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dc.contributor.authorVodenska, Irenaen_US
dc.contributor.authorTrajanov, Ristoen_US
dc.contributor.authorChitkushev, Louen_US
dc.contributor.authorTrajanov, Dimitaren_US
dc.date.accessioned2023-08-16T09:24:47Z-
dc.date.available2023-08-16T09:24:47Z-
dc.date.issued2022-06-24-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/27432-
dc.description.abstractEnvironmental, Social, and Governance (ESG) criteria gain increasing attention by governments and corporations to assess how advanced countries and companies are with sustainability. The adoption of the ESG in-vestment approach addresses risk management issues and sets goals to-ward more responsible behavior. Sustainable development goals adopted by the United Nations in 2015 include a call for action to end poverty, save the planet, and ensure peace and prosperity for all by 2030. This paper studies advanced machine learning methodologies to assess, analyze, and suggest improvements in corporate behavior to comply with global sustainable development goals.en_US
dc.publisherSpringer Nature Switzerlanden_US
dc.subjectESG (Environmental, Social, and Governance) Investments, Machine learning, AI-based systems, Sustainable Development, Financial and Economic Stabilityen_US
dc.titleChallenges and Opportunities in ESG Investmentsen_US
dc.typeProceeding articleen_US
dc.relation.conferenceInternational Conference on Computer Science and Education in Computer Scienceen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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