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

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dc.contributor.authorPaliński, Michał
dc.contributor.authorAşık,Güneş, Güneş
dc.contributor.authorGajderowicz, Tomasz
dc.contributor.authorJakubowski, Maciej
dc.contributor.authorNas Özen , Efşan
dc.contributor.authorRaju, Dhushyanth
dc.date.accessioned2024-09-19T13:35:56Z
dc.date.available2024-09-19T13:35:56Z
dc.date.issued2024-09-19
dc.description.abstractThis 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.en
dc.identifierhttp://documents.worldbank.org/curated/en/099457309162416941/IDU187c8b83e12ddf149031b9ce1e2ecdf2d773a
dc.identifier.doi10.1596/1813-9450-10908
dc.identifier.urihttps://hdl.handle.net/10986/42163
dc.languageEnglish
dc.language.isoen_US
dc.publisherWashington, DC: World Bank
dc.relation.ispartofseriesPolicy Research Working Paper; 10908
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/igo/
dc.subjectAI
dc.subjectTEXT MINING
dc.subjectOCCUPATIONAL CLASSIFICATION
dc.subjectGREEN JOBS
dc.subjectGREEN ECONOMY
dc.subjectDECENT WORK AND ECONOMIC GROWTH
dc.subjectSDG 8
dc.subjectINDUSTRY, INNOVATION AND INFRASTRUCTURE
dc.subjectSDG 9
dc.titleIdentification of an Expanded Inventory of Green Job Titles through AI-Driven Text Miningen
dc.typeWorking Paper
dspace.entity.typePublication
okr.crossref.titleIdentification of an Expanded Inventory of Green Job Titles through AI-Driven Text Mining
okr.date.disclosure2024-09-19
okr.date.lastmodified2024-09-16T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/099457309162416941/IDU187c8b83e12ddf149031b9ce1e2ecdf2d773a
okr.guid099457309162416941
okr.identifier.docmidIDU-87c8b83e-2ddf-4903-b9ce-e2ecdf2d773a
okr.identifier.doi10.1596/1813-9450-10908
okr.identifier.doihttps://doi.org/10.1596/1813-9450-10908
okr.identifier.externaldocumentum34391661
okr.identifier.internaldocumentum34391661
okr.identifier.reportWPS10908
okr.import.id5298
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/099457309162416941/pdf/IDU187c8b83e12ddf149031b9ce1e2ecdf2d773a.pdfen
okr.region.geographicalWorld
okr.topicSocial Protections and Labor::Labor Markets
okr.topicIndustry
okr.topicEnvironment::Green Issues
okr.topicEnvironment::Environmental Economics & Policies
okr.unitSocial Protection & Labor ECA (HECSP)
relation.isAuthorOfPublication1c10655a-fd13-5895-9ace-c83619e62db1
relation.isAuthorOfPublication.latestForDiscovery1c10655a-fd13-5895-9ace-c83619e62db1
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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