## 3 Results
### AI in the Assessment and Management of Ecosystem Services
In [104], the XGBoost, Random Forest, and Naive Bayes models were applied to analyze land degradation in the Cradle of Humankind and its implications for tourism attractiveness. The study revealed that the decline in vegetation cover, habitat destruction, and the loss of aesthetic appeal pose significant threats to the sustainability of ecotourism, thereby necessitating more precise forecasting and adaptive conservation strategies. Additionally, Ref. [105] employed social media data to automate the assessment of cultural ecosystem services in national parks. AI enabled the automation of data collection and analysis, enhancing the understanding of visitor preferences while incorporating intangible values such as aesthetic appeal, recreational significance, and educational potential. User-generated content (UGC) analysis has been proposed in [106] as a method for assessing the value of nature in tourism and informing spatial planning. In [107], the InVEST model was used to evaluate the tourism attractiveness of the Basilicata-Apulia region. A related approach is presented in [2], where automated analysis of urban greenery was applied to preserve ecosystem services such as microclimate regulation and air quality improvement. The role of AI and IoT in promoting sustainable ecotourism and forecasting environmental risks is examined in [12; 108]. The former highlights the benefits of AI for generating personalized tourist routes, managing visitor flows, and minimizing environmental impacts on natural ecosystems. The latter emphasizes the potential of AI to enhance monitoring accuracy, develop early warning systems, and establish sustainable models for managing ecological tourism. System dynamics (SD) modeling combined with the STELLA decision support system (DSS) platform was utilized in [51] to conduct scenario-based assessments of tourism's impact on coastal ecosystems, aiming to balance economic development with environmental risk mitigation. Furthermore, Ref. [109] demonstrates how a hybrid intelligent (HI) system, integrating citizen science and machine learning, can be employed to monitor marine biodiversity in tourist areas and strengthen the protection of marine ecosystems. AI tools are also employed to assess natural capital and support the development of sustainable economic strategies. In [110], an artificial neural network (ANN) was applied to estimate carbon sequestration in Indian wetlands, identifying key climatic determinants such as temperature and dissolved oxygen levels. The findings contribute to the optimization of ecosystem service management, particularly by reducing methane emissions and enhancing ecological restoration efforts. In a study of ecological restoration on the Yunnan-Guizhou Plateau during the period 2000-2019, Ref. [111] utilized remote sensing techniques and the Random Forest algorithm to identify factors influencing ecosystem recovery. Machine learning enabled a quantitative assessment of restoration across three dimensions--macrostructure, quality, and ecosystem services--while revealing the primary natural and anthropogenic drivers. Bayesian learning was applied in [112] to analyze the dependence of vulnerable social groups on natural resources, providing a more effective basis for ecosystem service management strategies under conditions of limited data availability. Using AI, Ref. [13] uncovered complex interrelations among land-use change, food production, emissions, and agricultural sustainability. An AI-based system integrating deep learning and blockchain was developed in [113] to support ecosystem management in the Paramo region of the Andes. This system enables the monitoring of threats such as deforestation, mining, and agricultural pressure, facilitates data analysis, and enhances decision-making processes. In [114], a hybrid model combining AdaBoost, XGBoost, and CatBoost was proposed to estimate the gross ecosystem product (GEP) using environmental data, achieving high interpretability and robust regional-level forecasting.