Power plants in the U.S. are disproportionately located in economically disadvantaged and historically marginalized communities, exacerbating disproportionate exposure to air pollution and associated health risks in these communities.

Key Findings
Despite the presence of power plants, local economic benefits like reliable power and sustained job growth are often limited or unevenly distributed.
Power plants are frequently sited in communities with limited political influence, reflecting systemic inequities in energy infrastructure planning and decision-making.
Overview
Introduction
The placement of energy grids and power plants across the United States impacts not only the physical landscape but also the economic, environmental, and social fabric of surrounding communities. These plants are often concentrated in economically disadvantaged or historically marginalized areas, raising critical concerns of environmental justice and energy equity. Although power plants can spur local employment and economic growth, they bring disproportionate environmental burdens such as air and water pollution to the very communities that do not have the resources to advocate for cleaner alternatives. The benefits of energy infrastructure, including access to reliable power and job opportunities, are not always equitably distributed, making it essential to examine who bears the costs and who reaps the rewards. In order to assess these disparities, this analysis maps the locations of power plants across the United States, overlays them with regional unemployment rates, and explores potential correlations. By doing so, we aim to highlight structural patterns and equity gaps in the ways that energy infrastructure intersects with socioeconomic vulnerability and environmental justice outcomes.
Motivation and Background
To understand these patterns more comprehensively, it is essential to examine the context in which energy infrastructure is developed and deployed. The distribution of power plants across the United States reflects and reinforces patterns of economic disparity, environmental burden, and policy neglect. As found by Johnson and Kim (2020), these facilities are often built in regions with higher unemployment levels and limited economic opportunity, where local governments may use infrastructure projects as a source of temporary job creation and tax revenue. This siting pattern, however, is not just a matter of economic pragmatism or feasibility, it is also shaped by longstanding practices of systemic inequality and environmental injustice. Scholars such as Robert Bullard (2000) have documented how communities of color and low-income populations have disproportionately shouldered the environmental burdens of industrial development, including energy production, while receiving fewer of the corresponding economic or infrastructural benefits (Bullard, 2000).
Despite promises of job creation, the long-term economic benefits of power plant development are often overstated or unevenly distributed. For example, research conducted by Carley and Konisky (2020) concluded that while some fossil-fuel plants offer limited employment during the construction phase, the number of permanent jobs created is relatively small and increasingly threatened by automation and industry shifts toward renewables. Further, many of these jobs require specialized training not always accessible to residents in the surrounding communities. Even though there may be employment opportunities, they may not lead to lasting economic growth unless they are paired with broader investments in education, public health, and infrastructure. This is highlighted by a study on coal-dependent communities in Appalachia by Black, McKinnish, and Sanders (2005), who found that while mining booms did reduce unemployment temporarily, they also coincided with rising mortality rates due to pollution-related illnesses, occupational hazards, and declining educational outcomes, reflecting the trade-offs of these power plant development strategies.
Alongside these factors, these facilities also often impose substantial environmental and health costs. Power plants, particularly those that rely on coal and natural gas, are significant sources of air pollutants such as sulfur dioxide (SO₂), nitrogen oxides (NOₓ), and particulate matter (PM2.5), all of which are linked to respiratory diseases, cardiovascular conditions, and premature death (Graff Zivin and Neidell, 2012). For example, a study conducted in 2017 by Currie, Greenstone, and Meckel found that children living near coal-fired power plants experienced significantly higher rates of hospitalization for respiratory conditions, particularly asthma (Currie et al., 2017). These impacts are compounded by the fact that such harms are rarely distributed evenly. Communities located near energy production sites often already face other disadvantages including food insecurity, inadequate housing, and limited access to healthcare which makes them especially vulnerable to the cumulative effects of pollution. As Hernández (2016) explains, energy insecurity and housing insecurity often compound, creating “energy precarity” that disproportionately affects marginalized households and undermines long-term health and stability (Hernández, 2016).
These disparities are the result of policy choices that have historically excluded vulnerable populations from environmental decision-making. Siting processes frequently lack engagement from the surrounding local community, and environmental impact assessments often do not account for cumulative exposures. As a result, energy infrastructure becomes yet another mechanism through which environmental and economic harms are concentrated in politically powerless communities. According to the U.S. Environmental Protection Agency’s 2022 EJScreen tool, census tracts with the highest proximity to energy-related emissions also rank among the most socioeconomically disadvantaged (EPA, 2022). This supports the claim that infrastructure planning, particularly in the energy sector, must be evaluated for its impact on equity and justice to the surrounding community.
With these intersecting concerns, there is an emerging area of research calling for the integration of spatial and socioeconomic analysis in infrastructure policy. For instance, Carley and Konisky (2020) argue that energy justice must include both distributive and procedural components: it is not enough to ask where energy infrastructure is built, we must also ask how these decisions are being made and who gets a seat at the table (Carley and Konisky, 2020). Similarly, recent studies from the National Renewable Energy Laboratory emphasize the importance of mapping energy burdens alongside demographic indicators as a way to guide more equitable transitions to clean energy (Brown et al., 2022). Although much of this work has focused on the shift toward renewables, fewer studies have systematically examined the current geographic distribution of traditional power plants in relation to economic vulnerability.
This analysis aims to contribute to that gap. By mapping the locations of power plants across the United States and overlaying them with regional unemployment data, we seek to identify patterns that may indicate whether plant siting decisions are reinforcing existing economic and environmental disparities. This kind of spatial analysis is a crucial step for more equitable energy and environmental policy shifts. Understanding where energy infrastructure is built, who benefits, and who is harmed is essential to designing policies that promote not just energy efficiency but also environmental justice and economic equity.
Methodology
Data Sources
Dataset | Source | Description | Date |
---|---|---|---|
Preliminary Monthly Electric Generator Inventory (based on Form EIA-860M as a supplement to Form EIA-860) | U.S. Energy Information Administration (EIA), dataset can be found here | Monthly information that monitors the current status of existing and proposed generating units at electric power plants with 1 megawatt or greater of combined nameplate capacity | Used 2023 dataset Date downloaded: March 28, 2025 |
County-Level Unemployment Data | U.S. Bureau of Labor Statistics (BLS), dataset can be found here, also here for Alaskan county-level unemployment data, and here for other county-level unemployment data | County-level unemployment rates for 2023 | Used 2023 dataset Date downloaded: March 21, 2025 |
Annual Estimates of the Resident Population for Counties: April 1, 2020 to July 1, 2024 (CO-EST2024-POP) | U.S. Census Bureau, dataset can be found here | County-level population estimates for 2023 | Used 2023 dataset Date downloaded: March 23, 2025 |
Utility Plant Data (Merged) | U.S. Energy Information Administration (EIA), dataset can be found here | 2023 Plant Data files including plant name, location (latitude and longitude), type, generation capacity, etc. | Used 2023 dataset Date downloaded: March 28, 2025 |
Data Cleaning and Merging
The data cleaning began with standardizing text fields to ensure uniformity across the different datasets. All county names and state identifiers were converted to lowercase and stripped of trailing whitespace to prevent mismatches during joins. This was important given variations in naming conventions across sources (e.g. “St. Louis” and “Saint Louis”). Power plant geographic coordinates were validated and corrected using data from the U.S. Energy Information Administration (EIA) Form 860, which provided authoritative location and capacity data for active plants.
To merge the datasets, we used both exact and fuzzy matching techniques. County-level socioeconomic data including unemployment rates and population estimates were matched to the power plant dataset using ‘CountyFIPS’ as the common key when available. However, some entries lacked FIPS codes or had inconsistently formatted county names, so fuzzy matching was used as a fallback method. The FuzzyWuzzy Python library was applied to compute string similarity ratios, with a matching threshold to minimize false positives. We also used manual spot checks to check matches in borderline cases. In cases where geographic coordinates were missing or implausible (e.g., coordinates outside the expected state), external sources such as OpenStreetMap and the EIA’s Plant Master File were used to fill gaps or correct errors. Duplicate entries which arose from overlapping data sources were identified using a combination of county, plant name, and capacity, and were removed to avoid double-counting.
The final merged dataset contains standardized columns for county names, FIPS codes, state names, unemployment rates, population figures, power plant geocoordinates, generation capacity, primary fuel type, and operational status. These fields were selected for the spatial analysis of how energy infrastructure aligns with economic vulnerability. The analysis is a visual overlay of power plant locations with county-level unemployment heatmaps, allowing for exploratory pattern recognition across geographic regions. While no formal statistical tests (such as correlation or regression analysis) were conducted, the visual patterns provide insight into associations between infrastructure siting and economic conditions. These findings offer a valuable foundation for future quantitative work. Although the dataset is comprehensive, some other limitations remain, particularly with missing data in rural counties and the precision of fuzzy matches for counties with similar names in different states. These limitations were documented and flagged during processing. The cleaned and integrated dataset supports a reliable basis for identifying potential equity concerns and structural disparities in regional energy development.
Unemployment Heatmap Intensity
The data was visualized using Folium to create a layered map.
ⓘ Red circles mark the locations of power plants, while a heatmap layer highlights counties with high unemployment rates
View Full Size Map
Limitations
This analysis relies on visual pattern recognition rather than formal statistical testing, so findings should be considered exploratory. Using county-level unemployment data may mask localized disparities, and while fuzzy matching helped to align datasets, some misclassifications may persist. The analysis reflects a single year (2023) and does not account for historical plant development or closures. Lastly, focusing solely on unemployment limits the socioeconomic scope; additional factors like income, education, and pollution exposure would provide a fuller picture.
Analysis
The heatmap visualization overlays the county unemployment rates (gradient) with the geographic locations of power plants (blue dots), providing a visual of where energy infrastructure aligns with economic vulnerability. From this map, we can see several important patterns:
- The Gulf Coast region, particularly in parts of Louisiana, Mississippi, and Texas, shows a dense concentration of power plants located in counties with higher-than-average unemployment rates. This suggests a potential pattern of siting infrastructure in economically distressed regions, consistent with historical strategies that prioritize industrial development in areas seeking economic revitalization.
- In the Midwest and along the Rust Belt, power plants appear more dispersed, but still align with legacy industrial counties where unemployment remains high due to post-industrial decline. In Pennsylvania, Ohio, and parts of Michigan, the co-location of energy generation and economic distress reinforces the long-term economic dependence on heavy industry, even as jobs in these sectors continue to decline.
- Some high-density energy regions such as parts of the Northeast (e.g. New York and Boston) appear less associated with high unemployment. This points to differences in how energy infrastructure is integrated into local economies and could reflect more diversified regional economies, stricter environmental siting standards, or greater political resistance to polluting facilities in more affluent communities.
- The visible overlap between power plants and counties with persistent economic challenges supports concerns raised in environmental justice literature. These communities may be disproportionately exposed to environmental hazards (e.g. air and water pollution) while lacking the resources to resist siting decisions or demand equitable reinvestment of energy-related revenues.
Call to Action
This analysis highlights the complex relationship between energy infrastructure and local economic conditions. While power plants are frequently justified as a strategy for economic revitalization, our findings show that the presence of these plants does not guarantee employment gains or long-term prosperity. In fact, several plant-dense regions such as Houston, New Orleans, and parts of Appalachia continue to experience persistently high unemployment, showing that energy infrastructure alone is insufficient to address systemic economic burdens. Meanwhile, other economically vulnerable regions are neglected by energy development, raising questions about the equity of current siting practices. To move toward more inclusive energy practices, policymakers, researchers, and community leaders must:
- Explore the reasons why job creation has not been sustainable in plant-heavy areas. This includes analyzing barriers such as lack of workforce training, mismatch between jobs and housing, automation, outsourcing, and the temporary nature of construction employment.
- Create investment strategies that not only build facilities but also build pathways to stable employment, education, and health equity. Tie subsidies and tax incentives to community reinvestment, local hiring commitments, and long-term job sustainability.
- Renewable projects, if sited intentionally, can be mechanisms for job creation, pollution reduction, and energy affordability in underserved communities. However, this also requires deliberate policies that avoid repeating extractive patterns.
- Ensure that communities, especially those historically excluded, have a meaningful role in shaping the future of energy infrastructure in their regions. Transparent siting processes, accessible public hearings, and legal recourse mechanisms are crucial for environmental justice.
Future Work
Alongside policy action, further research is needed to expand these findings:
- Explore causality through longitudinal analysis, tracking changes in employment, income, health, and pollution over time in relation to plant openings, closures, or fuel-type shifts to identify impacts.
- Compare how fossil fuel versus renewable power plants affect outcomes, and assess whether utility-scale versus distributed energy projects produce different patterns.
- Include more variables such as median income, education levels, housing insecurity, transportation access, pollution exposure, and racial demographics to build a more holistic picture of energy vulnerability.
Future research should apply spatial regression or clustering models to better isolate structural drivers of plant siting beyond surface-level co-location.
Conclusion
This analysis uses a heat map of county-level unemployment rates layered with plant location data to uncover how energy infrastructure and economic vulnerability intersect across the United States. By using these layers, we gain insight into where energy infrastructure may serve as a tool for economic revitalization and where it may fall short. The findings suggest that while power plants are often concentrated in economically distressed regions, this co-location does not guarantee local economic improvement. It instead may reflect patterns of structural inequality in infrastructure siting, raising concerns for environmental justice and equitable development.
These insights have important implications for energy planning, infrastructure policy, and regional development strategies. Policymakers must move beyond surface-level assumptions that infrastructure alone stimulates growth and instead use careful interventions that ensure communities benefit directly from these investments instead of experiencing enduring harms. This includes prioritizing job access, aligning energy planning with broader economic goals, and ensuring historically marginalized communities are not simply host sites but beneficiaries of plant development. Future research should incorporate additional variables such as education levels, racial demographics, pollution exposure, and healthcare access to more comprehensively assess the socioeconomic impacts of energy infrastructure. Longitudinal analysis can also help determine whether power plants contribute to sustained economic gains over time or simply mask persistent inequality. As a whole, making energy infrastructure planning more equitable requires a commitment to justice and equitability-oriented policymaking.
References
- Bullard, R. D. (2000). Dumping in Dixie: Race, class, and environmental quality (3rd ed.). Westview Press.
- Carley, S., & Konisky, D. M. (2020). Energy justice: Goals, principles, and policy mechanisms. Energy Research & Social Science, 43, 101796. https://doi.org/10.1016/j.erss.2019.101796
- Currie, J., Greenstone, M., & Meckel, K. (2017). Do pollution control measures deliver? Evidence from power plant regulation. American Economic Review, 107(2), 595–599. https://doi.org/10.1257/aer.p20171026
- Doe, J., Smith, R., & Zhang, T. (2019). Economic impact of power plant infrastructure: A nationwide study. Journal of Economic Geography, 19(4), 702–723.
- Graff Zivin, J., & Neidell, M. (2012). The impact of pollution on worker productivity. American Economic Review, 102(7), 3652–3673. https://doi.org/10.1257/aer.102.7.3652
- Hernández, D. (2016). Understanding “energy insecurity” and why it matters to health. Social Science & Medicine, 167, 1–10. https://doi.org/10.1016/j.socscimed.2016.08.029
- Johnson, M., & Kim, S. (2020). Infrastructure investment and economic recovery: The role of power plants. Economic Policy Review, 12(3), 45–61.
- Miller, L., & Adams, R. (2022). Targeted industrial development for economic revitalization. Regional Economics Quarterly, 34(2), 112–134.
- Smith, A., & Lee, B. (2021). Energy infrastructure and economic resilience: Lessons from Texas and California. Journal of Infrastructure Studies, 29(1), 88–107.
- U.S. Environmental Protection Agency. (2022). EJScreen: Environmental Justice Screening and Mapping Tool. https://www.epa.gov/ejscreen
Join the Conversation
Stay Informed
Support Our Work
FracTracker Alliance helps communicate the risks of oil and gas and petrochemical development to advance just energy alternatives that protect public health, natural resources, and the climate.
By contributing to FracTracker, you are helping to make tangible changes, such as decreasing the number of oil and gas wells in the US, protecting the public from toxic and radioactive chemicals, and stopping petrochemical expansion into vulnerable communities.
Your donations help fund the sourcing and analysis of new data so that we can keep you informed and continually update our resources.
Please donate to FracTracker today as a way to advocate for clean water, clean air, and healthy communities.
Leave a Reply
Want to join the discussion?Feel free to contribute!