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

Mehmet Balcılar

doi:10.1080/13683500.2023.2245109

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

2023-08-09

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DOI：10.1080/13683500.2023.2245109

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Abstract

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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.

Keywords:

U.S energy security risks

tourism development

policy uncertainty

technology innovation

KRLS machine learning

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

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