AI-Driven Sustainable Tourism Governance: A Multi-Agent Explainable Framework for Balancing Visitor Flow, Local Economy, and Environmental Carrying Capacity
Keywords:
sustainable tourism governance, artificial intelligence, explainable artificial intelligence, multi-agent systems, smart tourism governance, carrying capacityAbstract
Tourism's rapid global expansion has intensified governance challenges, including overtourism, environmental degradation, infrastructure pressure, unequal distribution of economic benefits, and unsustainable visitor congestion. Conventional tourism governance systems often lack the flexibility to respond effectively to real-time fluctuations in tourist flows and destination carrying capacity. Recent advances in Artificial Intelligence (AI), Explainable Artificial Intelligence (XAI), and Multi-Agent Systems (MAS) provide new opportunities to develop intelligent governance models that support sustainable destination management. This study proposes an AI-driven tourism governance framework integrating MAS, XAI, predictive analytics, and carrying-capacity optimization to improve decision-making and sustainability. The framework models tourists, government agencies, tourism businesses, environmental systems, and local communities as autonomous agents interacting within a dynamic tourism ecosystem. A mixed-method approach was adopted, combining a systematic literature review, conceptual framework design, simulation-based analysis, and governance optimization. Multi-agent simulations were used to evaluate visitor flow management, distribution of economic benefits, environmental pressures, and government responses under different governance scenarios. Explainable AI enhanced transparency, accountability, and interpretability of policy recommendations. Simulation results indicate that the proposed framework outperforms traditional governance approaches by improving environmental performance, optimizing visitor flows, strengthening governance responsiveness, and increasing system resilience. The proposed framework contributes to sustainable tourism governance by providing an integrated and explainable intelligent decision-support model with practical implications for policymakers, destination management organizations, and environmental planners.
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