ARIS: An IoT–Digital Twin Architecture for AI-Assisted Adaptive Management of Fragile Island Tourism Ecosystems

The ARIS paper makes four significant research contributions:
1. Closed-Loop Governance Architecture It proposes the first IoT–Digital Twin–AI pipeline that converts real-time ecological sensing into adaptive tourism permit decisions (Sensor → Data → Twin → AI → AGCI → Governance), moving beyond static dashboards to actionable governance.
2. Socio-Ecological Digital Twin Unlike conventional environmental twins, ARIS deploys three coupled sub-twins—Ecological, Tourism (agent-based), and Governance—creating a bidirectional feedback loop where tourism pressure is modelled as an endogenous system variable rather than external forcing.
3. AGCI as a Computational Governance Index The Adaptive Governance Capacity Index mathematically operationalises ecological state (E, R) against tourism pressure (I) and seasonal stress (S) to compute daily adaptive permit ceilings, replacing fixed carrying capacity with dynamic, data-driven thresholds.
4. Human-in-the-Loop AI Decision Support The AI layer (prediction, scenario, optimisation, recommendation agents) is explicitly designed as decision-support, not decision-authority —ensuring algorithmic transparency and institutional human oversight, which is critical for governance ethics in developing-country contexts.
The proof-of-concept demonstrates that under high tourism pressure (1,200/day), AGCI declines from 0.35 to 0.18 within 45 simulated days, triggering progressive restrictions—validating the framework’s capacity to prevent ecological overload without requiring permanent bans.