Climate-induced tourism breaks: Segmented-GAM analysis

Abstract

This study examines how severe weather affects hotel demand across the Pacific Coast and Northeast U.S. Using daily data from seven jurisdictions (2015–2024), we employ segmented regression and Generalized Additive Models (GAM). We detect 23 structural breakpoints, indicating tourism decline starting three years prior to COVID-19. Segmented regression reveals a systematic decrease in demand since 2017. GAM reveals non-linear climate-demand links, with variance explained rising from 21% for single events to 53% for multiple events. Regional differences emerge: the Pacific Coast experiences weather vulnerability with gradual adaptation, while the Northeast demonstrates resilience. The lack of a post-pandemic recovery suggests structural shifts, challenging assumptions about tourism resilience, highlighting the permanent impacts of weather, and the need for data-driven climate adaptation strategies.