Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting

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

No 30467, NBER Working Papers from National Bureau of Economic Research, Inc

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 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.

JEL-codes: H50 Q4 (search for similar items in EconPapers)
Date: 2022-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-env
Note: EEE
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Citations: View citations in EconPapers (5)

Published as 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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Journal Article: Energy efficiency can deliver for climate policy: Evidence from machine learning-based targeting (2024)
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