Statistical Emulation of Climate Impacts on Tourism Dynamics in Italy: Long-term Projections and Policy Implications

Understanding the long-term impacts of climate change on socio-economic systems requires computationally efficient methods that integrate complex climatological and economic processes. In this study, we employ statistical emulation techniques to project the impacts of climate change on domestic tourism demand in Italy through the year 2100. Using outputs from 22 regional climate models (RCMs) produced by the Coordinated Regional Climate Downscaling Experiment over Europe (EURO-CORDEX) project under RCP 4.5 and RCP 8.5 scenarios, we develop a statistical model that combines economic indicators (e.g., GDP and exchange rates) with climate variables such as temperature, solar radiation, and precipitation.

Non-linear effects of climate on tourism demand are also incorporated. By utilising statistical emulators, we achieve computational efficiency, enabling scenario analyses across diverse emissions pathways.

This research advances impact modelling for the tourism sector, illustrating how parsimonious statistical models can bridge the gap between complex Earth system simulations and practical applications in policy and industry. By quantifying these effects based on empirical evidence and widely accepted climate change projections, this study aims to inform mitigation policies and strategies that enhance sustainability and resilience in tourism destinations facing climate challenges. Ultimately, the findings are intended to influence policymakers and entrepreneurs, emphasising the need to address the long-term impacts of climate change on tourism demand.