Research on the Evaluation of Regional Logistics Capacity of Gansu Province Under the “The Belt and Road” Initiative Based on Gray Correlation Analysis

Li Wang (), Zhili Wang and Changxi Ma
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Li Wang: Lanzhou Jiaotong University, School of Traffic and Transportation
Zhili Wang: Lanzhou Jiaotong University, School of Economics and Management
Changxi Ma: Lanzhou Jiaotong University, School of Traffic and Transportation

A chapter in Proceedings of the 3rd International Conference on Internet Finance and Digital Economy (ICIFDE 2023), 2023, pp 4-18 from Springer

Abstract: Abstract Gansu Province is a key construction area on “The Silk Road Economic Belt” established by Chinese “The Belt and Road” initiative. In order to evaluate the level of regional logistics capability in Gansu Province, this paper proposes a regional logistics capacity evaluation method based on the gray correlation analysis model. Based on the comprehensive analysis of the factors affecting the regional logistics development capability, the evaluation index system of regional logistics capability is constructed. The combination weighting method model is constructed by combining the analytic hierarchy process (AHP) and entropy weight method, and the weight coefficient of each evaluation index is determined by using the model. Based on the statistical data of all cities (states) in Gansu Province in 2019, combined with the gray correlation analysis model, the regional logistics capacity of 14 cities (states) in Gansu Province was comprehensively evaluated and ranked, and the ranking results were consistent with the actual situation, which verified the effectiveness of the evaluation model, and the regional logistics capacity of Gansu Province was briefly analyzed, which provides theoretical and basic support for the follow-up and in-depth study of regional logistics capacity in Gansu Province.

Keywords: “The Belt and Road” Initiative; Regional logistics capability; Gray correlation analysis; Gansu Province (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:atlecp:978-94-6463-270-5_2

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DOI: 10.2991/978-94-6463-270-5_2

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