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Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining

Author

Listed:
  • Paliński, Michał

    (University of Warsaw)

  • Aşık, Gunes A.

    (TOBB University of Economy and Technology)

  • Gajderowicz, Tomasz

    (University of Warsaw)

  • Jakubowski, Maciej

    (University of Warsaw)

  • Nas Özen, Efşan

    (World Bank)

  • Raju, Dhushyanth

    (World Bank)

Abstract
This study expands the inventory of green job titles by incorporating a global perspective and using contemporary sources. It leverages natural language processing, specifically a retrieval-augmented generation model, to identify green job titles. The process began with a search of academic literature published after 2008 using the official APIs of Scopus and Web of Science. The search yielded 1,067 articles, from which 695 unique potential green job titles were identified. The retrieval-augmented generation model used the advanced text analysis capabilities of Generative Pre-trained Transformer 4, providing a reproducible method to categorize jobs within various green economy sectors. The research clustered these job titles into 25 distinct sectors. This categorization aligns closely with established frameworks, such as the U.S. Department of Labor's Occupational Information Network, and suggests potential new categories like green human resources. The findings demonstrate the efficacy of advanced natural language processing models in identifying emerging green job roles, contributing significantly to the ongoing discourse on the green economy transition.

Suggested Citation

  • Paliński, Michał & Aşık, Gunes A. & Gajderowicz, Tomasz & Jakubowski, Maciej & Nas Özen, Efşan & Raju, Dhushyanth, 2024. "Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining," IZA Discussion Papers 17286, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17286
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    References listed on IDEAS

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    1. Consoli, Davide & Marin, Giovanni & Marzucchi, Alberto & Vona, Francesco, 2016. "Do green jobs differ from non-green jobs in terms of skills and human capital?," Research Policy, Elsevier, vol. 45(5), pages 1046-1060.
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    More about this item

    Keywords

    AI; text mining; occupational classification; green jobs; green economy;
    All these keywords.

    JEL classification:

    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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