Bridging the Gap Between Residential Energy Data and Policy for Vulnerable Communities Under Climate Change

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🔬研究者:The interdisciplinary ML-remote sensing framework for electricity equity analytics is a novel methodological contribution.

🏢実務担当者:Utilities and city planners can use the approach to identify vulnerable areas and target electrification incentives.

🏛政策担当者:The findings on disproportionate burdens and public support for renewables inform equitable decarbonization policies.

Climate change is posing increasingly urgent challenges to global energy systems. Understanding how residential energy consumers respond differently to a changing climate is critical for developing effective solutions to address climate and energy challenges, especially in vulnerable communities. However, existing energy data is limited in spatial and temporal resolution, making it difficult to yield data-driven electrification and decarbonization insights. I first compile a unique 8-year county-level panel dataset to estimate the disproportionate impacts of hot or cold temperatures on different energy burdens in the conterminous U.S., with a particular focus on the heterogeneous role of social vulnerability. The findings show that temperature deviations may introduce additional burdens on those already energy-vulnerable groups, including those low-income, less-educated, and living in energy-inefficient old houses, with disadvantaged groups several times more adversely affected by temperatures than advantaged groups. Next, I revisit the climate-electricity equity nexus in the era of residential electrification. To further overcome limitations in electricity data, I innovatively construct an interdisciplinary research framework that combines energy economics, machine learning, remote sensing, and geospatial techniques for fine-grained residential electricity equity analytics. The results demonstrate that our ML-based framework substantially improves the space-time resolution of electricity data and uncovers large climate-induced spatiotemporal disparities, thus proposing data-driven policies for equitable electrification. Finally, I evaluate the public support for decarbonization strategies by estimating the willingness to pay and underlying determinants for renewable energy programs in Vietnam, thus providing a new perspective to understand the feasibility and potential of renewable energy deployment and adoption in energy-vulnerable communities. The results show that Vietnamese residents are more supportive than other emerging economies in Southeast Asia, and their decision-making is largely motivated by air pollution concerns and perceived utility profitability.

• openalex https://doi.org/10.17615/fsym-sb50first seen 2026-06-10 04:42:56