Mapping County-Level Exposure and Vulnerability to the US Energy Transition
This working paper examines where economic changes associated with the US energy transition are concentrated, as well as which communities are most vulnerable to this transition.
The urgent challenge of climate change necessitates an energy transition at unprecedented speed and scale (National Academies of Science, Engineering, and Medicine 2021). As the United States seeks to deeply reduce greenhouse gas emissions, and as public policies coupled with innovation accelerate the deployment of clean energy and associated technologies, economic changes will occur across the nation. But where are those economic changes likely to be concentrated, and which communities might be most vulnerable to disruptions?
This analysis seeks to help answer those questions by combining a near-comprehensive view of fossil fuel activities at the county level with a range of socioeconomic, environmental, and public health indicators. The results can help policymakers better understand and prioritize which communities may be most vulnerable, and which may be most resilient, to accelerating changes in the US energy economy.
Several recent analyses have sought to achieve related goals. In the scholarly literature, Carley et al. (2018) develop a framework to evaluate county-level vulnerability associated with the energy transition, incorporating measures of exposure (e.g., job losses), sensitivity (e.g., share of population living in poverty), and adaptive capacity (e.g., institutional capacity). They apply the framework to the case of renewable portfolio standards, which reduce emissions but increase electricity costs, and focus on communities that are vulnerable to these higher costs.
Other recent work has reviewed the social outcomes of climate mitigation policies (Lamb et al. 2020), assessed transition-related socioeconomic and environmental risks for communities around the world (Sovacool et al. 2021), examined the vulnerability of low-income US households to higher energy costs (Brown et al. 2020), and proposed principles to guide policymakers (Muttitt and Kartha 2020; Bazilian et al. 2021). Scholars have also provided case studies of US coal communities, identifying challenges and proposing policy pathways to improve transition planning (Haggerty et al. 2018; Jolley et al. 2019; Roemer and Haggerty 2021). A recent analysis examines Appalachian communities, seeking to identify the main factors that help enable economic resilience as coal production has declined (Lobao et al. 2021).
Taking a similar approach to this analysis, Snyder (2018) combines fossil fuel employment data from the Bureau of Labor Statistics (BLS) with socioeconomic measures to create an index of energy transition vulnerability for US counties. The paper provides an analogue to the current analysis but is limited in two ways. First, it aggregates all fossil fuel employment into a single category. As discussed in more detail below, the risks from climate policies vary considerably across fuels and technologies, a dynamic that Snyder does not take into account. As a result, for example, counties in Wyoming, which dominates US coal production, do not appear near the top of the index. Second, the rationale for including and weighting various metrics in the index is not clear, making it difficult to know whether the most important contributors to vulnerability are truly reflected in the index.
In recent months, government entities and policy researchers have produced analyses to provide more practical guidance for policymakers. The White House Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization recently identified 25 US regions where fossil energy activities are concentrated, grouping regions by BLS metropolitan and nonmetropolitan classifications (Interagency Working Group on Coal and Power Plant Communities and Economic Revitalization 2021). These groupings provide a useful starting point for understanding which regions are likely to be affected, but they offer limited geographic specificity and limited detail on socioeconomic and environmental risk factors.
To develop a more granular geographically picture of which communities are most dependent on fossil energy as an economic driver, I produced a series of maps identifying the counties where fossil energy accounts for large shares of employment and wages (Raimi 2021). However, these maps were incomplete because the BLS data that underpin them are often suppressed for low-population (typically rural) counties, which may be particularly vulnerable to the effects of the energy transition. In addition, that analysis did not incorporate additional measures of socioeconomic or environmental vulnerability.
The purpose of this analysis is to produce a tool that can guide policymakers in focusing attention and resources on the appropriate places at the appropriate time. To that end, it makes three main contributions. First, it identifies all US counties (or equivalent governmental units) that are heavily dependent on fossil energy as an economic driver, ranking them by the scale of the relevant energy activity. Second, it provides a high level of geographic specificity (county level). Third, it includes not only measures of energy activity but also metrics to assess the socioeconomic and environmental risk factors present in each county. Taken together, these metrics should give policymakers practical guidance on which US communities are most vulnerable to the economic effects of a transition away from fossil fuels.
Importantly, this analysis can (and will) be improved in the months ahead. Future work will seek to refine the relevant socioeconomic and environmental indicators, perhaps developing an index to more easily prioritize counties (though, as noted above and discussed more below, index creation presents methodological issues). In addition, it will seek to better characterize how effects may evolve over time in different locations. For example, ambitious climate policies are likely to cause more rapid declines in coal production than natural gas production, leading to differential timing between coal-producing and natural gas–producing communities. What’s more, there is variation within fuels: coals, oils, and natural gases produced in different locations have different life-cycle emissions characteristics, and those with lower life-cycle emissions, better access to markets, and other economic advantages are likely to be most resilient, at least in the near to medium term.
Read the full paper here.
The data tool, which includes maps for all listed indicators, can be found here.
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