NLR assisted ECMD in leveraging the LEAD tool to calculate and analyze energy burden across electric utility service areas, enabling them to assess program outcomes more effectively. To meet ECMD's goals, NLR developed a customized methodology to calculate utility-specific energy burden metrics using census tract data and available utility service area information from the Energy Information Administration (EIA). While acknowledging some limitations in the EIA dataset, NLR estimated the percentage of households within each service territory and incorporated relevant filters such as income, housing characteristics, and other demographics from the LEAD tool. The analysis provided ECMD with a new capability to evaluate program success based on energy savings, reductions in energy burden, and other performance indicators. The data and methodology also support discussions with utilities to improve the accuracy of service territory datasets. ECMD can use the outputs to track program effectiveness and plan future initiatives. NLR offered the possibility of follow-on work, including capacity-building for ECMD to repeat the analysis independently and the option to refine the analysis with updated service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Household Energy Burden in Lawrence, Massachusetts [Slides]

The City of Lawrence consists of 18 census tracts, of those, 9 census tracts have an average energy burden (the percent of income spent on energy bills) of 6% or greater. The Lawrence Stakeholders Coalition's (LSC) main goal is to "Reduce energy burden and create well-paying local jobs and businesses by increasing the distribution and use of sustainable technologies such as heat pumps, community and rooftop solar, and weatherization." As part of that goal, the LSC is interested in understanding Lawrence's pathway to electrification, specifically through the building sector. This technical assistance aims to assist the LSC's electrification and energy burden reduction planning by: 1) Identifying the most energy-burdened households by owner-occupied and renter-occupied housing status; 2) Identifying and quantifying the characteristics of the most energy-burdened housing units by housing type, age, and heating fuel type; 3) Identifying the tenure and housing types of the most energy-burdened and prevalent households for subsequent ResStock analysis of cost-effective efficiency upgrades.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Intersections of Disadvantaged Communities and Renewable Energy Potential: Analyses to Inform Equitable Investment Prioritization

Renewable energy development can bolster local economies through job creation, local tax revenues, and reduced energy costs; however, communities most in need of economic development and employment opportunities often see lower levels of renewable energy deployment. Megan Day and Liz Ross, along with their co-authors and supported by NREL's Sustainable Communities Catalyzer, identified areas where disadvantaged community indicators intersect with high potential for renewable energy deployment. Through a geospatial intersection of energy burden, environmental hazard, and sociodemographic data with the technical generation potential and levelized cost of energy for multiple renewable energy technologies, we identified trends across disadvantaged community indicators and renewable energy deployment potential. Combining metrics across several tools, including the State and Local Planning for Energy (SLOPE) platform, the Low-Income Energy Affordability (LEAD) tool, and the Environmental Justice Screening and Mapping (EJSCREEN) tool, we compiled a dataset that can be used to inform national- and state-level energy-related assistance programs, economic development efforts, and infrastructure programs seeking to prioritize investments in disadvantage communities.

catalyzer↗

DOE OSTI2024

Screening Tool for Equitable Adoption and Deployment of Solar (STEADy Solar)

The Screening Tool for Equitable Adoption and DeploYment of Solar (STEADy Solar) is a database and mapping tool designed to promoting clean energy investments for low-income communities across the United States. The tool indicates locations that may be eligible for the Investment Tax Credit bonus adders defined in the 2022 Inflation Reduction Act (IRA) and combines this information with demographics, social vulnerability, solar technical potential, solar economics (modeled net present value), and building counts by use-type. It can be used by states, municipalities, community-based organizations, developers, and researchers to identify sites where solar projects may be economical and where federal incentives may be available to support equitable adoption of solar. Specific values include: Areas eligible for the Energy Communities Tax Credit Bonus Program (including brownfield site counts) Areas eligible for the Low Income Communities Bonus Credit Program (including Tribal Lands, and covered affordable housing project counts) Areas categorized as disadvantaged by Justice40 Commercial and Residential Solar economics characterized by the Net Present Value and Simple Payback Period Total Population, Race, and Ethnicity Median Household Income, Poverty rate, Household Tenure Social Vulnerability Count of buildings, developable rooftop solar capacity (in kWdc) and estimated annual generation potential (in kWh) on four building types: Government General Services, Government Emergency Response, Grade Schools, and Colleges/Universities. The linked report describes the STEADy dataset metadata and presents high level insights from the data. The downloadable and formatted excel dataset makes it easy for users to gain insights for their locations. Supporting .csv and shapefiles provide users with the full data to run their own analyses on equitable solar siting.

14 SOLAR ENERGY↗