Energy Burden in the United States: An Analysis Using Decision Trees
Name
energies-18-00646-v2.pdf
Size
6.67 MB
Format
Adobe PDF
Checksum (MD5)
b83f90b2a31bea35988b900650c48ee6
Author(s)
Chun, Jungwoo
Ortiz, Dania
Jin, Brooke
Kulkarni, Nikita
Hart, Stephen
Knox-Hayes, Janelle
Date Issued
January 30, 2025
Journal
Energies
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Chun, J.; Ortiz, D.; Jin, B.; Kulkarni, N.; Hart, S.; Knox-Hayes, J. Energy Burden in the United States: An Analysis Using Decision Trees. Energies 2025, 18, 646.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/en18030646
