# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis
### The Macro State of City Clusters in the Greater Bay Area
#### 4.3.2 Regional Capital Flow Advantage Linkages  
In the capital flow network, the link from Shenzhen to Guangzhou consistently exhibits the highest flow intensity, recorded at 15.794% in 2019 and 15.657% in 2021. This underscores Shenzhen's prominent position as a key output center for capital resources. Additionally, the flow intensities from Hong Kong to Guangzhou and Shenzhen to Hong Kong are also noteworthy, highlighting Hong Kong's central role in attracting capital flows and its significance within the regional financial system. The increasing intensity of the Shenzhen \(\rightarrow\) Guangzhou flow (13.947%, 15.794%, and 15.657%) reflects Shenzhen's emergence as a regional economic engine, characterized by its status as a hub for innovation and industrial development. This growth is accompanied by capital spillover effects, contributing to the phenomenon of regional capital return. From 2016 to 2021, capital flows have gradually concentrated in the two core cities of Shenzhen and Hong Kong. Shenzhen, in particular, has demonstrated a significantly stronger role in capital export, maintaining a consistently high share in the direction of Shenzhen \(\rightarrow\) Guangzhou and Shenzhen \(\rightarrow\) Hong Kong. This indicates a clear pattern of dual-core driving within the network. Secondary cities are becoming more connected, such as Dongguan \(\rightarrow\) Guangzhou, where the intensity of financial flows is gradually decreasing, but still maintains an important position in the regional financial flow system. Overall, the capital flow network exhibits a pronounced dual-core trend centered on Shenzhen and Hong Kong. Compared to the logistics network, the coverage of capital flows is broader. Smaller cities, like Foshan and Dongguan, are showing some growth in capital absorption capacity, indicating a spatial dynamic characterized by both centralization and multipolarization, as illustrated in Table 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**2016** & \multicolumn{2}{c}{**2019**} & \multicolumn{2}{c}{**2021**} \\ \hline
**Flow Direction** & **Flow Intensity** & **Flow Direction** & **Flow Intensity** & **Flow Direction** & **Flow Intensity** \\ \hline (Hong Kong \(\rightarrow\) Shenzhen) & 31.699\% & (Hong Kong \(\rightarrow\) Shenzhen) & 26.080\% & (Hong Kong \(\rightarrow\) Shenzhen) & 46.178\% \\ (Shenzhen \(\rightarrow\) Hong Kong) & 10.904\% & (Guangzhou \(\rightarrow\) Hong Kong) & 8.211\% & (Guangzhou \(\rightarrow\) Shenzhen) & 8.418\% \\ (Guangzhou \(\rightarrow\) Kong Kong) & 6.489\% & (Shenzhen \(\rightarrow\) Hong Kong) & 5.074\% & (Shenzhen \(\rightarrow\) Hong Kong) & 3.981\% \\ (Foshan \(\rightarrow\) Shenzhen) & 2.098\% & (Foshan \(\rightarrow\) Guangzhou) & 2.357\% & (Macao \(\rightarrow\) Foshan) & 1.320\% \\ (Zhongshan \(\rightarrow\) Shenzhen) & 1.500\% & (Dongguan \(\rightarrow\) Zhuhai) & 0.929\% & (Foshan \(\rightarrow\) Guangzhou) & 0.629\% \\ (Dongguan \(\rightarrow\) Guangzhou) & 0.610\% & (Zhuhai \(\rightarrow\) Hong Kong) & 0.903\% & (Dongguan \(\rightarrow\) Guangzhou) & 0.615\% \\ (Macao \(\rightarrow\) Hong Kong) & 0.342\% & (Macao \(\rightarrow\) Shenzhen) & 0.701\% & (Zhuhai \(\rightarrow\) Hong Kong) & 0.574\% \\ (Zhuhai \(\rightarrow\) Hong Kong) & 0.122\% & (Zhongshan \(\rightarrow\) Guangzhou) & 0.394\% & (Hizhou \(\rightarrow\) Hong Kong) & 0.034\% \\ (Zhuong \(\rightarrow\) Guangzhou) & 0.012\% & (Jiangmen \(\rightarrow\) Hong Kong) & 0.114\% & (Zhongshan \(\rightarrow\) Macao) & 0.027\% \\ Huizhou & - & (Zhuoglu \(\rightarrow\) Zhuhai) & 0.061\% & (Jiangmen \(\rightarrow\) Dongguan) & 0.014\% \\ Jiangmen & - & (Huizhou \(\rightarrow\) Foshan) & 0.053\% & (Zhuqing \(\rightarrow\) Hong Kong) & 0.014\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: City dominance flows in the logistics network. Focusing on the direction and intensity of factor flows, the commonality of the logistics and capital flow networks is reflected in the stability of their core linkage directions.