## 4 Discussions  
The present scoping review underscores the accelerating importance of artificial intelligence in advancing the sustainability of regional and local ecosystems. Drawing on the analysis of 155 peer-reviewed publications, this study identifies nine functional domains in which artificial intelligence and machine learning methods are being deployed: environmental monitoring, climate-impact assessment, natural-resource optimization, smart agriculture, sustainable industry and manufacturing, urban infrastructure, logistics and mobility, health and education services, and broader socio-economic governance. Collectively, these domains reveal not only the breadth of applications but also the depth to which AI is penetrating decision-making processes that were once constrained by fragmented data streams and reactive management practices. To interpret these findings within a unifying conceptual lens, we adopt a socio-technical-systems perspective [162] in which AI serves as an intermediary layer that links biophysical feedbacks to institutional response capacity; this framing helps explain why identical algorithms may yield divergent outcomes under different regulatory, cultural or infrastructural conditions. The reviewed literature demonstrates, first and foremost, the growing sophistication with which AI is used to monitor and forecast environmental conditions. Algorithms ranging from deep convolutional networks to hybrid neuro-fuzzy systems are applied to spatial analyses of degradation processes across aquatic, forest, agricultural, and coastal ecosystems, enabling managers to pinpoint the primary drivers of change, model complex feedback loops, and design adaptive interventions that remain sensitive to regional ecological baselines [19; 22; 33]. In agricultural settings, for example, AI-enabled remote-sensing workflows now detect crop-stress signals weeks earlier than conventional field scouting, thereby reducing fertilizer and pesticide loads while safeguarding yield stability. Comparable advances are observed in coastal-zone management, where AI-integrated radar and \begin{table}
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**AI Domain/Section** & **Representative AI Techniques and Data** & **Environmental Impact** & **Economic Impact** & **Social Impact** \\ \hline Regional business and industry (3.7) & NLP/text-mining of disclosures, digital twins, LinkedIn analytics, clustering & Detection of greenwashing; industrial-process optimization \(\rightarrow\) lower emissions & Higher SME productivity and innovation; supply-chain transparency & New digital jobs; skills development; reduced regional inequality \\ \hline Smart energy and infrastructure (3.8) & NILM classifiers, PV-yield ML, micro-grid and BESS optimization, OPTIMASS biomass logistics, LSCEA-AIoT & CO\({}_{2}\) reduction; greater resource-use efficiency; higher RES penetration & Lower energy costs; reliable local grids; emergent local-energy markets & Broader energy access; community resilience; health gains from cleaner energy \\ \hline Education, health and inclusion (3.9) & LLM-based curricula, interpretable ML disease maps, IoMT + federated learning, GeoGPT & Heightened public environmental awareness and seawership & Green-skills human-capital formation; preventive-care cost savings & Inclusive education; personalized healthcare; civic engagement and social equity \\ \hline \hline \end{tabular}
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Table 2: _Cont._satellite imagery help forecast shoreline retreat under compound pressures of sea-level rise and intensified storm surges. These advances confirm rather than overturn existing knowledge about AI's diagnostic power; their novelty lies in demonstrating how widely used techniques can be operationalized at sub-national scale and integrated into everyday resource-management routines. Second, artificial intelligence is increasingly recognized as a platform technology whose impact transcends sectoral boundaries.