Using a Light Gradient-Boosting Machine–Shapley Additive Explanations Model to Evaluate the Correlation Between Urban Blue–Green Space Landscape Spatial Patterns and Carbon Sequestration

Global ecosystems are facing challenges posed by warming and excessive carbon emissions. Urban areas significantly contribute to carbon emissions, highlighting the urgent need to improve their ability to sequester carbon. While prior studies have primarily examined the carbon sequestration benefits of single green or blue spaces, the combined impact of urban blue–green spaces (UBGSs) on carbon sequestration remains underexplored. Meanwhile, the rise of machine learning provides new possibilities for assessing this nonlinear relationship. We conducted a study in the Yangzhou urban area, collecting Landsat remote sensing data and net primary productivity (NPP) data at five-year intervals from 2001 to 2021. We applied the LightGBM-SHAP model to systematically analyze the correlation between UBGSs and NPP, extracting key landscape metrics. The results indicated that landscape metrics had varying impacts on NPP. At the patch and type level, the Percentage of Landscape was significantly positively correlated with NPP in green space, while the contiguity index and fractal dimension index favored carbon sequestration under certain conditions. The contribution of blue space was lower, with some indicators exhibiting negative correlations. At the landscape level, the contagion index and aggregation index of UBGS had positive effects on NPP, while the division index and landscape shape index were negatively correlated with NPP. The results enhance the understanding of the relationship between UBGS and carbon sequestration, and provide a reference for urban planning.