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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1181-1217 |
| Number of pages | 37 |
| Journal | Journal of the Association of Environmental and Resource Economists |
| Volume | 7 |
| Issue number | 6 |
| DOIs | |
| State | Published - Nov 2020 |
Funding
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) ([email protected]). Christopher Knittel is at the Sloan School of Management and Center for Energy and Environmental Policy Research, MIT, and NBER ([email protected]). David Rapson is in the Department of Economics, University of California Davis ([email protected]). Mar Reguant is in the Department of Economics, Northwestern University, Center for Economic and Policy Research (CEPR), and NBER ([email protected]). Catherine Wolfram is at the Haas School of Business and Energy Institute at Haas, University of California Berkeley, and NBER ([email protected]). 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’s Graduate Research Fellowship Program under grant DGE-1106400. All remaining errors are our own. Dataverse data: https://doi.org/10.7910/DVN/NDAYCR
Keywords
- Energy efficiency
- Machine learning
- Schools
ASJC Scopus subject areas
- Economics and Econometrics
- Nature and Landscape Conservation
- Management, Monitoring, Policy and Law
