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

Matthew C. Harding () and Carlos Lamarche
Additional contact information
Matthew C. Harding: Department of Economics and Department of Statistics, University of California, Irvine, California 92697

Annual Review of Resource Economics, 2021, vol. 13, issue 1, 469-488

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.

Keywords: big data; machine learning; energy demand (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1146/annurev-resource-100920-034117
Full text downloads are only available to subscribers. Visit the abstract page for more information.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:anr:reseco:v:13:y:2021:p:469-488

Ordering information: This journal article can be ordered from
http://www.annualreviews.org/action/ecommerce

DOI: 10.1146/annurev-resource-100920-034117

Access Statistics for this article

More articles in Annual Review of Resource Economics from Annual Reviews Annual Reviews 4139 El Camino Way Palo Alto, CA 94306, USA.
Bibliographic data for series maintained by http://www.annualreviews.org ().