## 3 Results
### AI in the Assessment and Management of Ecosystem Services  
Regional coverage: This sub-section includes 26 studies. The majority of cases pertain to Asia (\(n=14\); e.g., India, China, Taiwan), with additional case studies identified in Africa(\(n=3\); South Africa), Europe (\(n=1\); Italy), and Latin America (\(n=2\); e.g., Colombia). Six studies adopt a global perspective (\(n=6\)). The application of artificial intelligence methods in the assessment, monitoring, and forecasting of ecosystem services (ES) presents new opportunities for sustainable management of natural areas and the maintenance of ecosystem functional stability. Recent research in this domain demonstrates the potential of AI to support automatic land zoning, spatiotemporal modeling, analysis of anthropogenic pressures, and the integration of ES into conservation planning and economic development strategies. Several studies emphasize the role of AI in spatial modeling and assessment of ES. For instance, Ref. [97] employed Self-Organizing Feature Maps (SOFM) and support vector machines (SVM) to analyze the spatial heterogeneity of ecological functions, enabling automatic delineation of zones based on ecosystem characteristics. In a related study, Ref. [10] applied AI for spatially explicit monitoring of groundwater-dependent ecosystem (GDE) services, including vegetation variation indices, which facilitated the evaluation of biodiversity potential and livelihood support capacity. Additionally, Ref. [98] used Rotation Forest (RF) models to reclassify satellite imagery and assess the impact of land-use changes on the economic value of ecosystem services. This approach enables the analysis of landscape configuration, the identification of trends in forest and pasture expansion, and the support of balanced natural resource planning at the regional level. Interpretable machine learning models combined with spatiotemporal analysis were employed in [99] to quantitatively assess ES losses--measured using the GEP indicator--caused by coal mining activities. This approach enabled the identification of industrial activity thresholds compatible with sustainable development. In a case study of Shuozhou city (China), [PERSON]. [100] demonstrated the application of a geographically weighted artificial neural network (GWANN) to evaluate the contribution of anthropogenic and climatic factors to ecosystem health, with a particular focus on the impacts of coal extraction. The use of AI facilitated a detailed analysis of spatiotemporal dependencies and helped identify zones most negatively affected by mining operations. Similarly, [PERSON]. [101] applied the XGBoost-SHAP framework to model the spatiotemporal evolution of ecosystem services in a karst region of China, revealing the key determinants of degradation and potential mitigation strategies. According to [102], artificial intelligence and remote sensing technologies provide effective tools for monitoring spatial changes in ecosystem services driven by both climate change and human activity, thus supporting the development of conservation strategies. Human-induced changes in the value of ecosystem services can also be tracked through the analysis of socioeconomic influences. For instance, Ref. [65] highlights that demographic and economic drivers are becoming dominant factors in shaping ES dynamics, necessitating adaptive management responses to ensure their preservation. In industrial and mining regions, artificial intelligence facilitates the assessment of synergies and trade-offs between environmental and economic priorities. The study by [103] explores the coordination between ecosystem services and regional development in the Qinling Mountains (China), accounting for the impacts of urbanization, tourism, and industrialization. This approach enables the identification of zones with weakened linkages, highlights regional specificities, and supports the design of context-sensitive sustainable development strategies. Special attention has been given to cultural and recreational ecosystem services.