Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting

Peter Christensen, Paul Francisco, Erica Myers, Hansen Shao and Mateus Souza

Journal of Public Economics, 2024, vol. 234, issue C

Abstract: 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 Illinois implementation of the U.S.’s largest 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.

Keywords: Energy efficiency; Machine learning; Cost-effectiveness; Targeting (search for similar items in EconPapers)
JEL-codes: C45 C53 Q48 Q56 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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Working Paper: Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pubeco:v:234:y:2024:i:c:s0047272724000343

DOI: 10.1016/j.jpubeco.2024.105098

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