Review of Energy Equity Metrics

Abstract

A literature review of energy equity and energy justice metrics was performed to support efforts to develop an energy equity metrics framework. Pacific Northwest National Laboratory (PNNL) reviewed the available literature, surveyed work in progress on the topic, and solicited expert feedback to lay the groundwork for metrics development and provide reference material for equity research and development applications. The literature review identified three distinct equity metric types: target population identification, investment decision making, and program impact assessment. • Target population identification metrics capture descriptive analytics on the population that may be eligible for support programs. • Investment decision making metrics describe how one population compares to another. These metrics are often developed by contrasting target population metrics between groups. • Program impact assessment metrics show how well a support program has helped a target community. Advancing an equitable energy future requires understanding and expanding beyond the currently available measurement mechanisms. Demographic and energy related indicators such as income, age, race, ethnicity, geographic location, energy access, energy use intensity, energy affordability, access to renewable energy, incentive accessibility, access to public services, community engagement, etc. can be used to represent the relevant equity outcomes for collecting baseline equity measurements. The near-term needs for equity metrics fall under two areas: enhancing capabilities for mapping and tracking energy inequities, and designing methods to appropriately identify target populations by operationalizing community descriptive terminologies (for example, disadvantaged communities). A key analysis area is the need for assigning scales for equity measurement—that is, answering the question: at what level should equity effects be assessed? Whether the appropriate scale is at the societal, community, neighborhood, household, or individual level needs to be thought through while addressing the issue of data availability at the desired measurement level. The data that allows for population identification at the community-scale can be episodic and difficult to correlate to other activities or systems. The most comprehensive, national data sets can be geographically diffuse, and must be either downscaled or developed through analytical means. Identifying the appropriate levels for equity measurement would allow for a more equitable quantification and comparison of inequities across populations.