Mapping US Energy Communities

This data tool, which accompanies a 2021 working paper, allows users to visualize "hotspots" that may be most affected by an energy transition away from fossil fuels. The tool combines a near-comprehensive view of fossil fuel activities at the county level with a range of socioeconomic, environmental, and public health indicators. These indicators are based on recent (e.g., 2018 or 2019) data, and do not predict the effects of the energy transition for communities. Instead, they indicate the baseline conditions under which an energy transition would occur. As such, they are subject to change in the future. For example, a rapid transition away from oil could reduce local pollution in refining communities while at the same time reducing population or economic outcomes in oil producing regions.

For more information on the data, methods, and further insights, read the working paper or read the press release.

Many variables and data sources could be used to identify socioeconomic and environmental vulnerability, and choosing among the options is not straightforward. After reviewing dozens of candidate variables, analyzing correlations, and speaking with experts, we settled on 12 measures, each of which provides relevant information on vulnerability and most of which are weakly correlated with one another.

Each variable is described below, briefly noting the rationale for its inclusion, and identifying the underlying data source.

• Population: 2019 population estimate. These data are included in the “circle size” dropdown to illustrate per capita levels of energy activity. Rationale: Large populations may justify enhanced attention while per capita energy activity levels indicate heightened dependence on that activity. Source: US Census.
• Economic status: Index from 1 to 4 (4=distressed) based on 3-year average unemployment rates, per capita market income, and poverty rates. Rationale: Higher economic distress increases vulnerability. Source: ARC
• Share with less than Bachelor’s degree: Share of population with less than a bachelor’s degree, 2019. Rationale: Lower education levels increase vulnerability. Source: USDA.
• Rurality: USDA’s rural-urban continuum codes from 1 to 9 (1=most urban, 9=most rural). Rationale: Higher rurality increases vulnerability. Source: USDA.
• Share minority population: Share of population that is not in the Census category "White alone, not Hispanic or Latino." Rationale: racial or ethnic minorities may face discrimination, increasing vulnerability. Source: Census.
• Share linguistic isolation: Share of households that are linguistically isolated. Rationale: Linguistic isolation may increase vulnerability. Source: EPA EJScreen.
• Share pre-1960 housing: Share of households living in homes constructed before 1960 (lead paint indicator). Rationale: Older housing stock increases vulnerability. Source: EPA EJScreen.
• Air toxics cancer risk: Nationwide percentile for lifetime cancer risk from inhalation of air toxics. Rationale: Higher cancer risk increases vulnerability. Source: EPA EJScreen.
• Toxic water discharges: Nationwide percentile for risk-screening environmental indicators modeled toxic concentrations at stream segments. Rationale: More toxic water discharges increase vulnerability. Source: EPA EJScreen.
• Superfund sites: Nationwide percentile for count of proposed or listed Superfund sites within 5 kilometers (or nearest one beyond 5 kilometers). Rationale: Proximity to Superfund sites increases vulnerability. Source: EPA EJScreen.
• Ambient ozone: Nationwide percentile for ozone summer seasonal average of daily maximum 8-hour concentration in air, in parts per billion. Rationale: Higher ozone levels increase vulnerability. Source: EPA EJScreen.
• Ambient PM2.5: Nationwide percentile for PM2.5 levels in air, g/m3 annual average. Rationale: Higher PM2.5 levels increase vulnerability. Source: EPA EJScreen.

Notes: For EPA EJScreen, data are aggregated from census block groups to the county level, with each block group weighted by population.