## 5 Conclusions  
This study presents a novel integrated framework to assess and optimize the spatial balance of GSI supply and demand across large-scale urban agglomerations, using the GBA as a representative case. The main innovations and findings of the research are summarized as follows:  
* Novel Framework Design  
A comprehensive analytical framework was developed by integrating CD, CCD, and proximity network analysis. This multi-method approach enables a nuanced assessment of spatial mismatches between GSI provision and urban demand.  
* Identification of Spatial Imbalances  
Analysis of 642 districts in the GBA reveals pronounced spatial heterogeneity in GSI distribution. Central urban cores such as Guangzhou and Shenzhen exhibit under-supply due to dense populations and high impervious surface coverage, while peripheral regions like Zhaoqing and Huizhou show over-supply linked to low development intensity and ecological abundance.  
* Typological Classification of Districts  
Districts were categorized into four types based on CD and CCD metrics: (1) Coupled and Coordinated, (2) Not Coupled but Coordinated, (3) Coupled but Not Coordinated, and (4) Not Coupled and Not Coordinated. Notably, 266 districts fall into the fourth category, highlighting areas in urgent need of targeted GSI interventions.  
* Demand-Driven Clustering  
Proximity network analysis based on socio-demographic, infrastructural, economic, and flood exposure indicators identified four high-demand clusters. These clusters provide a differentiated understanding of local GSI needs and reveal systemic inequities in infrastructure allocation.  
* Reference Mapping for Planning Guidance  
Reference network maps were constructed to visually represent district similarities within each demand cluster. These maps serve as practical tools for planners, offering benchmark cases to guide equitable and adaptive GSI deployment in districts with similar characteristics.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15137271/s1](https://www.mdpi.com/article/10.3390/app15137271/s1)]([https://www.mdpi.com/article/10.3390/app15137271/s1](https://www.mdpi.com/article/10.3390/app15137271/s1)), Figure S1: Spatial distribution of GSI-related supply and demand indicators in the GBA. (a) Parks; (b) impervious surface ratio; (c) vegetation cover rate; (d) road network density; (e) waterway network density; (f) urban waterlogging points; (g) gross domestic product (GDP); (h) traffic facilities; (i) medical institutions; (j) science and education institutions; (k) historical buildings; (l) cultural relics protection units; (m) population density (female); (n) population density (under 14 years); (o) population density (over 60 years); (p) population density (less than a high school education.); Table S1: Weightings for supply-side indicators (%); Table S2: Weightings for demand-side indicators (%).  
Conceptualization, M.W. and J.Z.; methodology, J.Z., Y.C. and R.M.A.L.; software, J.Z., Y.C. and H.X.; validation, R.M.A.L.; formal analysis, M.W. and J.Z.; investigation, J.Z., Y.C. and R.M.A.L.; resources, M.W. and S.K.T.; data curation, J.Z. and R.M.A.L.; writing--original draft preparation, M.W. and S.K.T.; writing--review and editing, M.W.; visualization, J.Z., Y.C. and H.X.; supervision, M.W.; project administration, M.W.; funding acquisition, M.W. and S.K.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Guangdong Basic and Applied Basic Research Foundation, China [grant number 2023A1515030158, 2025A1515012916], Guangzhou City School (Institute) Enterprise Joint Funding Project, China [grant number 2024A030317], and the Major Program of the National Natural Science Foundation of China [grant number 62394334].  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: