# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 5 Conclusions and Implications  
### Discussion  
This study employs directional network analysis to systematically investigate the structural attributes of transportation and economic networks across city clusters in the Greater Bay Area. The analysis encompasses spatial configurations, synergistic relationships, evolutionary trajectories, and degrees of coupling and coordination. Through this approach, the study uncovers underlying synergistic mechanisms and regional development dynamics. These findings are validated and further contextualized by existing research across key thematic domains. First, in relation to the core-periphery structure of urban networks, prior research has argued that global metropolitan systems are inherently stratified and dominated by central nodes. [PERSON] et al. [PERSON] et al. (2018), through the lens of world city network theory, proposed a \"super-node versus peripheral-node\" hierarchy, emphasizing the pivotal role of financial and professional service sectors in shaping global urban hierarchies. Building on this framework, [PERSON] et al. [PERSON] et al. (2018), using enterprise connection data, identified Hong Kong, Shenzhen, and Guangzhou as consistent \"super nodes\" within the Greater Bay Area. Aligned with these findings, this study further delineates a heterogeneous pattern of \"logistics dual-core and financial tri-core\" dominance, revealing divergent spatial command structures across functional elements of the regional network. This contributes to a deeper understanding of the multicentric division of labor mechanism within urban systems. Second, with respect to the structural characteristics and synergistic mechanisms of logistics and capital flows, [PERSON] et al. [PERSON] et al. (2018) identified morphological distinctions between regional transportation and economic networks: logistics networks emphasize broad coverage and accessibility, whereas capital flows exhibit centralized and selective patterns. This study corroborates that differentiation, revealing a core concentration within the multipolarity pattern in capital flows, is evidenced by the increasing directional dominance of the region's three core cities. This finding aligns with [PERSON] et al. [PERSON] et al. (2018), who described \"functional polycentricity with financial dominance\" in their analysis of European urban systems. Even within polycentric network structures, capital flows concentrate in a limited number of financially dominant cities, forming a core-radial configuration. The present study reinforces this pattern, illustrating how spatial financial hierarchies persist despite structural polycentricity. Third, in analyzing the evolution of network dynamics, this study tracks three key time points--2016, 2019, and 2021--to observe longitudinal trends. Findings indicate increasing structural complexity within the logistics network, heightened concentration in capital flows, and progressively strengthened bidirectional synergy between the two systems. These transformations are consistent with [PERSON] (2018) conceptualization of large urban systems transitioning from \"point interconnections\" to \"network synergies,\" marking the Greater Bay Area's shift from rudimentary connectivity toward integrated, multi-scalar coupling. Fourth, in examining the degree of coupled coordination, prior studies have frequently employed principal component analysis and coupling models to reveal intra-regional disparities. For instance, [PERSON] et al. [PERSON] et al. (2018) identified a core-periphery binary differentiation structure in logistics-capital coordination within the Beijing-Tianjin-Hebei urban agglomeration. Similarly, [PERSON] (2018), through empirical analyses of output efficiency, it illustrated the multi-complex regional relationship between centrality and productivity. This study introduces the dual network coupling model of transportation and economic linkages into the Greater Bay Area for the first time.