Learning energy burden indicators for data-driven policy using self-organizing maps
Journal Article
Overview
abstract
Abstract; ; Energy burden, the ratio of energy expenditure to household income, is a critical yet often overlooked measure of economic and environmental inequality in the United States. A high energy burden, 6% or greater, is not just a financial issue; it is a public health and environmental justice concern, as frontline communities often experience greater exposure to pollution, poorer housing efficiency, and heightened vulnerability to extreme weather events. This study uses self-organizing maps (SOMs), an unsupervised neural network, to identify contributing factors and inform policy interventions for energy-burdened communities in the North, South, Midwest, and West census regions, a novel use of this method. It is also among the first to integrate environmental justice indicators, including outdoor air quality metrics and health disparities, as determinants of energy burden. In addition to environmental justice indicators, socioeconomic status, building characteristics, and power outages are explored to assist policymakers, engineers, and advocates working within the energy transition. Results revealed statistically significant (; p; < 0.05) differences in these indicators across SOM-defined energy-burden regimes. For the Midwest and South regions, all 45 indicators showed statistical significance, while 44 were significant in the Northeast, and 41 were significant for the West. These findings suggest that high energy-burden regimes tend to coincide with elevated environmental and health risk indicators, which may intensify under climate change.;
