Assessing county-level vulnerability to the energy transition in the United States using machine learning

Abstract

Energy sector decarbonization will play a critical role in society's attempt to minimize climate change. This transition will unavoidably involve economic disruption to communities that rely on fossil fuels for jobs and economic activity. In this work, we assess the economic vulnerability of counties across the contiguous United States to the energy transition using a novel methodological approach that combines exposure through reduction in energy employment and socio-economic sensitivity due to a lack of adaptive capacity during the energy transition. Using machine learning clustering, we first identify county-level exposure to negative employment shocks. We then develop and implement the Resilience during the Energy Transition Index (RETI) to measure county-level sensitivity and adaptive capacity and compared this Index across employment clusters. We find that counties in coal mining clusters are significantly – and uniquely – less able to cope with economic distress. We also apply our framework to a case study focused on three of the states in Marcellus-Utica shale plays: Pennsylvania, Ohio and West Virginia. The results of this case study support the argument that financial and infrastructure boosts from non-renewable energy extraction and utilization build resilience only if they are used to building capital for the post‑carbon economy, providing further evidence of the conditions required for fossil fuels to serve as bridge fuels towards a low-carbon economy.