Blog – Thoughtfully Measuring Energy Poverty

 

Thoughtfully Measuring Energy Poverty

BLOG POST  |  AUGUST 17, 2020
By: Dana Harmon, Executive Director

Identifying the populations suffering from energy poverty is not simple.  A growing body of work, including our own, has shown the complexities of both finding and engaging people who may be best served by energy interventions. Researchers and practitioners often use the metric of energy cost burden, measured as the percentage of household income needed to cover home energy costs, to determine need. Most programs aimed at addressing energy poverty use an income threshold as qualification criteria for participation.

While energy burden is a useful metric, the measure of energy burden alone is insufficient to describe whether a household is able to reasonably consume enough energy to lead a healthy and productive lifestyle.  Energy burden alone doesn’t adequately identify the most vulnerable populations, which is a vital step towards developing appropriate interventions to sustainably address energy poverty.

A new study from the University of Texas at Austin, recently published in Energy Policy Journal, is entitled Subjective Versus Objective Energy Burden: A Look at Drivers of Different Metrics and Regional Variation of Energy Poor Populations Advances Our Understanding of Identifying Energy Poor Populations. The team posits that it is necessary to combine both objective and subjective metrics to identify the populations most in need of aid to alleviate energy poverty.

The team used statistical analysis of TEPRI 2018 survey data and other data sources to: 1) assess the regional variation of energy poverty (defined as a ratio of household income spend to electricity bills); 2) determine if there’s an association between objective and subjective metrics of energy poverty; and 3) identify statistical drivers of objective and subjective energy poverty metrics.

The team measured objective metrics of energy burden using average monthly electricity bills as a percentage of income, and subjective metrics by analyzing responses to questions like, “Have you had difficulty paying your electricity bill?” and, “Do your electricity bills cause you great stress or mental discomfort?”

The findings of this study show that subjective metrics classified the greatest portion of respondents, and objective metrics captured the lowest portion, as shown in the figure below.

Figure 1. Percentage of respondents classified as energy burdened using subjective vs. objective metrics. Reprinted from Agbim, C., Araya, F., Faust, K., Harmon, D. (2020).

This work suggests that we must not rely on objective energy burden alone to identify populations suffering from energy poverty, and more must be done to develop useful metrics to identify those most in need. This work further confirms findings of TEPRI’s 2018 Low-Income Community Profile (LICP) Study—that energy burden is an important metric but insufficient on its own. In our analysis, we explored conditions that were associated with subjective metrics. For instance, among our low-income survey respondents difficulty with electricity bills was often related to poor housing conditions, being a renter, and the presence of children in the home.

We look forward to the continuing discussion surrounding the most thoughtful ways to measure energy poverty. As we aim to inspire lasting energy solutions for low-income communities, employing the right measurement tools is a critical piece of the puzzle.

Note: This publication will be publicly available via the link above (see here) until Friday, August 21st. Beyond that date, it will be available behind a paywall.

Source:

Agbim, C., Araya, F., Faust, K., Harmon, D. (2020). Subjective vs. objective energy burden: A look at drivers of different metrics and regional variation of energy poor populations. Energy Policy, 144. https://doi.org/10.1016/j.enpol.2020.111616.