Yao, Xin, Cheng, Yuangyuang, Zhou, Li ORCID: https://orcid.org/0000-0001-7132-5935 and Song, Malin (2020) Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods. Annals of Operations Research, 308 (1-2). pp. 727-752. ISSN 0254-5330 (Print), 1572-9338 (Online) (doi:10.1007/s10479-020-03763-w)

Preview

PDF (Author's Accepted Manuscript) 29153 ZHOU_Green_Efficiency_Performance_Analysis_Of_The_Logistics_Industry_In_China_(AAM)_2020.pdf - Accepted Version Download (1MB) | Preview

Abstract

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function (NDDF) method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) In general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China.

Item Type:	Article
Additional Information:	ABS 3; SJR Q1
Uncontrolled Keywords:	logistics industry, green efficiency, non-directional distance function, functional clustering, machine learning
Subjects:	H Social Sciences > H Social Sciences (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of Systems Management & Strategy Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified:	11 Jan 2022 17:01
URI:	http://gala.gre.ac.uk/id/eprint/29153

Actions (login required)

View Item

Downloads