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

Balcilar M., USMAN O., Özkan O.

Current Issues in Tourism, cilt.27, sa.1, ss.37-44, 2024 (SSCI) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 27 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/13683500.2023.2245109
  • Dergi Adı: Current Issues in Tourism
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Geobase, Hospitality & Tourism Complete, Hospitality & Tourism Index, PAIS International, Veterinary Science Database
  • Sayfa Sayıları: ss.37-44
  • Anahtar Kelimeler: KRLS machine learning, policy uncertainty, technology innovation, tourism development, U.S energy security risks
  • İstanbul Ticaret Üniversitesi Adresli: Evet

Özet

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.