Artificial Intelligence for Socio-Ecological Resilience and Sustainable Resource Governance

Abstract

Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15