US federal resource allocations are inconsistent with concentrations of energy poverty

Recent data from the United States (US) Energy Information Administration reveals that nearly one in three households in the US report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We produce a novel machine learning approach based on sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. Our analysis confirms that average household energy burdens increased, and the range of households suffering energy poverty broadened. We provide an optimized allocation structure to urge policy makers to revise the distribution of funds to better match assistance needs.

Methods
We use machine learning to determine how various demographic and physical characteristics are correlated with household energy burdens across the US. Energy burden estimates allow us to identify where energy poverty may be concentrated at the census-tract level. Our analysis extends and improves upon the Low-income Energy Affordability Data (LEAD) tool, developed by the US Department of Energy’s National Renewable Energy Laboratory to estimate energy expenditures and burdens in several ways (28). The LEAD tool is designed to help local and state governments with decisions for addressing energy poverty; however, it is static in time and uses self-reported energy expenditures given only for one month of the year, which is not reported publicly. The reliance on one month implies that the estimation of annual values is not guaranteed to account for the seasonal variation in energy costs throughout the months. The sampling done by the survey must sufficiently cover all months of the year, and this is not verifiable from the publicly available data. In addition, which month is used varies across respondents. Different from LEAD, we use household-level sociodemographic and geographic data, detailed in the following subsection, from the Energy Information Administration’s (EIA) Residential Energy Consumption Survey (RECS) to estimate the annual energy burden. This survey is completed every five years, enabling us to track changes in energy burden over time. To develop our projections at a census-tract level, we use an adaptive least absolute shrinkage and selection operator (LASSO) technique to select important variables from the RECS data to be applied to census-tract level information from the US Census Bureau’s American Community Survey (ACS).