Learning the Indicators of Energy Burden for Knowledge Informed Policy (Papers Track)
Jasmine Garland (University of Colorado Boulder); Rajagopalan Balaji (University of Colorado, Boulder); Kyri Baker (University of Colorado, Boulder); Ben Livneh (University of Colorado, Boulder)
Abstract
The United States is one of the largest energy consumers per capita, which puts an expectation on households to have adequate energy expenditures to keep up with modern society. This adds additional stress on low-income households that may need to limit energy use due to financial constraints. This paper investigates energy burden, the ratio of household energy bills to household income, within the United States West. Self-Organizing Maps, an unsupervised neural network, is used to learn the indicators attributed to energy burden to inform public policy. This is one of the first studies to consider environmental justice indicators, which include outdoor air quality metrics and health disparities as energy burden indicators. The results show significant (p<0.05) differences among high energy burden areas and those with no energy burden for the environmental justice indicators. Thus, beyond the socioeconomic hardships of marginalized communities, counties with high energy burden suffer from environmental and health hazards, which will be amplified under a changing climate.