## 3 Results
### Carbon Monitoring, Vulnerability Assessment, and Ecosystem Change Forecasting Using AI
The analysis incorporated CMIP6 climate projections and socio-economic pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5), using 14 explanatory variables, including precipitation, temperature, soil moisture, and NDVI-based vegetation indices. The model demonstratedhigh predictive accuracy and identified key high-risk areas, highlighting the compound influence of land-use change, urbanization, and climate variability on regional hydrological vulnerability. In a related study, [PERSON]. [69] employed long short-term memory (LSTM) neural networks to forecast the decline of wetlands in Iran under the influence of climatic and anthropogenic stressors. The results indicated a high likelihood of seasonal desiccation by 2058, emphasizing the urgency of integrated climate adaptation measures for water-sensitive ecosystems. Artificial intelligence is also playing an increasingly important role in environmental meteorology, particularly in analyzing the complex interrelationships between meteorological conditions and ecosystem health, as well as in forecasting natural disasters. As highlighted by [9], these capabilities are essential for supporting ecosystem resilience in a changing climate. In a recent study, [PERSON]. [93] implemented a deep neural network to model ecological deficit in Henan Province, China, using indicators such as net primary productivity (NPP), aerosol optical depth (AOD), and population density. The deep learning approach enabled accurate forecasting of ecological dynamics, providing a robust scientific foundation for sustainable regional development and monitoring anthropogenic pressures on the environment. To assess climate resilience in the Yangtze River Delta urban agglomeration, an integrated DPSIR (Driving forces-Pressures-State-Impact-Responses) framework was developed, augmented with machine learning techniques and spatial autocorrelation analysis [94]. The study employed geographically and temporally weighted regression and a super-efficiency slacks-based measure (super-SBM) model to evaluate spatial heterogeneity and improvement potential in climate resilience, offering valuable insights for evidence-based regional governance. Additionally, [PERSON]. [95] demonstrated how AI integration into life cycle assessment (LCA) frameworks can be used to evaluate the climate impact of buildings, including CO\({}_{2}\) emissions and natural resource consumption. Such approaches are critical for advancing the development of climate-neutral architecture and environmentally sustainable construction practices. Special attention has been given to the application of AI in integrating localized data into climate monitoring systems. For instance, [PERSON]. [96] introduced a spatial analysis approach based on citizen complaints, processed using natural language processing (NLP). By segmenting the text and applying geospatial tagging, the study identified areas of air pollution that closely aligned with data from official monitoring stations in Shandong Province, China. This method demonstrates the potential of AI to expand the scope of environmental surveillance and promote citizen engagement in environmental quality control. In summary, AI has proven highly effective in advancing climate monitoring and forecasting--from global carbon inventories to vulnerability assessments of local ecosystems. Its ability to process vast and multidimensional datasets enhances the precision of climate modeling and the prediction of environmental risks. Moreover, AI facilitates the integration of local and global scales of analysis, thereby supporting the development of adaptive strategies, sustainable resource management, and the fulfillment of international climate commitments. In total, according to the regional classification, the following references pertain to Asia [69; 87; 88; 90; 91; 92; 94; 96] and Europe [89].