The Impact of Urban Green-Space Landscape Patterns on Carbon Storage: A Case Study of Shenyang, China

Urban green-space carbon sinks can contribute to reducing urban carbon emissions and supporting carbon-neutrality goals. This study examined the associations between urban green-space landscape patterns and above-ground carbon storage (AGCS) in Shenyang. The spatial distribution of estimated AGCS was mapped using ArcGIS and remotely sensed imagery. Pearson correlation analysis, geographically weighted regression, and an exploratory data-binning approach were used to evaluate associations between landscape pattern metrics (LPMs) and AGCS and to identify their thresholds. At the study-area scale, AGCS exhibited strong positive correlations with class area (CA) and largest patch index, weak negative correlations with patch density and landscape division index, and a weak positive correlation with Connectivity Index (CONNECT). These relationships varied among the three ring zones. Spatially, CA was positively associated with AGCS throughout the study area, whereas positive local coefficients for CONNECT occurred in 51.8% of the study area. The low- and high-carbon-storage thresholds were 7.26 and 10.79 for CA and 8.33 and 12.08 for CONNECT, respectively. Higher CA and CONNECT values above their low-carbon-storage thresholds were associated with a lower probability of a low-carbon-storage zone. These findings provide context-specific evidence that may inform urban green-space planning in Shenyang and offer a methodological reference for comparable case studies.