## 4 Discussion
### Limitation and Future Research Perspectives  
This study employed a coupled coordination model to quantitatively assess the spatial balance between GSI supply and demand in the GBA. While the research focuses on ecohydrological processes and their role in mitigating flood risks, the broader multifunctional benefits of GSI--particularly cultural ecosystem services--remain underexplored. These include improvements in air quality, reductions in urban heat island effects, and the provision of recreational green spaces that promote physical and mental health. Such benefits are especially critical in densely populated urban districts with limited access to public green areas and heightened social vulnerability. GSI systems, by incorporating vegetation, wetlands, and other nature-based features, not only enhance flood resilience but also support broader ecological and social objectives. These include fostering community cohesion, reducing stress, and contributing to long-term public health. Despite this, the current study does not offer a systematic evaluation of these intangible benefits or their implications for social equity. Furthermore, although interactions among ecohydrological variables are modeled, the potential of GSI to support cultural engagement, well-being, and climate adaptation is insufficiently addressed [17]. Future research should consider integrating these dimensions to provide a more comprehensive understanding of GSI's contribution to sustainable urban development [62; 63]. While traditional GIS methods--such as buffer analysis and spatial overlay--are valuable for identifying physical proximity among land use features (e.g., roads, hydrological networks, parks, and heritage sites), this study adopts a more systemic approach to evaluate the functional interdependencies between GSI supply and demand. Specifically, the use of CD and CCD metrics enables the analysis of internal subsystem dynamics and external system alignment, capturing performance beyond spatial adjacency. This approach allows for a district-level understanding of GSI mismatches that accounts for both interaction intensity and alignment quality across biophysical and socio-hydrological indicators. Moreover, the integration of entropy weighting and AHP-based expert judgment supports a more nuanced synthesis of heterogeneous spatial indicators, accounting for variations in data reliability and contextual importance. Proximity network clustering, based on RCA scores, further refines the analysis by identifying groups of districts with disproportionately high demand relative to the regional average--an insight that standard buffer approaches may overlook. Nevertheless, foundational GIS processing--such as land cover classification, spatial joins, and raster-to-vector conversion--was essential in the construction of these indicators and remains integral to the model's spatial logic. In addition, this study assumes relatively limited spatial variation in precipitation intensity across the GBA, based on its shared subtropical monsoon climate and regionally consistent exposure to extreme rainfall events. This assumption enables inter-district comparisons focused on GSI supply-demand dynamics, but also introduces constraints by excluding precipitation-specific variables. In practice, the temporal structure and intensity of rainfall, existing storm sewer network capacity, and the presence and distribution of stormwater reservoirs or detention basins are key factors influencing real-time flood behavior. The exclusion of these variables limits the study's ability to model hydrodynamic performance and drainage efficiency under actual storm conditions. The absence of hydraulic modeling is largely attributable to the lack of high-resolution, publicly available data on underground drainage infrastructure across the GBA. However, the integration of such data--when accessible--would significantly enhance the analytical precision and operational value of GSI planning tools.