世界银行-通过人工智能驱动的文本挖掘识别扩展的绿色职位清单（英）

Policy Research Working Paper10908Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text MiningMichał PalińskiGüneş AşıkTomasz GajderowiczMaciej JakubowskiEfşan Nas ÖzenDhushyanth RajuSocial Protection and Jobs Global PracticeSeptember 2024 Public Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedPublic Disclosure AuthorizedProduced by the Research Support TeamAbstractThe Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.Policy Research Working Paper 10908This 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 liter-ature 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 identi-fied. The retrieval-augmented generation model used the advanced text analysis capabilities of Generative Pre-trained Transformer 4, providing a reproducible method to catego-rize jobs within various green economy sectors. The research clustered these job titles into 25 distinct sectors. This catego-rization 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.This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at snasozen@worldbank.org and draju2@worldbank.org. Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining Michał Paliński Güneş Aşık Tomasz Gajderowicz Maciej Jakubowski Efşan Nas Özen Dhushyanth Raju Keywords: AI, text mining, occupational classification, green jobs, green eco