## 5 Conclusions  
This scoping review demonstrates that artificial intelligence (AI) is no longer an experimental add-on but an increasingly indispensable instrument for advancing sustainable development at regional and local scales. Across domains as diverse as environmental monitoring, climate-risk assessment, resource optimization, infrastructure management, and social service delivery, AI has begun to reshape decision making by elevating both the spatial precision and the temporal immediacy of available information. These technical gains translate into more adaptive and--in favorable institutional contexts--more inclusive development trajectories. Yet the benefits of AI are mediated by institutional readiness, regulatory clarity, and societal trust; technological sophistication alone is not sufficient. Taken together, the studies reviewed here reveal a decisive shift from fragmented, post hoc interventions towards integrated, forward-looking governance. Well-designed AI applications widen the planning horizon, increase procedural transparency, and create novel channels for citizen participation in ecosystem stewardship. Their value is most pronounced where conventional governance tools are hampered by scarce data and limited administrative capacity. Nevertheless, the pathway from promising pilot projects to broad-based implementation remains uneven, and it is shaped as much by ethical and institutional considerations as by algorithmic performance. Within the socio-technical-systems frame adopted in the Discussion section, successful scaling will depend on the mutual alignment of technological design, regulatory oversight, and local knowledge systems. Several gaps identified in the literature deserve systematic attention. First, empirical evidence remains geographically uneven. Regions such as Central and Eastern Europe, parts of North and South America, and many post-Soviet states are still under-represented, despite distinctive land-use regimes and policy environments that could critically affect AI performance. Expanding case-study coverage to these areas would test the transferability of current insights and refine our understanding of context sensitivity. Specific gaps include the absence of longitudinal data on the social impacts of AI-enabled precision agriculture in temperate regions and the lack of explainable-AI benchmarks for watershed management in humid-tropical catchments. Second, the field would benefit from longitudinal, practice-oriented evaluations that scrutinize how AI systems perform under operational constraints. Issues of scalability, cost-effectiveness, risk resilience, and user acceptance are rarely addressed in depth, yet they determine whether experimental prototypes mature into reliable public or commercial services. Third, promising opportunities lie in combining AI with complementary paradigms such as citizen science, blockchain-enabled transparency, nature-based solutions augmented by AI, and advanced Earth-system modeling. Such interdisciplinary approaches could yield governance frameworks that remain robust under deep uncertainty while enhancing accountability and public trust. Equally important is a sustained focus on the social and ethical implications of large-scale AI deployment. Ensuring fairness, safeguarding personal data, and making algorithmic processes explainable are pre-conditions for durable societal endorsement. Research that engages vulnerable groups, measures public perceptions, and designs inclusive digital-literacy programmers will be essential for preventing the emergence of new forms of technological exclusion. Finally, the development of universal yet locally adaptable metrics for tracing the environmental and social impacts of AI represents a critical frontier. A coherent indicator framework would supply policy makers with the feedback loops necessary to recalibrate sustainability strategies in real time and to monitor long-term progress. Practical implications follow accordingly.