Abstract
A lot of ink has been shed lately on the concept of port connectivity. This is particularly true currently in view of the strength of global shipping alliances (GSA), their ability to jointly ‘manage’ the supply of tonnage, and the negative impact such power has had on the frequency of services, the number of companies calling at a port, on containership sizes, and call sizes, i.e., on port connectivity. However, connectivity alone cannot explain the importance of a port as an international hub, its attractiveness to shippers, and its ability to develop new transshipment traffic (no matter how well connected a port is in the Arctic, or in Tierra del Fuego, it will never assume hub-port status). We argue that connectivity needs to be combined with measures of centrality, as these are derived from network theory. We thus introduce the novel concept of composite connectivity: Through an innovative use of two-stage data envelopment analysis (DEA) and complex network theory, we first evaluate the efficiency of ‘basic connectivity’ and use this as input in the second stage, which measures the strength of centrality. To do so, we employ such network theory measures as betweenness centrality, closeness centrality, and eigenvector centrality. The “Composite Connectivity Index” — CCI is thus obtained as the ratio of (our measures of) port centrality to port connectivity. The top nine mainland China ports are used as a case-study. Our results (and rankings) conform to the general perception on the international importance of the ports of Shanghai, Shenzhen, and Hong Kong, thus demonstrating the validity of our model. The usefulness of CCI as a decision-support tool for ports with hub aspirations is, we believe, obvious.
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Notes
The region includes countries and territories of Northwestern Africa among which Algeria, Libya, Mauritania, Morocco, and Tunisia.
The quantity indicator is presented as an Annualized Slot Capacity (ASC) index, measured by the annual slot capacity offered by shipping lines at ports. The quality indicator is an approximate calculation using the number of shipping services and the number of destinations as variables.
Although usually a port serves a wider territory than the port city itself, the ‘city GDP’ is used here because of our emphasis on the impact of a port's city on its connectivity.
In view of the complexity of its calculation, we have used Python in the Jupyter Notebook to calculate the Eigenvector centrality of the ports. See Appendix for the details.
Space-B networks show the relationships between arcs and nodes in the network, but relationships between the nodes themselves is not clear. Space-C networks represent the relationship between arcs, but the display of node relations is ambiguous. Space-L and Space-P networks instead can clearly show the connections between nodes.
For a review of foreland and hinterland strategies of Chinese ports, particularly those in the Pearl River Delta (Hong Kong, Shenzhen, Guangzhou), see Shan Li, Hercules Haralambides and Qingcheng Zeng (forthcoming) ‘Competitive forces shaping the evolution of integrated port systems—The case of the container port system of China’s Pearl River Delta’. Research in Transportation Economics (2021).
A development policy exists between Shanghai, Zhejiang province, and two other provinces along the Yangtze River. This is known as ‘strategy for integrated development of the Yangtze River Delta’.
The index is obtained by calculating the proportion of the shortest paths through this node over all shortest paths in the network.
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Acknowledgements
We would like to be grateful to the editors and anonymous referees for their valuable comments. This paper is supported by the National Natural Science Foundation of China (No. 71974123) and Innovation Program of Shanghai Municipal Education Commission(No. 2017-01-07-00-10-E00016).
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Appendix
Appendix
The calculation code of eigenvector centrality
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Wang, C., Dou, X. & Haralambides, H. Port centrality and the Composite Connectivity Index: Introducing a new concept in assessing the attractiveness of hub ports. Marit Econ Logist 24, 67–91 (2022). https://doi.org/10.1057/s41278-022-00220-2
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DOI: https://doi.org/10.1057/s41278-022-00220-2