Examining the influence mechanisms of gateway ports on inland ports in the context of Port regionalization - A case study of the Yangtze River Port system

This study examines the influence of gateway seaports on inland ports within a river port system, focusing on the Yangtze River port system as a case study. The research initially employs a fixed-effects panel model to confirm the overall positive impact of Shanghai Port on Yangtze River inland ports. Subsequently, using Panel data model with individual-specific slopes, this study reveal that Shanghai Port positively affects each inland port, but the magnitude of this impact varies significantly. The research further employs a panel threshold regression model to dissect the mechanisms through which Shanghai Port influences inland ports in terms of geographical, operational, and economic distances. In terms of geographical distance, the influence of Shanghai Port on the throughput of Yangtze inland ports diminishes as the navigational distance increases. Notably, inland ports located downstream on the Yangtze River are the first and most impacted, marking the downstream area as an early influencer in the port regionalization process. As to operational distance, Shanghai Port exerts a stronger influence on inland ports with higher throughput levels, as opposed to those with lower throughput. With respect to economic distance, inland ports in cities with higher economic development are found to be less influenced by Shanghai Port compared to those in less economically developed regions. This nuanced understanding provides invaluable insights into the interconnectedness of major gateway ports and their inland counterparts.

• Record URL:
  • https://doi.org/10.1016/j.tranpol.2024.08.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0967070X24002270
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/29485010
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wu, Zhen
  • 0000-0003-0618-5520
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References; Tables;
• Pagination: pp 10-19
• Serial:
  • Transport Policy
  • Volume: 157
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0967-070X
  • Serial URL: http://www.elsevier.com/locate/issn/096707X

Subject/Index Terms

• TRT Terms: Data models; Economic development; Economic growth; Gateways; River ports
• Geographic Terms: Shanghai (China); Yangtze River Region
• Subject Areas: Data and Information Technology; Economics; Freight Transportation; Marine Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01929653
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 5 2024 4:26PM