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Machine Learning from Schools about Energy Efficiency

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Listed:
  • Fiona Burlig
  • Christopher Knittel
  • David Rapson
  • Mar Reguant
  • Catherine Wolfram

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.

Suggested Citation

  • Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    • Handle: RePEc:ucp:jaerec:doi:10.1086/710606
    DOI: 10.1086/710606
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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