Energy Efficiency Can Deliver for Climate Policy: Evidence from Machine Learning-Based Targeting / Peter Christensen, Paul Francisco, Erica Myers, Hansen Shao, Mateus Souza.

Format:

Book

Author/Creator:

Christensen, Peter.

Contributor:

National Bureau of Economic Research.

Francisco, Paul.

Myers, Erica.

Shao, Hansen.

Souza, Mateus.

Series:

Working Paper Series (National Bureau of Economic Research) no. w30467.

NBER working paper series no. w30467

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2022.

Summary:

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.

Notes:

Print version record

September 2022.