Tourism development and U.S energy security risks: a KRLS machine learning approach
Yükleniyor...
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Routledge
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
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.
Açıklama
Anahtar Kelimeler
U.S energy security risks; tourism development; policy uncertainty; technology innovation; KRLS machine learning
Kaynak
Current Issues in Tourism
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
