New work in Non-Energy Benefits / Impacts (NEBs / NEIs)

Panel: 4. Monitoring and evaluation for a wise, just and inclusive transition

Authors:
Lisa A. Skumatz, Skumatz Economic Research Associates(SERA), USA
Ann Vander Vliet, Skumatz Economic Research Associates

Abstract

Non-Energy Benefits (NEBs/NEIs) represent an array of effects realized by participants, utilities, and society that are delivered by energy efficiency (EE) measures and programs. 20 years of research monetizing these values provides useful information for:

• marketing/uptake,

• tracking progress toward policy/program goals (low income, equity issues, and policy advice on fuel-switching, etc.), and

• improved, less biased, utility benefit-cost (B/C).

This study uses a comprehensive, searchable database of the NEBs literature to examine several questions in three relevant areas in NEBs. The database was assembled from a comprehensive literature survey, including US and international studies, including all sectors (residential, low income, multi-family, commercial), and all NEB types (utility, societal, and participant). The resulting searchable database includes: bibliography of 1800 studies, more than 43,540 lines of NEB data from more than 500 NEB studies, and more than 160 NEBs.

Besides summarizing background information about how much NEBs work has been done over the years, we illustrate the flexibility of the database by drilling down on three topics of interest:

• Program Implications: What benefits do EE programs provide to disadvantaged households? How big are they, and what benefits are provided in the various hardship areas. And how do these compare to the direct energy savings?

• Policy Applications: How well do the currently-adopted NEB adders in place in US states reflect the monetized estimates of the value of NEBs based on research to date? What values could be justifiable? What can be learned for the next states that consider approving NEB adders or incentives?

• Methods-Related: What lessons can be learned about how well studies are conducted. Are they following best measurement practices, and are they falling short?

Based on our NEBs database, we found several gaps including:

• “Hardship”: NEBs can be used to reflect the improvements in hardship that are the goal of many low income EE programs in the US. The results of this study show that the improvements – in financial stability, health and quality of life, and housing stock/security – represent 2-5 times the value of the energy savings delivered by these programs. The size of the estimated effect depends on the items included in the definition of hardship. The NEB metrics, using existing NEBs research, show EE programs bring substantial, measurable benefits to these disadvantaged households.

• NEB Adder: A number of states and utilities in the US have been working to improve the B/C tests used to “screen” EE measures, programs, and portfolios. About half the states have incorporated some reflection of the missing “benefits” into the B/C equations, many using percentage adders. The research indicates that these easy adders are far lower than the NEBs research would indicate are justifiable based on hundreds of quantitative NEBs studies completed to date. States with adders should consider revisiting their adopted values, and new states implementing adders could make a case for higher adder values. The database provided information sufficient to calculate credible adders for each of the main cost test perspectives.

• NEBs Methods Gaps: Estimating NEBs for EE programs requires attention to methodological detail, and the review of the literature finds several common gaps, especially related to properly specifying the “baseline” conditions. In addition, there is a desire to re-use NEBs that have already been estimated in existing studies for other locations. However, many of the NEBs studies are not well set-up for transferability of results. To improve transferability, this analysis indicates the field would benefit from conducting more studies on a per-measure basis, rather than a program-wide basis, and from greater “normalization” ($/kWh saved, etc.) of the results to support transporting some NEBs between programs.

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