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Port regionalization for agricultural commodities: Mapping exporting port hinterlands

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  • Souza, Marlon Fernandes de
  • Tisler, Trevor Ray
  • Castro, Gustavo Spadotti Amaral
  • Oliveira, Andréa Leda Ramos de

Abstract

Reductions in maritime transport costs have increased cargo displacement within countries, demanding improvements in internal logistics, mainly in developing countries. Port regionalization can be applied for impact analysis of logistics infrastructure projects, enabling the delimitation of the proposed infrastructure zone of influence for advanced economic, social, and environmental studies. This article primarily aims to test whether the proposed model can efficiently delimit port hinterlands. We built a model that finds the potential destination port according to parameterized logistics cost (considering the actual distance, slope, and spatial variation of road freight), solved by the Fast Sweeping Method algorithm for Eikonal equations. We present an example of model application in Brazil's grain export supply chain and validate the model's result by comparing it with Embrapa's model hinterlands. Then, a future scenario considering the railway project EF-170 (Ferrogrão) shows the possible change in the spatial configuration of Brazilian ports' hinterlands. Furthermore, the planned route for this railway project is entirely inside the Amazon biome, requiring special environmental attention in the proposed infrastructure's zone of influence. We conclude that the model presented adheres to the practical hinterlands and can support the impact analyses of large logistics projects, contributing to suitability analyses and proposals for mitigating the impacts caused by infrastructure.

Suggested Citation

  • Souza, Marlon Fernandes de & Tisler, Trevor Ray & Castro, Gustavo Spadotti Amaral & Oliveira, Andréa Leda Ramos de, 2023. "Port regionalization for agricultural commodities: Mapping exporting port hinterlands," Journal of Transport Geography, Elsevier, vol. 106(C).
    • Handle: RePEc:eee:jotrge:v:106:y:2023:i:c:s0966692322002290
    DOI: 10.1016/j.jtrangeo.2022.103506
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