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Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment

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  • Changmin Jiang
  • Yulai Wan
  • Anming Zhang

Abstract

This paper develops a theoretical model to analyze the congestion internalization of the shipping lines, taking into account the ‘knock on’ effect (i.e. the congestion delay passed on from one port-of-call to the next port-of-call). We find that with the presence of the knock-on effect, liners will operate less in terminals, and an increase of a liner’s operation in one terminal will decrease its operation in the other. If the liners are involved in a Stackelberg competition, whether they operate more or less in a terminal under the knock-on effect depends on the comparison between the marginal congestion costs of terminals. Furthermore, we find that the coordinated profit-maximizing terminal charges are higher than both the socially optimal terminal charges and the independent profit-maximizing terminal charges. When the knock-on effect is small, the independent profit-maximizing terminal charges are set at higher levels than the socially optimal terminal charges; but when the knock-on effect is sufficiently large, this relationship may reverse. Besides, the capacity investment rules are the same for welfare-maximizing terminal operator and coordinated profit-maximizing terminal operator, while independent profit-maximizing terminal operators invest less in capacity. The larger the knock-on effect, the larger this discrepancy.

Suggested Citation

  • Changmin Jiang & Yulai Wan & Anming Zhang, 2017. "Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment," Maritime Policy & Management, Taylor & Francis Journals, vol. 44(1), pages 112-130, January.
    • Handle: RePEc:taf:marpmg:v:44:y:2017:i:1:p:112-130
    DOI: 10.1080/03088839.2016.1237783
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    9. Zheng, Shiyuan & Fu, Xiaowen & Jiang, Changmin & Ge, Ying-En, 2020. "Airline investments in exclusive airport facilities: Timing decisions under demand ambiguity," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 343-363.
    10. Wang, Junjin & Liu, Jiaguo & Wang, Fan & Yue, Xiaohang, 2021. "Blockchain technology for port logistics capability: Exclusive or sharing," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 347-392.
    11. Egor PLOTNIKOV & Aleksandr RAKHMANGULOV, 2021. "Modeling China'S Dry Port Cooperation In Supply Chains," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 16(3), pages 89-103, September.
    12. Wang, Tingsong & Tian, Xuecheng & Wang, Yadong, 2020. "Container slot allocation and dynamic pricing of time-sensitive cargoes considering port congestion and uncertain demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    13. Guo, Liquan & Ng, Adolf K.Y. & Jiang, Changmin & Long, Jiancheng, 2021. "Stepwise capacity integration in port cluster under uncertainty and congestion," Transport Policy, Elsevier, vol. 112(C), pages 94-113.
    14. Zheng, Shiyuan & Jiang, Changmin & Fu, Xiaowen, 2021. "Investment competition on dedicated terminals under demand ambiguity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).

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