Spatial-Temporal Distribution Characteristics and Driving Mechanism of Green Total Factor Productivity in China's Logistics Industry

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.

Automated summary

This study evaluated the green total factor productivity (GTFP) of the logistics industry in 30 provinces in China from 2004 to 2017 using the Epsilon-based measure model (EBM) and global Malmquist-Luenberger index (GML), and applied geographically and temporally weighted regression (GTWR) to analyze the influences of driving factors on GTFP. The study found significant spatial and temporal differences in the GTFP, with GTWR outperforming ordinary least squares (OLS) and geographically weighted regression (GWR) in terms of goodness of fit. The regression results showed that factors have different directions and intensities on GTFP in the logistics industry at different times and regions, indicating spatiotemporal non-stationarity.