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.
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Copy CitationPeter Christensen, Paul Francisco, Erica Myers, Hansen Shao, and Mateus Souza, "Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting," NBER Working Paper 30467 (2022), https://doi.org/10.3386/w30467.
Published Versions
Peter Christensen & Paul Francisco & Erica Myers & Hansen Shao & Mateus Souza, 2024. "Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting," Journal of Public Economics, vol 234.
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