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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number105098
JournalJournal of Public Economics
Volume234
DOIs
StatePublished - Jun 2024

Keywords

  • Cost-effectiveness
  • Energy efficiency
  • Machine learning
  • Targeting

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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