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

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...

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Author

Palinski,Michal;

Asik,Günes;

Gajderowicz,Tomasz;

Jakubowski,Maciej;

Efsan Nas Ozen;

Dhushyanth Raju;
Document Date
2024/09/16
Document Type
Policy Research Working Paper
Report Number
WPS10908
Volume No
1
Country
World,
Region
Other,
Disclosure Date
2024/09/16
Disclosure Status
Disclosed
Doc Name
Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining

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Citation

Palinski,Michal; Asik,Günes; Gajderowicz,Tomasz; Jakubowski,Maciej; Efsan Nas Ozen; Dhushyanth Raju.

Identification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining (English). Policy Research working paper;PLANET Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/099457309162416941

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

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Citation

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