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

Mehmet Balcilar, Ojonugwa Usman () and Oktay Özkan

Current Issues in Tourism, 2024, vol. 27, issue 1, 37-44

Abstract: This 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.

Date: 2024
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DOI: 10.1080/13683500.2023.2245109

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