Significance

Cities seek income and racial equity in residential low-carbon energy efficiency and conservation programs. However, empirical data are limited; prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25% (i.e., 25% greater EUI in low- versus high-income homes), while racial disparities are unquantified. New empirical fine spatial scale energy use data covering all ∼200,000 households in two US cities, along with separation of temperature-sensitive EUI, reveal large EUI disparities by income (27 to 167%) and race (40 to 156%). These disparities are up to a factor of five greater than the 25% income disparity previously reported. New analytics provide key insights on energy use inequality unpacking race and income, informing spatial prioritization for equitable energy efficiency investments.

Abstract

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.