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Natural disasters, increasingly intensified by climate change, pose significant threats to global tourism by heightening risk perceptions and reducing tourist arrivals. Previous studies have examined disaster impacts through country-specific case studies, yet important dimensions such as disaster intensity, destination development levels, and cross-country differences in vulnerability have often been overlooked. This study addresses these gaps by integrating a structural gravity model of international tourism demand with a binned regression analysis that classifies disaster events by severity. The empirical strategy applies a two-stage estimation procedure that controls for the full set of multilateral resistances to tourism. Results show that severe events substantially reduce arrivals in developing destinations, whereas developed countries display greater resilience. Disaster intensity, rather than frequency, emerges as the primary driver of tourist responses, while moderating factors such as distance, border effects, and origin-country income shape heterogeneous outcomes.
Biardeau et al. (Fri,) studied this question.