Unveiling hidden energy poverty using the energy equity gap

Energy poverty is characterized by high energy expenditure as a percentage of income, vulnerability to electricity shutoffs, and inability to maintain comfortable indoor temperatures. Current metrics, such as energy burden (percentage of income spent on energy), primarily focus on financial strain and overlook households that limit energy consumption to alleviate financial stress. This limitation is particularly significant considering the health risks associated with inadequate cooling, especially in the context of climate change and increasing heatwaves. Existing energy assistance programs in the US (LIHEAP and WAP) rely on income thresholds and energy burden, failing to capture this "hidden" energy poverty. The paper aims to address this gap by developing a new metric that incorporates energy-limiting behavior.

Existing energy poverty metrics are categorized as primary/secondary (based on data source) and relative/absolute (based on comparison). Relative-secondary metrics use aggregated regional data and compare progress across regions. Relative-primary metrics utilize household-level survey data on self-perceived energy poverty. Absolute-secondary metrics combine income-based metrics with socio-demographic factors, while absolute-primary metrics use household-level information against a predetermined threshold (e.g., 10% energy burden). Income-based metrics, although widely used, are sensitive to energy prices, fail to capture energy-limiting behavior, and miss crucial dimensions like inability to maintain comfortable temperatures. Most research focuses on electricity supply constraints in developing countries and energy affordability in developed countries, but lacks metrics to identify energy-limiting households in developed countries with universal energy access.

This study introduces the energy equity gap, a behavior-based energy poverty measure. The methodology involves identifying each household's inflection temperature—the outdoor temperature at which they begin using cooling systems—through a nonlinear regression model. This model considers daily electricity consumption, daily average temperature, electricity pricing plan, holiday effects, day-of-the-week, and month-of-the-year fixed effects. The energy equity gap is then calculated as the difference between the highest and lowest median household inflection temperatures across all income groups within a metropolitan area in Arizona. The study uses data from Salt River Project, including hourly electricity consumption from 2015-2019 for 6000 households, billing plans, and a residential equipment survey. The study also employs Mood's Median test to assess statistical significance in differences between median inflection temperatures across income groups, ethnicities, and age groups. A tiered system is developed to categorize households into low-risk, energy insecure, and energy poor based on their inflection temperatures, using 78°F as a threshold for energy poverty. The energy equity gap findings are compared with those from the traditional 10% energy burden metric.

The study estimates the energy equity gap in the Arizona metropolitan area to range from 4.7–7.5 °F (2.6–4.2 °C) across the four years analyzed (2015-2019). This indicates that low-income households consistently start using cooling systems at significantly higher temperatures than high-income households. The analysis reveals a statistically significant difference in median inflection temperatures between income groups in all four years. The energy equity gap fluctuates, with a narrowing in the first two years and widening in the subsequent years, possibly correlated with changes in residential electricity prices and cooling degree days. Disparities are observed across ethnicities, with the Black population showing the highest inflection temperatures and substantial energy equity gaps, suggesting a disproportionate impact of price increases. Similar disparities exist across age groups, with younger populations (18-24 years old) experiencing a sharp increase in both median inflection temperature and energy equity gap between 2018 and 2019. The tiered system identifies 86 energy-poor and 214 energy-insecure households (2015-2016), significantly more than the 141 households identified as energy insecure using the 10% energy burden threshold. A small overlap (≤20 households) exists between the two categorization methods, highlighting the limitations of the income-based metric. A substantial number of households with high inflection temperatures are ineligible for LIHEAP and WAP assistance, underlining the need for broader criteria for energy assistance programs.

The findings highlight the limitations of income-based energy poverty metrics, demonstrating that they fail to capture a significant portion of the population facing energy insecurity due to energy-limiting behavior. The energy equity gap offers a valuable complementary metric by focusing on behavioral patterns and revealing disparities across income groups, ethnicities, and age groups. The study’s tiered system provides a more nuanced approach to identifying households at risk of heat-related illnesses and facilitates the design of more targeted and equitable energy assistance programs. The fluctuation in the energy equity gap across the four years underscores the need to consider climate change impacts and energy price changes when developing and evaluating such programs. The results emphasize the importance of a multidimensional approach to energy poverty assessment, incorporating both financial strain and behavioral responses.

The study introduces the energy equity gap as a novel metric for assessing energy poverty, effectively capturing energy-limiting behavior often overlooked by traditional income-based measures. The findings demonstrate the need for a broader and more inclusive definition of energy poverty that incorporates behavioral aspects in addition to economic indicators. The tiered system developed in this study provides a practical tool for policy makers to design more effective and equitable energy assistance programs. Future research could investigate actual indoor temperatures, incorporate heating data (particularly for colder climates), and further analyze the influence of housing characteristics on inflection temperatures.

The study's limitations include a lack of indoor thermostat data and inability to directly measure indoor temperatures, which might influence the accuracy of the inflection temperature estimation. The analysis focuses on cooling energy use and does not fully consider heating energy consumption. While housing characteristics such as age and income correlation are addressed, further investigation into energy efficiency attributes of homes would strengthen the results. The urban heat island effect, which disproportionately affects low-income communities, might also influence the findings.