Urban-rural differences in the nonlinear individual and interactive effects of blue-green space landscape patterns on carbon sequestration

Against the backdrop of global climate change, optimizing landscape patterns of urban green space is widely recognized as a highly promising approach to enhancing carbon sequestration. However, most studies tend to overlook the synergistic effects of water bodies and green space patterns within urban blue-green spaces on green space carbon sequestration, while also neglecting urban-rural differences and nonlinear mechanisms. Using Beijing as a case study, this research employs Sentinel-2 remote sensing data and the Carnegie-Ames-Stanford approach (CASA) model to achieve high-precision carbon sequestration assessments. By applying random forest and SHapley Additive exPlanations (SHAP), it analyzes urban-rural differences and nonlinear dynamics of both individual and interactive effects of blue-green space patterns on green space carbon sequestration. Results indicate that in terms of the patch size, enlarging green patches smaller than 25 ha within the urban area is most effective in enhancing carbon sequestration. The patch area of blue space generally exhibits a negative correlation with carbon sequestration, but the negative effect of large water bodies exceeding 10 ha tends to plateau. The impact of green-blue space shape complexity has a fixed threshold, beyond which the mitigating effect of negative impacts shows a gradual change across urban-rural areas. Increasing the length of blue-green interfaces effectively enhances carbon sequestration within certain ranges. The different combinations of blue and green patterns have complex interactive mechanisms. These findings deepen our understanding of blue-green space synergies and provide a scientific basis for refined decision-making under diverse planning scenarios.