A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature

Sturla F. Kvamsdal (), Ivan Belik (), Arnt Ove Hopland () and Yuanhao Li ()
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Sturla F. Kvamsdal: SNF - Centre for Applied Research at NHH
Ivan Belik: NHH Norwegian School of Economics
Arnt Ove Hopland: NHH Norwegian School of Economics
Yuanhao Li: NHH Norwegian School of Economics

Environmental & Resource Economics, 2021, vol. 79, issue 1, No 5, 93-115

Abstract: Abstract We use topic modeling to study research articles in environmental and resource economics journals in the period 2000–2019. Topic modeling based on machine learning allows us to identify and track latent topics in the literature over time and across journals, and further to study the role of different journals in different topics and the changing emphasis on topics in different journals. The most prevalent topics in environmental and resource economics research in this period are growth and sustainable development and theory and methodology. Topics on climate change and energy economics have emerged with the strongest upward trends. When we look at our results across journals, we see that journals have different topical profiles and that many topics mainly appear in one or a few selected journals. Further investigation reveal latent semantic structures across research themes that only the insider would be aware.

Keywords: Environmental and resource economics; Latent Dirichlet allocation; Literature review; Machine learning; Textual analysis; Topic modeling (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10640-021-00554-0

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