Tourism development and U.S energy security risks: a KRLS machine learning approach

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dc.contributor.authorBalcilar, Mehmet
dc.contributor.authorUsman, Ojonugwa
dc.contributor.authorÖzkan, Oktay
dc.date.accessioned2023-11-10T13:16:35Z
dc.date.available2023-11-10T13:16:35Z
dc.date.issued2023en_US
dc.departmentFakülteler, İşletme Fakültesi, İktisat Bölümüen_US
dc.description.abstractThis study presents evidence on how tourism development affects U.S. energy security risks from 1997 to 2020 using a Kernel-based regularized least squares (KRLS) machine learning approach. Our empirical results demonstrate that tourism development amplifies the U.S. energy security-related risks. Also, while technological innovation and urbanization dampen the pressure on energy security-related risks, economic policy-based uncertainty and industrial production increase energy security risks. These results survive in the disaggregated models except for the environmental-related risks sub-index which decreases as a result of tourism development. Our findings, therefore, provide useful insights for policymakers to minimize energy security-related risks.en_US
dc.identifier.doi10.1080/13683500.2023.2245109en_US
dc.identifier.scopus2-s2.0-85167836386en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6992
dc.identifier.urihttps://doi.org/10.1080/13683500.2023.2245109
dc.identifier.wosWOS:001045332800001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherRoutledgeen_US
dc.relation.ispartofCurrent Issues in Tourismen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectU.S energy security risks; tourism development; policy uncertainty; technology innovation; KRLS machine learningen_US
dc.titleTourism development and U.S energy security risks: a KRLS machine learning approachen_US
dc.typeReviewen_US

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