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Mapping Spatiotemporal Disparities in Residential Electricity Inequality Using Machine Learning.

Ying Yu1, Xijing Li2,3, Angel Hsu1

  • 1Data-Driven EnviroLab, Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.

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|October 30, 2024
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Summary
This summary is machine-generated.

Electrification is key for decarbonization but risks energy unaffordability. This study uses machine learning to map electricity inequality and identify vulnerable communities, revealing significant seasonal and rural burdens.

Keywords:
equitable electrificationmachine learningresidential electricity inequalityspatiotemporal disparities

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Area of Science:

  • Energy policy
  • Environmental science
  • Data science

Background:

  • Decarbonizing the energy sector through electrification is essential.
  • Energy unaffordability may increase without adequate safeguards.
  • Understanding neighborhood-scale residential electricity inequality is crucial.

Purpose of the Study:

  • To develop a high-resolution, spatiotemporally explicit machine learning (ML) framework to predict residential electricity consumption.
  • To construct an electricity affordability gap (EAG) metric to identify energy-vulnerable communities.
  • To analyze spatial and temporal disparities in energy affordability.

Main Methods:

  • Utilized publicly available, multisourced remote sensing and census data.
  • Developed a machine learning (ML) framework for predicting tract-level monthly electricity consumption (2013-2020).
  • Constructed the electricity affordability gap (EAG) metric (electricity bills vs. 3% household income).

Main Results:

  • The ML framework improved electricity consumption data resolution, achieving an R² of 0.82 compared to LEAD data.
  • An estimated annual $16.18 billion economic burden impacts electricity bill affordability.
  • Monthly EAG is 2-3 times higher in summer/winter, and rural residents face up to 1.7 times higher burdens than urban counterparts.

Conclusions:

  • The developed framework enhances the understanding of residential electricity inequality.
  • Significant seasonal and urban-rural disparities in energy affordability were identified.
  • Insights can inform equitable electrification strategies and energy justice efforts by addressing spatiotemporal mismatches.