A measurement strategy to address disparities across household energy burdens

Abstract

Energy inequity is an issue of increasing urgency. Few policy-relevant datasets evaluate the energy burden of typical American households. Here, we develop a framework using Net Energy Analysis and household socioeconomic data to measure systematic energy inequity among critical groups that need policy attention. We find substantial instances of energy poverty in the United States – 16% of households experience energy poverty as presently defined as spending more than 6% of household income on energy expenditures. More than 5.2 million households above the Federal Poverty Line face energy poverty, disproportionately burdening Black, Hispanic, and Native American communities. For solar, wind, and energy efficiency to address socioeconomic mobility, programs must reduce energy expenditures by expanding eligibility requirements for support and access to improved conservation measures, efficiency upgrades, and distributed renewables. We recommend the United States develop a more inclusive federal energy poverty categorization that increases assistance for household energy costs.

Hot or cold temperature disproportionately impacts U.S. energy burdens

“…Socioeconomic and demographic variables of interest, including the proportion of the Black population and the percentage of the population with educational attainment below or equal to a high school diploma, are from ACS 5-year estimates. With reference to previous studies [9,10,18,28,[33][34][35][36], we select nine representative socio-demographic attributes that provide reasonable proxies for energyvulnerable groups and conduct the heterogeneous analysis.…”

“…Our study advances the existing literature in the following ways: first, we enlarge the cross-sectional energy burden dataset to the longitudinal time dimension and household data aggregated at the county level across the conterminous U.S., largely improving existing regional survey data and national cross-sectional data that typically covers a static year, thus better capturing spatiotemporal disparities in energy burdens. Although finer spatial resolution data, such as census tract, may be available according to previous energy burden studies based on the LEAD Tool [26,27], these studies only report 1-year results for 2018, which not only limits spatiotemporal discussion on energy burdens from a longer-term temporal perspective [28][29][30], but also constrains the information that can be obtained due to their crosssectional regression analyses [31,32]. Second, we disaggregate total energy burdens by energy sources into electricity burdens, natural gas burdens, and fuel oil burdens, overcoming the limitations of current research accounting for all sources of energy bills and providing a more detailed understanding of energy burdens based on various household fuels and technology richness.…”

Unravelling the variations of the society of England and Wales through diffusion mapping analysis of census 2011

“…For example, [4] shows in some cities, the number of university students and social deprivation are paramount in explaining other social variables in census statistics. Other research illuminates the impact of social, economic and ethnic attributes on regional disparities, such as energy burdens in households [5], heterogeneity in epidemic vulnerability [6] and Furthermore, researchers' differing perspectives on nominal attributes like race or religion can lead to a lack of consensus on these features' significance. These issues make synthesizing findings from different studies to identify critical socioeconomic characteristics challenging [10].…”

Economic growth and household energy footprint inequality in China

“…The energy footprint has been increasingly used to measure the impact of human activities on global climate change [4][5][6]. The close link among the wealth gap, energy footprint inequality and energy poverty has been demonstrated [7][8][9]. This study aims to provide policymakers with information to help them understand more about the relationship among poverty, inequality and energy footprint in designating policy measures.…”