Tourism development and U.S energy security risks

A KRLS machine learning approach

Bibliographic Data

ID21698998
AuthorsMehmet Balcılar (0000-0001-9694-5196, University of New Haven), Ojonugwa Usman (0000-0002-6459-9898, Department of Economics, Istanbul Ticaret University, Istanbul, Turkey, corresponding author), Oktay Ozkan (0000-0001-9419-8115, Department of Business Administration, Tokat Gaziosmanpasa University, Tokat, Turkey)
Year2024
Volume27
Issue1
Pages37-44
Publication date2024-01-02
Open AccessNo
TypeARTICLE
VenueCurrent Issues in Tourism (JOURNAL)
Journal identifiersISSN: 1368-3500 • E-ISSN: 1747-7603
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/13683500.2023.2245109
OpenAlexW4385738983
LanguageEN
Citations received10
References cited9

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

Business · Econometrics · Economics · Energy security · Environmental economics · Political science · Renewable energy · Statistics · Tourism · Computer Science · Energy, Environment, and Transportation Policies · Energy, Environment, Economic Growth · Engineering · Market Dynamics and Volatility · Mathematics

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