Cross-domain dynamics: How geopolitical and climate policy uncertainties shape tourism patterns in the United States

Abstract

Predicting tourism demand is crucial for policymakers and stakeholders in the tourism industry, enabling them to enhance promotional strategies, optimize marketing efforts, align investments, and achieve operational efficiency. While existing research has highlighted the negative impact of global risk factors such as geopolitical risks, economic policy uncertainty, and climate changes on tourism demand, the specific influence of climate policy changes and their differential impact on tourist arrivals has been less explored. Addressing this gap, our study employs bivariate, multiple, and partial wavelet methods to analyze the differential impact of climate policy uncertainty and geopolitical risk on tourism arrivals in the United States. Utilizing recent monthly data from January 1996 to August 2022, we uncover significant time-frequency varying dependencies between these factors and tourism demand. Our causality analysis reveals that both geopolitical risk and climate policy uncertainty are key determinants of tourism demand in the U.S. Furthermore, the Nonlinear Autoregressive Distributed Lag model corroborates the findings of the wavelet analysis, highlighting asymmetrical interactions in the long run. These findings offer valuable insights and have significant implications for policymakers and industry stakeholders, suggesting the need for adaptive and responsive tourism strategies that consider the evolving landscape of geopolitical and climate policy uncertainties.