Decomposing the Wedge between Projected and Realized Returns in Energy Efficiency Programs

Abstract

Evaluations of energy effciency programs reveal that realized savings consistently fall short of projections. We decompose this ‘performance wedge’ using data from the Illinois Home Weatherization Assistance Program (IHWAP) and a machine learning-based event study research design. We find that bias in engineering models can account for up to 41% of the wedge, primarily from overestimated savings in wall insulation. Heterogeneity in workmanship can also account for a large fraction (43%) of the wedge, while the rebound effect can explain only 6%. We find substantial heterogeneity in energy-related benefits from IHWAP projects, suggesting opportunities for better targeting of investments.

The implicit cost of carbon abatement during the COVID-19 pandemic

“…The use of machine learning is also becoming increasingly popular within causal frameworks in energy economics ( Burlig et al, 2020 , Christensen et al, 2021 , Knittel and Stolper, 2019 ), potentially because it is a field in which required assumptions are particularly more likely to hold. Prior literature has shown, for example, that it is possible to accurately forecast energy demand using only exogenous covariates, such as weather realizations.…”

“…A key input of the simulations thus regards the estimation of the electricity demand reductions attributable to the COVID-19 crisis, for which we build on prior literature using machine learning methods ( Burlig et al, 2020 , Christensen et al, 2021 ). We use high-frequency energy data, weather variables, and date/time fixed effects from 2015–2019 to train a highly flexible model to predict counterfactual demand in 2020 in the absence of the pandemic.…”

The Distribution of Energy Efficiency and Regional Inequality

“…2 However, evaluations have shown that actual savings realized from energy-efficiency programs seldom achieve the level of energy savings that are predicted using ex-ante engineering models (Fowlie et al 2018, Giraudet et al 2018, Christensen et al 2020 codes that are associated with the best socio-economic outcomes are home to a disproportionately higher share of energy-efficient buildings. However, the poorest states of Germany, in the East, also benefit from both the use of less carbon-intensive heating fuel type and energy-efficient buildings.…”

A Systematic Review of Energy Efficiency Home Retrofit Evaluation Studies

“…We found that just over 60% of the studies we identified as evaluating energy savings from residential retrofit programs used simulated data, which may not accurately account for retrofit contractor quality or consumer behavioral responses, and are prone to prediction errors from incorrect modeling assumptions (Joskow and Marron, 1992;1993;Fowlie et. al., 2018;Christensen et. al, 2020).…”