Integrating social justice criteria into multiscale spatial modeling of energy burden: A case study of four states in the United States

Abstract

Energy equity is a growing global concern that addresses the fair distribution of energy access and affordability across different groups. While previous studies have demonstrated that factors, such as income level, race, and housing characteristics, affect energy burden, the integration of social justice into quantitative modeling remains limited. Most existing studies rely on socioeconomic or environmental variables selected for statistical convenience rather than their alignment with justice-oriented principles. This gap has constrained our understanding of how structural inequities, such as historical disinvestment, marginalization, or policy inaccessibility, manifest spatially in energy burden patterns. To address this, this study establishes revised Engineering for Social Justice (E4SJ) criteria, providing a systematic framework for selecting variables that reflect key social justice dimensions in energy burden analysis. Using a multiscale geographically weighted regression (MGWR) model, this study analyzes energy burdens in Indiana, Illinois, Arizona, and Texas to capture the spatially varying impact of different variables across census tracts. The analysis achieves higher adjusted R2 values compared to traditional regression models, such as ordinary least squares and geographically weighted regression, demonstrating the effectiveness of MGWR and E4SJ-informed variable selection in improving explanatory power. The results reveal substantial spatial heterogeneity in the determinants of energy burden, shaped by regional economic structures and climatic conditions. By integrating justice-oriented criteria into spatial modeling, this study advances methodological approaches to energy equity assessment and provides evidence for developing region-specific policies that address localized drivers of inequity.