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Small Steps with Big Data: Using Machine Learning in Energy and Environmental Economics

Author

Listed:
  • Matthew C. Harding

    (Department of Economics and Department of Statistics, University of California, Irvine, California 92697)

  • Carlos Lamarche

    (Department of Economics, Gatton College of Business and Economics, University of Kentucky, Lexington, Kentucky 40506)

Abstract

This article reviews recent endeavors to incorporate big data and machine learning techniques into energy and environmental economics research. We find that novel datasets, from high frequency smart meter data to satellite images and social media data, are already used by researchers. At the same time most of the analyses rely on traditional econometric techniques. Nevertheless, we find applications of machine learning models that address the high dimensionality of the data and seek out new and better strategies for estimating heterogenous treatment effects. We provide an introduction to the main themes in machine learning, which are likely to be of use to economists in energy and environmental economics, and illustrate them using a real data example derived from an energy efficiency program evaluation. We provide the data and code in order to stimulate further research in this area.

Suggested Citation

  • Matthew C. Harding & Carlos Lamarche, 2021. "Small Steps with Big Data: Using Machine Learning in Energy and Environmental Economics," Annual Review of Resource Economics, Annual Reviews, vol. 13(1), pages 469-488, October.
    • Handle: RePEc:anr:reseco:v:13:y:2021:p:469-488
    DOI: 10.1146/annurev-resource-100920-034117
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