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Machine learning from schools about energy efficiency

Author: Fiona BurligChristopher R KnittelDavid RapsonMar Reguant-RidoCatherine D WolframAll authors
Publisher: Cambridge, Mass. : National Bureau of Economic Research, 2017.
Series: Working paper series (National Bureau of Economic Research), no. 23908.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this  Read more...
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Details

Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Fiona Burlig; Christopher R Knittel; David Rapson; Mar Reguant-Rido; Catherine D Wolfram; National Bureau of Economic Research,
OCLC Number: 1006766931
Notes: "October 2017"
Includes online appendix (18 pages).
Description: 1 online resource (49 pages) : illustrations, maps.
Series Title: Working paper series (National Bureau of Economic Research), no. 23908.
Responsibility: Fiona Burlig, Christopher Knittel, David Rapson, Mar Reguant, Catherine Wolfram.

Abstract:

In the United States, consumers invest billions of dollars annually in energy efficiency, often on the assumption that these investments will pay for themselves via future energy cost reductions. We study energy efficiency upgrades in K-12 schools in California. We develop and implement a novel machine learning approach for estimating treatment effects using high-frequency panel data, and demonstrate that this method outperforms standard panel fixed effects approaches. We find that energy efficiency upgrades reduce electricity consumption by 3 percent, but that these reductions total only 24 percent of ex ante expected savings. HVAC and lighting upgrades perform better, but still deliver less than half of what was expected. Finally, beyond location, school characteristics that are readily available to policymakers do not appear to predict realization rates across schools, suggesting that improving realization rates via targeting may prove challenging.

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