Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/98256
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Title: Optimal subsidies for rail containers : a bi-level programming solution
Authors: Yang, J
Luo, M 
Shi, M
Issue Date: 2020
Source: Maritime policy and management, 2020, v. 47, no. 2, p. 172-187
Abstract: High demand for containerized cargo and the low share of rail containers in China is causing heavy container truck traffic and pollution. Due to its social benefits, many provinces/municipalities provide subsidies for rail containers. These subsidies, aimed at reducing road traffic and emissions, may, however, have unexpected results if they are uncoordinated. Our objective is to illustrate the problem with uncoordinated subsidies and propose an optimal subsidy scheme. This study applies the minimum cost flow model to analyze three scenarios: no subsidy, the internalization of external costs, and uncoordinated subsidies. A bi-level programming model is developed to study the scenario of coordinated subsidies, where a hypothetical network planner minimizes the total subsidies for a given externality reduction target in the upper level and the cargo owners minimize total transportation cost in the lower level. A novel method is designed to transform the model into a simple four-step linear programming process. The model and solution method are then applied to containerized trade of Northeast Asia with Europe and that with northern Chinese inland cities. The coordinated subsidy scheme is found to use less in total subsidies, achieve better results than the uncoordinated one, and better total social welfare.
Keywords: Bi-level programming
Multimodal container
Novel solution method
Optimal subsidy
Transportation network
Publisher: Routledge, Taylor & Francis Group
Journal: Maritime policy and management 
ISSN: 0308-8839
EISSN: 1464-5254
DOI: 10.1080/03088839.2019.1690173
Rights: © 2019 Informa UK Limited, trading as Taylor & Francis Group
This is an Accepted Manuscript of an article published by Taylor & Francis in Maritime Policy & Management on 16 Nov 2019 (published online), available at: http://www.tandfonline.com/10.1080/03088839.2019.1690173.
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