The role of ESG indicators in closing the disaster insurance gap: a machine learning analysis

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dc.authoridhttps://orcid.org/0000-0002-2079-0674
dc.authoridhttps://orcid.org/0000-0001-6784-5698
dc.contributor.authorMeral, Hasan
dc.contributor.authorÇavga, Seyit Hamza
dc.date.accessioned2025-08-22T13:53:41Z
dc.date.available2025-08-22T13:53:41Z
dc.date.issued2025
dc.departmentSağlık Hizmetleri Meslek Yüksekokulu
dc.description.abstractThis study investigates the role of environmental, social, and governance (ESG) indicators in addressing global protection gaps in disaster insurance. Utilizing a comprehensive dataset from EM-DAT and the World Bank, covering the period from 2000 to 2022, the research employs advanced machine learning techniques to analyze the complex relationships between ESG factors and disaster insurance coverage. Among the methodologies applied, CatBoost and Gradient Boosting stand out for their strong predictive performance and reliable generalization capabilities. The findings reveal that governance quality, particularly in terms of stronger control of corruption, is the most significant ESG factor. On the social dimension, improved access to essential infrastructure, emerges as a crucial contributor to disaster insurance coverage. Additionally, environmental conditions related to the economic significance of primary sectors help elucidate variations in coverage. Alongside these ESG variables, disaster-specific factors, particularly total economic damage, remain critical determinants of protection gaps.
dc.identifier.citationHasan Meral & Seyit Hamza Cavga (22 Jul 2025): The role of ESG indicators in closing the disaster insurance gap: a machine learning analysis, Sustainable and Resilient Infrastructure, DOI: 10.1080/23789689.2025.2536404
dc.identifier.doi10.1080/23789689.2025.2536404
dc.identifier.issn2378-9689
dc.identifier.issn2378-9697
dc.identifier.urihttps://hdl.handle.net/11363/10301
dc.identifier.wos001533785100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÇavga, Seyit Hamza
dc.institutionauthoridhttps://orcid.org/0000-0001-6784-5698
dc.language.isoen
dc.publisherTAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND
dc.relation.ispartofSUSTAINABLE AND RESILIENT INFRASTRUCTURE
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDisaster insurance coverage
dc.subjectenvironmental social and governance (ESG)
dc.subjectinsurance protection gap
dc.subjectmachine learning
dc.subjectsustainable development
dc.titleThe role of ESG indicators in closing the disaster insurance gap: a machine learning analysis
dc.typeArticle

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