Research on economic benefits of multi-city logistics development based on data-driven analysis

Zilong Zhuang, Siqin Fu, Shulin Lan, Hang Yu, Chen Yang, George Q. Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

This study aims to measure the logistics economic efficiency of major first-tier cities in China and propose the interactive development plan of the green logistics industry based on the division of the urban agglomeration. In this study, the evaluation system for the logistics input and economic output of urban agglomerations is firstly established based on data-driven analytics, and a green logistics economic efficiency model using the dataset from 2008 to 2017 is constructed to estimate the input–output efficiency, development trend, and spatial differentiation of urban agglomerations comprehensively. Finally, the Shapley value method is adopted to obtain the specific distribution plan of logistics investment. The results show that the logistics economic efficiency of the 9 major urban agglomerations is all greater than 1 under the constant returns-to-scale (CRS) hypothesis while the average logistics economic efficiency of Pearl River Delta region, Chengdu-Chongqing region, and Shandong Peninsula region is significantly less than 1 under the medium returns-to-scale (MRS) hypothesis. The comprehensive input–output efficiency of five out of 9 major Chinese urban agglomerations showed a downward trend, with the highest declining rate of 5.9% in the Yangtze River Delta urban agglomeration. The urban agglomeration with the highest increase rate in input–output efficiency from 2013 to 2017 is the Chengdu-Chongqing region, which reached 3.97%.

Original languageEnglish
Article number101322
JournalAdvanced Engineering Informatics
Volume49
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Keywords

  • Data envelopment analysis
  • Logistics economic efficiency
  • Merger benefit
  • Urban agglomeration

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence

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