Energy Burden in the United States: An Analysis Using Decision Trees

The concept of energy burden (EB) continues to gain prominence in energy and associated policy research as energy prices rise and electricity and heating options diversify. This research offers a deeper understanding of EB dynamics and how EB can be addressed more effectively by discerning the interplay between regional environmental, social, and economic factors. Using decision trees (DTs), a powerful machine learning technique, we explore the multifaceted dynamics that shape EB across the United States (U.S.) by examining how factors like housing quality, demographic variations, access to energy sources, and regional economic conditions interact, creating distinct EB profiles across communities. Following a comprehensive review of existing literature and DT analysis, we map the results to identify the most significant factors influencing EB. We find that no single variable has a determinant effect on EB levels. While there is no uniform regional pattern, regions with higher population density exhibit a stronger correlation between EB and socioeconomic and other demographic factors such as educational attainment levels and racial segregation. Our findings underscore the significance of regional ecologies in shaping EB, revealing how localized environmental and economic contexts amplify or mitigate systemic inequities. Specifically, our analysis reveals significant regional disparities, highlighting the need for localized policies and interventions. We find that a one-size-fits-all approach is insufficient and that targeted, place-based strategies are necessary to address the specific needs of different communities. Policy interventions should prioritize energy democracy, address systemic inequities, and ensure universal energy access through participatory planning, financial assistance, and targeted initiatives such as housing rehabilitation, energy efficiency improvements, and incentives for underrepresented communities.