It is of great significance to accurately assess the carbon sink capacity and trend of forest ecosystems on a regional scale, which is a key step to realizing sustainable forest management and carbon sink. Based on several remote sensing datasets, this study analyzes the dynamic characteristics of forest carbon stock in the Yangtze River Basin and its response to environmental factors using the Mann–Kendall nonparametric test, correlation analysis, and BP neural network during 2005–2020. The results show that forest carbon stock in the Yangtze River Basin shows a fluctuating upward trend, with an average annual growth rate of 0.91%. Forest carbon stock in western high-altitude areas and areas with high human activity in the east showed a downward trend, while the central plains showed a stable growth trend. In the vast plains of the Yangtze River Basin, a suitable drought degree (−0.5 < SPEI < 0.5) is helpful to the accumulation of forest carbon reserves. In the future, rich forest resources should be fully developed to promote synergy between environmental protection and economic development from the perspective of developing green carbon trading, such as the carbon-sink forestry projects of CCER.

The evaluation and prediction of ecological benefits are significant for regional resource development planning and path designing. This study established a novel ecological benefits evaluation system by integrating macro-ecosystem structure, Ecosystem service index (ESI), and ecological quality index (EQI). Based on this system, this study evaluated the spatiotemporal characteristics and changing trend of ecological benefits in Song-Liao River Basin (SRB) from 1990 to 2020. The results show that the macro-ecosystem structure in Song-Liao River Basin remains stable, and the ecosystem service and ecological quality generally show a trend of first decline and then increase. The average growth rates of ESI and EQI were 0.6% and 0.4%, respectively, during 1990–2020. The ecological benefits of natural areas with widely distributed forest areas are higher, while those of areas with frequent human activities are lower. The prediction model based on machine learning has achieved good modeling effect, which shows that the ecological benefits of SRB will be on the rise in the future. Based on the evaluation results, we suggest that more environmental protection policies on the basis of maintaining the existing development plan should be promoted to reduce the contradiction between human and nature in the development process. For the abundant natural forests in this area, reasonable forest management should be carried out to improve the carbon-fixation capacity of vegetation, and a Methodology for managing natural forests should be constructed to make full use of the existing carbon sinks. For the new afforestation project being promoted, carbon-sink afforestation projects of CCER (Chinese Certified Emission Reduction) should be promoted to realize the synergy between economic development and environmental protection.