From the Cover: Measuring social equity in urban energy use and interventions using fine-scale data

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.

Cities have become a key action arena for carbon mitigation, with the US Mayors’ Climate Protection Agreement launched in 2005 and the inclusion of cities in the 2011 United Nations Climate Change Conference. More recently, cities have also started to address equity in their carbon mitigation plans (1, 2). For example, in the United States, New York City and Boston have begun to evaluate social inequality in energy use to inform more equitable distribution of energy-related investments (e.g., efficiency rebates) (3–7). To inform equity, cities are seeking methods and metrics that advance a more nuanced and fine-scale understanding of inequality in energy use and in investments, addressing both race and income.Here, we distinguish between social inequality and inequity. Social inequality metrics quantify differences in any parameter of interest based on social stratification (8). Some inequality metrics, such as those representing income inequality, assess inequality across the whole population (or surveys representing the population), using the Gini coefficient (9–11) and interquantile income ratios (e.g., P80/P20). Other inequality metrics, such as those used in public health, compute health risk disparity ratios by race, gender, or income (12). Social equity goes beyond inequality to evaluate the fairness in allocating resources (e.g., energy assistance or health care investments) among different groups to reduce social inequality with a focus on reducing disparities for the most disadvantaged strata in society (8, 13, 14). Thus, the analysis of inequality guides the distributional aspect of social equity (13, 15), that is, the distribution of burdens and benefits across social strata. Social equity also includes a procedural dimension related to the agency and participation of disadvantaged groups in decisions that impact them (16).This paper focuses on distributional equity. Cities face four main challenges, described below, in quantifying social inequality in energy use to inform more equitable investments in energy conservation and efficiency.

• 1.Lack of fine-scale empirical data across a whole city: There have been relatively few analyses of energy use inequality and investments using empirical data (i.e., data provided by utilities) at fine spatial scales within cities to inform equity. A few efforts that explore inequality in intraurban energy use have primarily relied on modeled energy use (17, 18) using the US Energy Information Administration’s Residential Energy Consumption Survey (RECS). RECS only surveys some thousands of homes nationwide (e.g., ∼5,700 in 2015 and ∼12,000 in 2012), resulting in scattered coverage within 10 census divisions, each covering five states on average (19). Consequently, modeled intraurban energy use derived from RECS reports a goodness of fit of ∼60% (17, 18), which can mask social inequality at the fine spatial scale within cities where social stratification by race and income manifests spatially. Thus, empirical data from utilities are much needed, at least at the block group level, which is the finest scale at which sociodemographic data on race and income are reported by the Census Bureau. Furthermore, to inform equity, data on energy conservation and efficiency program participation and investment across the whole city are needed to evaluate the allocation of investments across all neighborhoods in a city. However, only a few cities such as Los Angeles (20) have obtained fine-scale energy use data from utilities to evaluate inequality in energy use covering the whole city. Likewise, only a few studies have explored social inequality in investment in energy conservation and efficiency at the intraurban scale (21, 22). No previous study has evaluated both inequality in energy use and inequity in energy investments at intraurban scales.
• 2.Lack of analysis of disparities in energy use and intensity by both race and income: No previous studies have explored disparities in energy use and intensity by both race and income using real intraurban consumption data. For example, Los Angeles (20) evaluated energy inequality at a fine spatial scale by income but not by race (23, 24). Only two previous studies addressed intraurban racial disparities in heating energy use intensity (EUI) using modeled data from RECS (17, 18); however, there is high uncertainty in RECS-derived models given the small survey sample sizes noted earlier.
• 3.The challenge of spatial scale of data aggregation: When utilities provide intraurban energy usage data to cities, these data are aggregated at different spatial scales, with unknown impacts on energy inequality metrics such as disparity ratios and Gini coefficients. Spatial scales of data aggregation range from the most disaggregated premise level to census blocks (∼76 person on average in the United States), census tracts (1,200 to 8,000 persons), and ZIP (Zone Improvement Plan) code (with an average of ∼8,000 people). For example, several municipal utilities analyze premise-level data for their city policymakers [e.g., Tallahassee (25), Los Angeles (20), and others (26)]; St. Paul, Minnesota has census tract–level data provided by the local utility (27), while California cities have ZIP code–level data (28) complying with state-level regulations on data privacy provided by utilities. The spatial scale of data aggregation can impact the analysis of dispersion, recognized in geography and public health as the modifiable areal unit problem (29, 30). However, this modifiable unit area problem has not been systematically analyzed for energy use inequality due to the lack of both energy use data and sociodemographic data at fine spatial scales. Assessing how the spatial scale of data aggregation impacts energy inequality measures is important, given that different utilities are spatially aggregating data at different scales for subsequent analysis by cities.
• 4.Suitable energy use metrics and analysis procedures to inform equity: Even when city-wide fine-scale energy use data are available, there are few analysis protocols and metrics to evaluate intraurban equitable distribution of investments in conservation and efficiency. While metrics to assess inequalities in energy access, energy burden (i.e., percentage of income spent on energy services), and energy use are well developed (1, 2, 31–36), energy use metrics that best represent the impact of energy conservation and efficiency investments are still evolving. Energy use metrics, such as household energy use (kilowatt hour/household a year), energy use per capita [kilowatt hour/person a year (36)], and household energy use intensity by floor area [kilowatt hour/square feet a year (17, 18)] have been used, but all have challenges. Studies have shown that high-income households have a higher energy consumption primarily due to having larger homes (37). These high-income households are also found to be more “efficient,” showing lower EUI (18, 23). Thus, only tracking total energy use per household will primarily represent floor area effects but not the efficiency of building stock. EUI has more potential to reflect the condition of the building stock and the efficiency of heating and cooling appliances; however, low-income homes may conserve energy by sacrificing thermal comfort, experiencing energy insufficiency (36). Thus, a lower EUI does not necessarily represent more efficient provision of thermal comfort for low-income homes. Housing stock occupancy can also influence EUI, which can be normalized by household size (38), that is, EUI/capita, to capture the impact of occupancy. A better understanding of floor area, along with total household energy use, housing occupancy, and thermal comfort in conjunction with EUI, is needed to develop suitable inequality metrics for energy use. In addition to exploring appropriate energy inequality metrics, there are no analysis protocols to apply those metrics to inform conservation and efficiency investments toward the triple goals of community-wide carbon mitigation, improving energy affordability (reducing burden), and reducing social inequality in energy use and intensity.

To address the above challenges, our paper makes three key contributions. First, we develop a unique intraurban fine-scale dataset, combining sociodemographic data with energy use, occupancy, program participation, and investment data covering all homes/neighborhoods across two cities. Second, using the empirical fine-scale data (suitable to unpack race and income effects), we explore metrics for cities to quantify social inequality in energy use by both income and race and apply those to inform social equity in energy sector investments in conservation and efficiency (ESICE), for example, efficiency rebates, loans, etc. Our study brings together inequality both in energy use and in efficiency investments at the intraurban scale. Third, with the availability of fine-scale data, we provide an assessment on how energy use inequality metrics are impacted by the spatial scale of data aggregation. Overall, this work informs how cities and utilities can gather and analyze information on energy inequality to guide ESICE to advance social equity and carbon mitigation. The analytical tools demonstrated in two cities in this paper are potentially translatable to other cities and utilities.Fine-scale data (block group or finer) on both residential energy use and ESICE across the entire city for Tallahassee, Florida, and St. Paul, Minnesota, are obtained through partnerships with electric utilities under nondisclosure agreements to preserve data privacy, consistent with state and federal regulations. Data were provided at the premise level for Tallahassee’s ∼90,000 households with 1 y monthly energy use and 5 y investment data and at the block level for St. Paul’s ∼110,000 households with 1 y monthly energy use and investment data. The energy investment data include various efficiency programs (e.g., efficiency rebates, home energy use analysis, etc.; SI Appendix, Table S1). Investments in household-scale renewable energy programs, for example, rooftop solar panels, are not within the scope of this study, which focuses on ESICE.The overall method is shown in SI Appendix, Fig. S1, wherein the fine-scale database incorporates social, ecological, infrastructural, and urban form variables, consistent with urban systems frameworks (39).The inequality metrics used in this study include Gini coefficients and disparity ratios. The Gini coefficient provides a general measure of dispersion for a given parameter, without considering social stratification by income or race, with the coefficient ranging from 0 (perfectly equal distribution) to 1 (extremely unequal). We also adapt the concepts of quintile ratios and disparity ratios used in public health (8) to energy use. Energy use disparity ratios by income are computed as the ratio of the average energy attribute (e.g., EUI) reported in the lowest-income quintile block groups (20% lowest) versus that reported in the highest-income quintile. EUI disparity ratios by race are computed as the ratio of EUI in the top 20% most racially diverse block groups (>80th percentile of non-White population percentage) versus the 20% least racially diverse block groups. Disparity ratios are closely related to differences across social groups, for example, a disparity ratio of 2.5 between the lowest- and highest-income groups indicates a 150% difference with respect to the highest-income group.     

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