@article{44dc540a510a480db72294bab09a1b39,
title = "Machine learning from schools about energy efficiency",
abstract = "We use high-frequency panel data on electricity consumption to study the effectiveness of energy efficiency upgrades in K–12 schools in California. Using a panel fixed effects approach, we find that these upgrades deliver between 12% and 86% of expected savings, depending on specification and treatment of outliers. Using machine learning to inform our specification choice, we estimate a narrower range: 52%–98%, with a central estimate of 60%. These results imply that upgrades are performing less well than ex ante predictions on average, although we can reject some of the very low realization rates found in prior work.",
keywords = "Energy efficiency, Machine learning, Schools",
author = "Fiona Burlig and Christopher Knittel and David Rapson and Mar Reguant and Catherine Wolfram",
note = "Funding Information: Fiona Burlig is at the Harris School of Public Policy and Energy Policy Institute, University of Chicago, and the National Bureau of Economic Research (NBER) (burlig@uchicago.edu). Christopher Knittel is at the Sloan School of Management and Center for Energy and Environmental Policy Research, MIT, and NBER (knittel@mit.edu). David Rapson is in the Department of Economics, University of California Davis (dsrapson@ucdavis.edu). Mar Reguant is in the Department of Economics, Northwestern University, Center for Economic and Policy Research (CEPR), and NBER (mar.reguant@northwestern.edu). Catherine Wolfram is at the Haas School of Business and Energy Institute at Haas, University of California Berkeley, and NBER (cwolfram@berkeley.edu). We thank Dan Buch, Arik Levinson, and Ignacia Mercadal as well as seminar participants at several venues for helpful comments. Joshua Blonz and Kat Redoglio provided excellent research assistance. We gratefully acknowledge financial support from the California Public Utilities Commission. Burlig was generously supported by the National Science Foundation{\textquoteright}s Graduate Research Fellowship Program under grant DGE-1106400. All remaining errors are our own. Dataverse data: https://doi.org/10.7910/DVN/NDAYCR Publisher Copyright: {\textcopyright} 2020 by The Association of Environmental and Resource Economists.",
year = "2020",
month = nov,
doi = "10.1086/710606",
language = "English (US)",
volume = "7",
pages = "1181--1217",
journal = "Journal of the Association of Environmental and Resource Economists",
issn = "2333-5955",
publisher = "University of Chicago Press",
number = "6",
}
