Machine Learning from Schools about Energy Efficiency
Fiona Burlig,
Christopher Knittel,
David Rapson,
Mar Reguant and
Catherine Wolfram
No 23908, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C14 C55 L9 Q41 (search for similar items in EconPapers)
Date: 2017-10
New Economics Papers: this item is included in nep-big, nep-cta, nep-ene and nep-reg
Note: EEE IO LS PE PR
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Citations: View citations in EconPapers (35)
Published as 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, vol 7(6), pages 1181-1217.
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