## 3 Results
### Carbon Monitoring, Vulnerability Assessment, and Ecosystem Change Forecasting Using AI  
Regional coverage: This sub-section includes 14 studies. The majority of cases pertain to Asia (\(n=9\); e.g., China and Iran), with additional case studies identified in Europe (\(n=1\); Italy). Four studies adopt a global perspective (\(n=4\)). Artificial intelligence technologies and satellite-based remote sensing have become critical tools in ensuring the accuracy and timeliness of environmental monitoring, particularly for assessing climate-related risks, carbon balance, and ecosystem resilience. The integration of AI into climate research enables the development of more reliable forecasts, a detailed analysis of spatiotemporal dynamics, and the adaptation of conservation strategies to rapidly changing environmental conditions. One of the most promising directions involves the monitoring of greenhouse gases and carbon fluxes within ecosystems. For example, Ref. [11] highlights the role of the Chinese satellite TanSat-2 in providing high-precision estimates of atmospheric concentrations of CO\({}_{2}\), CH\({}_{4}\), and other pollutants. AI is employed to integrate \"bottom-up\" (ground-based) and \"top-down\" (satellite-derived) data, enhancing the accuracy of emissions inventories in line with the Paris Agreement. In another study, Ref. [87] applies a Random Forest model to assess carbon stocks in the Yangtze River Basin from 2001 to 2021. Through machine learning methods, the researchers identified carbon distribution patterns, distinguished between climatic and anthropogenic influences, and evaluated the role of forest ecosystems in carbon sequestration. In [88], the integration of system dynamics models, patch-generating land-use simulation (PLUS), and Random Forest algorithms was employed to assess carbon sequestration by vegetation in Hubei Province, China. Climate scenario modeling under the SSP-RCP framework (CMIP6) revealed that the SSP1-1.9 scenario offers the highest potential for carbon sequestration through 2060. A significant contribution to the understanding of carbon cycle instability is presented in [89], where machine learning algorithms were applied via the Google Earth Engine platform to evaluate landslide susceptibility in Italy. The findings indicated that large-scale landslides can shift ecosystems from functioning as carbon sinks to becoming carbon sources, thereby undermining the stability of biogeochemical cycles. In [90], deep learning methods were used to enhance the estimation accuracy of gross primary productivity (GPP) by integrating outputs from CMIP6 climate models with satellite-derived data. This approach improved the precision of carbon balance projections and enabled more reliable assessment of climate change impacts on ecosystem productivity in China through the end of the 21 st century. Additionally, Ref. [91] introduced a CatBoost-based model incorporating topographic variables to improve GPP simulations in Fujian Province, China. The proposed model reduced prediction error by 16% compared to conventional approaches, contributing to the development of more robust and location-sensitive carbon monitoring systems. Artificial intelligence is increasingly employed to assess climate-related risks and ecosystem vulnerability. In [6], a Random Forest model was used to identify the drivers of drought by integrating satellite observations, climate model outputs, and reanalysis data. This approach significantly improved the accuracy of Strategic Environmental Assessment (SEA), making it more data-driven and quantitative, and thereby strengthening its role in climate adaptation planning. In Golestan Province, Iran, a study [92] utilized a maximum entropy (MaxEnt) machine learning model to assess both current and projected drought susceptibility for the period 2030-2050.