Abstract

Urban green infrastructure (GI), a pivotal element of urban ecosystems, enhances carbon sequestration and sustainability. However, current research has not adequately addressed changes in the spatial pattern of GI and their implications for future carbon sequestration benefits. This study focuses on Hangzhou’s main urban areas, analyzing the GI’s spatial pattern from 2002 to 2020. Utilizing climate data provided for two future scenarios (SSP126-SSP370lu and SSP370-SSP126lu) by CMIP6, predictions up to 2060 were made using a backpropagation neural network. Gross primary productivity (GPP) was employed to assess carbon sequestration benefits. The impact of the GI spatial pattern on GPP from 2002 to 2060 was examined through a spatiotemporal geographically weighted regression model. Sensitivity analysis and Geodetector were used to evaluate the uncertainty and interactive effects of changes in the GI spatial pattern on GPP. The findings suggest that under the SSP126- SSP370lu scenario, a decrease in GI area and increased fragmentation by 2060 could reduce average GPP to 0.592 gC/m2. Under the SSP370- SSP126lu scenario, an increase in GI area and enhanced compactness will increase the average GPP to 0.641 gC/m2. The GI spatial pattern significantly boosts GPP yet exhibits complex fluctuations in future scenarios, particularly regarding GI area, number, and density. A comprehensive consideration of various factors and their optimal control can effectively enhance the explanatory power of the GI spatial pattern on GPP. Based on these results, this research proposes optimized strategies for the GI spatial pattern under both future scenarios, providing scientific evidence and data support for urban planners to enhance urban carbon sequestration benefits and sustainability through optimized GI layouts.

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