Environmental Sustainability Indicators and International Tourism Demand: Evidence from Machine Learning and SHAP Analysis
Tourism and Hospitality, cilt.7, sa.6, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 7 Sayı: 6
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/tourhosp7060170
- Dergi Adı: Tourism and Hospitality
- Derginin Tarandığı İndeksler: Scopus, ABI/INFORM, Hospitality & Tourism Complete, Hospitality & Tourism Index, Directory of Open Access Journals
- Anahtar Kelimeler: ecological carrying capacity, environmental vulnerability, machine learning, macroeconomic determinants, SHAP analysis, tourism demand
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
This study evaluates the demand dynamics of the 20 leading strategic destinations in the global tourism market by modeling the interactions between traditional macroeconomic determinants and climate-linked environmental sustainability indicators. The primary objective is to assess the predictive capacity of physical and structural environmental factors—including water stress, air pollution, renewable energy adoption, and sanitation infrastructure—relative to established economic metrics like GDP per capita. Employing non-parametric predictive frameworks on a panel dataset of 400 observations (2000–2019), the empirical analysis suggests that tree-based ensemble models, notably Extra Trees (90.54%) and CatBoost (84.75%), yield higher predictive accuracy than conventional multiple linear regression (73.97%). Interpretations derived from cooperative game theory via SHAP analysis suggest that environmental determinants may serve as important predictive drivers of tourism demand. Specifically, variables such as water stress (28.20%), renewable energy share (27.12%), and sanitation infrastructure carry substantial predictive weight, whereas the benchmark macroeconomic indicator (2.30%) exerts a relatively marginal influence within the model architecture. These findings imply that environmental sustainability metrics may capture international tourism demand variations more effectively than traditional economic variables. The results suggest that acute environmental vulnerabilities may be associated with reduced tourism inflows, potentially reflecting limitations in destination sustainability thresholds. Broadly, the evidence is consistent with the notion that contemporary global tourism demand may be increasingly interdependent with ecological resilience and low-carbon transition policies. It is important to note that the findings reported here reflect predictive associations derived from machine learning models and should not be interpreted as evidence of causal relationships.