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

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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)

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.

Suggested Citation

  • Sturla F. Kvamsdal & Ivan Belik & Arnt Ove Hopland & Yuanhao Li, 2021. "A Machine Learning Analysis of the Recent Environmental and Resource Economics Literature," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(1), pages 93-115, May.
    • Handle: RePEc:kap:enreec:v:79:y:2021:i:1:d:10.1007_s10640-021-00554-0
    DOI: 10.1007/s10640-021-00554-0
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    2. Jia, Jun-Jun & Zhu, Mengshu & Wei, Chu, 2022. "Household cooking in the context of carbon neutrality: A machine-learning-based review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).

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