ArticleEnvironmental science and pollution research international2022
Spatial-temporal evolution and influencing factors of total factor productivity in China's logistics industry under low-carbon constraints.
Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Behind the rapid development of China's logistics industry, there are problems of high energy consumption and high pollution. Under the dual constraints of resources and environment, promoting the low-carbon transformation of the logistics industry is the key to achieving sustainable development of the logistics industry. This paper applies the epsilon-based measure (EBM) model which considers undesirable output and global Malmquist-Luenberger (GML) index to measure the logistics efficiency under the low-carbon constraints of 30 provinces in China from 2005 to 2017, that is, the green total factor productivity (GTFP), and characterizes its temporal and spatial evolution characteristics through visualization and spatial analysis methods. Then, this paper uses the geographically weighted regression (GWR) model to analyze the influence of industrial agglomeration level, informatization level, foreign direct investment, logistics energy intensity, traffic network density, and technological innovation capability on the GTFP of the logistics industry. The findings of this paper show that (1) during the inspection period, the overall average GTFP of the logistics industry was 0.992, which did not reach the effective level, and the spatial differentiation showed that the average GTFP of eastern was greater than that of in central, and that of in central was greater than that of in western. (2) The GTFP of the logistics industry has experienced an alternating process of rising and falling in time, with large fluctuations. Also, in terms of spatial dimension, there is a trend that high-level areas gradually gather to the southeast, and there is significant spatial autocorrelation. (3) For the logistics industry, high-efficiency areas and high-output areas show significant spatial homogeneity. (4) The estimation results of the GWR show that the direction and intensity of the multi-dimensional driving factors on the GTFP of the logistics industry are different in different regions, showing obvious spatial non-stationary characteristics.
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