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

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  • Mehmet Balcilar
  • Ojonugwa Usman
  • Oktay Özkan

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.

Suggested Citation

  • Mehmet Balcilar & Ojonugwa Usman & Oktay Özkan, 2024. "Tourism development and U.S energy security risks: a KRLS machine learning approach," Current Issues in Tourism, Taylor & Francis Journals, vol. 27(1), pages 37-44, January.
    • Handle: RePEc:taf:rcitxx:v:27:y:2024:i:1:p:37-44
    DOI: 10.1080/13683500.2023.2245109
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    Cited by:

    1. Kartal, Mustafa Tevfik & Pata, Ugur Korkut & Alola, Andrew Adewale, 2024. "Energy security risk and financial development nexus: Disaggregated level evidence from South Korea by cross-quantilogram approach," Applied Energy, Elsevier, vol. 363(C).

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