Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting
Building energy efficiency has been a cornerstone of greenhouse gas mitigation strategies for decades. However, impact evaluations have revealed that energy savings typically fall short of engineering model forecasts that currently guide funding decisions. This creates a resource allocation problem that impedes progress on climate change. Using data from the largest U.S. energy efficiency program, we demonstrate that a data-driven approach to predicting retrofit impacts based on previously realized outcomes is more accurate than the status quo engineering models. Targeting high-return interventions based on these predictions dramatically increases net social benefits, from $0.93 to $1.23 per dollar invested.
We thank Mick Prince, Chad Wolfe, the PRISM Climate Group, and student assistants in the University of Illinois Big Data and Environmental Economics and Policy (BDEEP) Group at the National Center for Supercomputing Applications for assistance with data and computational resources related to this project. We acknowledge excellent feedback and comments from Tatyana Deryugina, Arik Levinson, Lucija Muehlenbachs, Stefan Staubli, and Bruce Tonn. We acknowledge generous support from the Alfred P. Sloan Foundation and the Illinois Department of Commerce and Economic Opportunity's Illinois Home Weatherization Assistance Program. Souza gratefully recognizes the support from the European Research Council (ERC, under the European Union's Horizon 2020 research and innovation programme, Grant Agreement No. 772331). The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.