The evolution and competition patterns of shipping networks under the influence of an Arctic route system

ABSTRACT

To study the impact of Arctic routes on shipping networks, this paper uses an Arctic route system (ARS) model. Based on the evolution of the ARS, an improved weighted network evolution model (the BBV model) is used to predict the evolution of shipping networks from 2017 to 2035. Then, the multiple linkage analysis method is applied to extract the significant flows in the networks, and a competition network is built. The main results are as follows: (1) Significant flows in the maritime network under the influence of the ARS show an increasing trend and then a decreasing trend during evolution process. (2) According to multiple linkage analysis, Shanghai, Busan, and Tokyo have higher positions. (3) The overall network competition decreases. (4) In terms of the competitive landscape of China’s container ports, Guangzhou has the strongest domestic competitive intensity. In addition, Chinese ports that face greater competition from foreign ports are always Shanghai and Shenzhen. One implication of analysis is that due to gradual influence of the ARS, port policy makers should implement different policies at different times. The paper also shows that some ports (such as Shanghai and Ningbo) should formulate cooperation agreements to promote ports development.

KEYWORDS:

• Arctic route system
• shipping network
• evolutionary model
• multiple linkage
• competition network

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Swarm is the name of a modeling simulation package based on open source agents, which can be used to simulate the interaction of agents and their emergent collective behaviour.

2. Japan’s current port policy is based on the concept of ‘selection and centralization’ proposed by the Transportation Policy Council on 29 January 2002 (Shinohara and Saika 2018). The designated ports are Keihin including Tokyo, Kawasaki and Yokohama. So, Tokyo here refers to Keihin.

Additional information

Funding

This work was supported by grants from the National Key Scientific Research Project, China [2016YFC142706], the National Social Science Foundation of China [19FZZB013], the National Science Foundation of China [71831002], the Program for Innovative Research Team in University, China [IRT17R13], the ‘Thirteen Five’ Key Scientific Research Project, China under Dalian Maritime University [3132016361], and the Fundamental Research Funds for the Central Universities, China [3132019307, 3132018301, 3132018304].