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

• 成果类型: Article
• 署名作者: Christensen, Peter; Francisco, Paul; Myers, Erica; Shao, Hansen; Souza, Mateus
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Calgary; University of Mannheim
• 刊物名称: JOURNAL OF PUBLIC ECONOMICS
• ISSN/ISSBN: 0047-2727
• DOI: 10.1016/j.jpubeco.2024.105098
• 发表日期: 2024
• 关键词: Energy efficiency Machine learning Cost-effectiveness Targeting

摘要

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.