Data-Driven Resource Efficiency Evaluation and Improvement of the Logistics Industry in 30 Chinese Provinces and Cities

Heping Ding, Yuxia Guo, Xue Wu, Cui Wang, Yu Zhang, Hongjun Liu, Yujia Liu, Aiyong Lin and Fagang Hu
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Heping Ding: Business School, Suzhou University, Suzhou 234000, China
Yuxia Guo: Business School, Suzhou University, Suzhou 234000, China
Xue Wu: Business School, Suzhou University, Suzhou 234000, China
Cui Wang: Business School, Suzhou University, Suzhou 234000, China
Yu Zhang: School of Civil Engineering, Central South University, Changsha 410083, China
Hongjun Liu: Business School, Suzhou University, Suzhou 234000, China
Yujia Liu: Business School, Suzhou University, Suzhou 234000, China
Aiyong Lin: Business School, Suzhou University, Suzhou 234000, China
Fagang Hu: Business School, Suzhou University, Suzhou 234000, China

Sustainability, 2022, vol. 14, issue 15, 1-23

Abstract: Improving the logistics industry’s resource efficiency (LIRE) is one of the most significant measures for ensuring sustainable development. We offer a data-driven technique for analyzing and optimizing the LIRE to improve it and achieve sustainable development. A LIRE index system is built based on relevant data gathering and a complete examination of the economy, society, and environment. The Super-EBM-Undesirable model was used to calculate the LIRE; the Global Malmquist–Luenberger index model was used to calculate the LIRE’s dynamic change characteristics, and ArcGIS and spatial autocorrelation models were used to analyze the LIRE’s spatial evolution pattern. The LIRE in 30 Chinese provinces and cities from 2011 to 2019 is used to illustrate the method implementation process. The results indicate the following: (1) The overall LIRE is low, with an average value of 0.717, and there are regional variances with a decreasing gradient pattern of “East–Northeast–Central–West”. (2) Changes in pure technical efficiency have a bigger impact in general; increasing technical efficiency is the LIRE’s principal motivator. (3) Improving the LIRE should take spatial spillover and inhibitory effects into account. This study provides theoretical and methodological support for the evaluation and optimization of the LIRE and a theoretical foundation for the logistics industry’s sustainable development (LISD).

Keywords: sustainable development; resource efficiency of the logistics industry; Super-EBM-Undesirable model; Global Malmquist–Luenberger index model; spatial autocorrelation; data-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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