Tax Credit Bonuses Map

With this new tool from Climate Cabinet Education, discover which parts of your community have the potential to maximize the benefits of the historic federal climate investments from the Inflation Reduction Act. Areas meeting key eligibility criteria will qualify for even greater levels of funding.

Search For Your Community

Enter the name of a state, county, municipal utility, or rural electric cooperative to view its Justice40 communities, distressed zip codes, energy communities, and designated low-income census tracts on an interactive map. You’ll also see descriptions of which programs are available and how much bonus eligibility parts of your community can access.

Tax Credit Programs

Name	Agency	Description	Base Benefit	Bonus Amounts
Direct Pay clean energy INVESTMENT tax credits	IRS	Provides a tax credit on the initial investment on a clean energy asset including fuel cell, colar, geothermal, small wind, energy storage, biogas, microgrid controllers, and combined heat and power properties.	6-30% (based on labor standards and material sourcing requirements)	Energy Communities: 10% Low Income Census Tracts: 10-20% for wind and solar projects <5 mw and projects designed to serve qualified low-income housing projects beginning construction before 2025
Direct Pay clean energy PRODUCTION tax credits	IRS	Provides a tax credit for production of electricity from clean or renewable sources through the lifetime of the policy (currently until 2031, but will likely be extended), or the lifetime of the generating asset.	.55 cents/kwh - 2.75 cents/kwh (based on labor standards and material sourcing requirements)	Energy Communities: 10% Low Income Census Tracts: 10-20% for wind and solar projects <5 mw and projects designed to serve qualified low-income housing projects beginning construction before 2026
Solar For All		Solar for All grantees will provde subsidies and other financial assistance to residential rooftop and residential-serving community solar projects in and benefiting low-income and disadvantaged communities in addition to project-deployment technical assistance such as workforce development, community outreach, and other project-deployment support (e.g., interconnection technical assistance, siting and permitting support) to help overcome barriers to solar development. Federal funds for Solar For All will mostly be granted to state agencies such as State Energy Offices. Local leaders should learn what agency in their state has received these funds to advocate for the inclusion of their community in implementation of those funds.	Based on grant application	Distressed Communities: Eligible for grant funding Low Income Census Tracts: Eligible for grant funding Justice 40 Communities: Eligible for grant funding
Alternative Fuel Refueling Property Credit	IRS	Tax credits for charging stations and alternative fuel stations which are available to both municipalities as well as taxable individuals and businesses. After 2023 eligible projects must be located in low-income communities.	6-30% (based on labor standards and material sourcing requirements)	Low Income Census Tracts: Required to receive credit
Neighborhood Access & Equity Grant	DOT	Grants that support projects which shift the transportation system towards more equitable access and benefits through increased safety and mitigation of past negative impacts brought by existing infrastructure.	up to 80% covered	Distressed Communities: 100% covered

Data

Administrative Boundaries

Boundaries were extracted from TIGER/Line Shapefiles corresponding to the 2020 U.S. Census and spanned the 50 U.S. states, District of Columbia, and U.S. Island Areas. Geography types included census blocks, block groups, counties, county subdivisions, places, states, tracts, and zip code tabulation areas (ZCTAs). The data may be accessed through the U.S. Census Bureau’s web tool or FTP server.

Distressed Communities

Distressed ZCTAs were obtained by downloading and filtering a CSV data file from the 2023 edition of the Economic Innovation Group’s Distressed Communities Index (DCI). The release uses the U.S. Census Bureau’s Business Patterns and American Community Survey 5-Year Estimates for 2016-2020. ZCTAs identified as “distressed” by EIG were then merged with 2020 TIGER/Line ZCTA Shapefiles from the U.S. Census Bureau to produce the final dataset.

Electric Retail Service Territories

Shapefiles for rural cooperatives and municipal utilities were downloaded from the Homeland Infrastructure Foundation-Level Data (HIFLD) open data portal, hosted by the Geospatial Management Office of the U.S. Department of Homeland Security. The data, which may also be viewed on an alternative web map, was last published on December 9, 2022, and utilizes multiple data sources from different years (e.g., state GIS portals, Energy Information Administration Tiger/Line Shapefiles).

Energy Communities

Two datasets for coal closure and fossil fuel employment qualifying energy communities were downloaded from the Energy Data eXchange, hosted by the Department of Energy’s National Energy Technology Laboratory (NETL). The data was last updated on June 15, 2023. At present, two categories of energy communities under the Inflation Reduction Act (IRA) have been made publicly available through the platform:

1. Census tracts and directly adjoining tracts that have had coal mine closures since 1999 or coal-fired electric generating unit retirements since 2009.

2. Metropolitan statistical areas (MSAs) and non-metropolitan statistical areas (non-MSAs) that have had for at least one year since 2009, 0.17% or greater direct employment related to extraction, processing, transport, or storage of coal, oil, or natural gas (the fossil fuel employment (FFE) threshold) and have an unemployment rate for 2022 that is equal to or greater than the national average unemployment rate for 2022.

The coal closure and FFE-eligible datasets were downloaded as zipped Shapefiles, filtered to include only geographies qualifying as energy communities, and then merged together to create the final dataset.

Justice40 Communities

A Shapefile containing metadata and geographic boundaries for Justice40 Communities was directly downloaded from the Climate & Economic Justice Screening Tool (v.1), last updated on November 22, 2022. The Council on Environmental Quality, Executive Office of the President, which manages the tool, used 2010 census tracts for boundaries and 2015-2019 American Community Survey (ACS) demographic data to determine which tracts were classified as “disadvantaged” for at least one of eight criteria: climate change, energy, health, housing, legacy pollution, transportation, water and wastewater, and workforce development.

Low-Income Communities

The U.S. Department of Energy (DOE), which administers the Low-Income Communities Bonus Credit Program under IRS tax code section 48(e), determines low-income eligibility using thresholds set by the New Markets Tax Credit (NMTC) Program, run by the Community Development Institutions Fund (CDIF) at the U.S. Department of the Treasury. Therefore, a Shapefile of 2020 census tract boundaries was downloaded and then filtered to include only tracts identified as low-income according to the program.

Under statute (26 USC §45D(e)) and amendments from the American Jobs Creation Act of 2004 (P.L. 108-357, 118 Stat. 1418), the NMTC program defines low-income communities as follows:

Census tracts with a poverty rate of 20 percent or greater,
Census tracts with a median family income at or below 80 percent of the applicable area median family income, OR
Census tracts in a High Migration Rural County with a median family income at or below 85 percent of the applicable area median family income.

A High Migration Rural County is any county with a 10 percent or greater population drop in the 20 years preceding the latest census. When any tract is located outside a metropolitan statistical area, its point of comparison is state median family income. Otherwise, the point of comparison is the greater of the state or metropolitan area median family income.

Methodology

Population-Weighted Centroids

To estimate populations for specific geographic areas, counts were pulled directly from 2020 U.S. census data products whenever possible. However, if the geography referenced older census boundaries; was not related to the census at all, as in the case of municipal utilities and rural cooperatives; or represented a spatial intersection between two geographies that lacked a FIPS code, the population was approximated by summing population-weighted centroids that fell within the geography’s borders.

The U.S. Census Bureau provides population-weighted centroids at the block group and tract level for the 50 U.S. States, District of Columbia, and Puerto Rico. However, block group centroids were selected for the population estimations because their population density did not vary as much. (Census tracts usually hold between 1,200 and 8,000 people, whereas block groups typically hold 600 to 3,000.) To estimate populations for the remaining U.S. Island Areas (i.e., American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands), block group centroids were manually created using the only population proxy available at the block level: housing counts.

The population allocation was all-or-nothing. If a block group’s population-weighted centroid fell within a particular geography, its entire population was assigned to that geography. Otherwise, none of its population was assigned.

Comparison with Areal Interpolation

Another popular but more computationally expensive strategy for estimating values in a geographic region is areal interpolation. In our case, that would mean calculating the percentage area of a block group that intersected with a geography and then allocating that percentage of that block group’s population to the geography.

To test the difference between the two methodologies, we used a paired T-test with 1,100 sampled geographies. To pick our sample size, we used an estimated effect size of 0.1, a significance level of 0.05, and a power of 0.9. The T-statistic was -0.36 and the p-value was 0.72, which indicates that the differences in methodologies were not statistically significant. Based on this, we used the less computationally expensive population-weighted centroid methodology.