Evaluation of Carbon Sequestration of Restored Degraded Lakeside Wetlands Around Chaohu Lake Based on GIS and Machine Learning

With the acceleration of global urbanization and intensified agricultural activities, approximately 61% of the world’s wetlands have degraded over recent decades, significantly weakening their carbon sequestration capacity. The Shibalianwei Wetland, a crucial tributary system of Lake Chaohu in China, has suffered severe degradation due to land use and cover change, nutrient loading and hydrological disruption. In response, large-scale ecological restoration has been implemented since 2018. To quantify the restoration outcomes, this study integrated remote sensing, GIS, and machine learning techniques, employing the XGBoost model to evaluate and predict carbon sequestration in 2017 and 2024 based on 2010 carbon data. The results reveal that the average carbon density increased from 48.70 t ha −1 in 2017 to 90.18 t ha −1 in 2024, representing an overall increase of 85.2% in total carbon storage. This substantial enhancement is primarily attributed to land use transitions and ecosystem-scale restoration effects, including vegetation recovery and hydrological rehabilitation. Model validation indicated moderate prediction errors (RMSE = 0.47–0.74), with consistent performance across repeated iterations. Together with complementary MAE and R 2 metrics, the results suggest that the XGBoost model is capable of capturing relative spatial patterns and restoration-induced changes in wetland carbon sequestration, while retaining reasonable predictive stability under changing landscape conditions. Overall, the findings demonstrate that large-scale wetland restoration can rapidly and effectively enhance regional carbon sink capacity and highlight the potential of data-driven modeling frameworks to support wetland management and carbon-neutrality strategies. This provides important guidance for policymakers to promote sustainable land use and optimize ecosystem management under China’s dual-carbon development goals.