Constructing a Dual-Functional Green Infrastructure Network for Synergistic Carbon Sink and Bird Conservation

As global climate change intensifies and urbanization accelerates, ecosystems face increasing pressure on carbon sequestration and biodiversity conservation. However, most studies focus on synergies between these functions while neglecting their complex trade-offs. Therefore, identifying integrated approaches that simultaneously enhance carbon sequestration and biodiversity conservation within multi-objective spatial optimization has become an urgent research priority. Using the Hangzhou metropolitan area as a case study, regional carbon sinks and bird habitats were quantified using the Carnegie–Ames–Stanford approach (CASA) and MaxEnt models. Core areas were identified using Morphological Spatial Pattern Analysis (MSPA) to extract carbon sink and bird habitat hubs, and resistance surfaces were constructed using the Analytic Hierarchy Process (AHP) and habitat suitability inversion. Circuit theory was then applied to construct carbon sink and bird habitat Green Infrastructure Networks (GINs), which were coupled to form a multi-functional carbon–bird habitat GIN. The network was evaluated for connectivity and robustness, and community detection was applied to explore its structure and inform ecological management. The results identified 133 carbon sequestration corridors and 171 bird habitat corridors. Coupling the two GINs produced a multi-functional network with dual carbon–bird habitat synergies, including 34 intersection points and 68 composite hubs. Unlike previous studies focused on single-function GINs, this study emphasizes synergy among multiple ecological functions. The findings indicate that the multi-functional GIN reduces uncertainty in multi-objective optimization, shifting GIN planning from single-objective management to coordinated multi-objective governance. This approach increases structural redundancy and functional resilience, thereby enhancing overall network robustness and connectivity.