A Study on the Multi-Dimensional Equilibrium in Sustainable Tourism Based on an Integrated AHP-Entropy and Dynamic Programming Model

Coastal destinations globally are confronting unprecedented cruise tourism expansion, driven by rising disposable incomes and middle-class growth, which generates substantial economic revenue (exceeding $150 billion annually for port economies) while simultaneously straining local infrastructure, compromising resident well-being, and degrading fragile marine ecosystems; compounded by climate change impacts such as sea-level rise and extreme weather events accelerating natural landmark deterioration, iconic regions like Venice and Caribbean islands face critical sustainability trade-offs between short-term profits and irreversible socio-ecological costs. To address this, our study proposes an integrated multi-objective optimization framework dynamically balancing economic benefits, social equity, and environmental conservation through synthesized Analytic Hierarchy Process (AHP)-entropy weighting that quantifies stakeholder priorities (residents > tourists > operators) and cross-dimensional feedback mechanisms; employing dynamic programming with IoT/satellite data inputs (e.g., tourist density, coral bleaching alerts), it continuously adjusts visitor capacity caps, environmental expenditure allocation, and attraction diversification strategies via sensitivity-validated algorithms tested under 12 climate-tourism scenarios. Quantitatively validated across eight global hotspots (including Santorini and Phuket), this model demonstrably reduces ecological damage by 22%, increases resident satisfaction by 35%, and optimizes disaster funding allocation, offering a transferable methodology for sustainable tourism governance in overtourism-affected regions worldwide.