ORIGINAL RESEARCH
Spatial-Temporal Distribution Characteristics
and Driving Mechanism of Green Total Factor
Productivity in China’s Logistics Industry
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School of Economics and Management, Fuzhou University, Fuzhou, China
Submission date: 2020-02-27
Final revision date: 2020-04-22
Acceptance date: 2020-04-26
Online publication date: 2020-08-05
Publication date: 2020-10-05
Corresponding author
Jian Wang
School of Economics and Management, Fuzhou University, China
Pol. J. Environ. Stud. 2021;30(1):201-213
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ABSTRACT
The rapid development of China’s logistics industry is accompanied by the deterioration of the
ecological environment and excessive energy consumption. Therefore, how to effectively measure and
improve the green total factor productivity (GTFP) of the logistics industry is an important guarantee
for achieving the coordination of the logistics industry development and the ecological environment
protection in the high-quality development stage. This study evaluated the logistics industry’s GTFP
of 30 provinces in China from 2004 to 2017 using the Epsilon-based measure model (EBM) and global
Malmquist-Luenberger index (GML). Then, this paper applied the geographically and temporally
weighted regression (GTWR) to analyze the spatiotemporal non-stationarity of influences of driving
factors on GTFP. There are three main conclusions drawn in this paper. Firstly, the GTFP of the
logistics industry has significant spatial and temporal differences. From a temporal perspective, the
GTFP has undergone a process of alternating changes in ascent and descent. From a spatial perspective,
the GTFP has an obvious “east-central-west” gradient decreasing trend. Secondly, compared with the
ordinary least squares (OLS) and the geographically weighted regression (GWR), GTWR performs best
in terms of goodness of fit. Thirdly, the regression results of GTWR indicate that the influences of
factors have different directions and intensities on GTFP in the logistics industry at different times
and regions, showing obvious characteristics of spatiotemporal non-stationarity. Finally, some practical
recommendations are put forward in this paper.