Abstract
Under the low-carbon background, with the aid of the Malmquist–Luenberger SBM (Slack-based Measure) model of unexpected output, the green total factor productivity (GTFP) of the logistics industry in Jiangsu Province, China, was measured and decomposed in this study based on the reality and experience of logistics industry development in 13 cities in three regions of Jiangsu Province in the years 2006–2018 by taking resource consumption into the input system and discharged pollutants into the output system. It is concluded that the environmental regulation (ER) has a significant positive effect on the growth of the GTFP of the logistics industry, and technological progress has become an important endogenous force that promotes the GTFP of the logistics industry in Jiangsu Province. On this basis, a dynamic GMM (Generalized method of moment) model and a Tobit model were constructed to further study the possible temporal and spatial effects of ER on the GTFP of the logistics industry. The research results reveal that the ER can exert both promoting and inhibitory effects on the GTFP of the logistics industry, and there is a temporal turning point for the effects. Besides, the effects notably differ spatially and temporally. Finally, some policies and advice for the green sustainable development of the logistics industry were proposed. For example, the government and enterprises should pay attention to the green and efficient development of the logistics industry and dynamically adjust the ER methods. They should consider the greening of both forward logistics links and reverse logistics system in the supply chain.
Highlights
In the face of the problems of global warming and gradual deterioration of the ecological environment, governments around the world have regarded low-energy consumption, low pollution, and low emissions as the main directions of future economic development strategies [1,2]
According to Models 2 and 3, under the comprehensive consideration of a low-carbon economy, environmental regulation (ER) is taken as the explained variable while labor productivity, energy productivity, per capita GDP, level of technological innovation, and degree of logistics industry agglomeration are selected as the five control variables
(1) The GFTP of the logistics industry is measured with the aid of the Malsquist–Luenberger SBM model of unexpected output
Summary
In the face of the problems of global warming and gradual deterioration of the ecological environment, governments around the world have regarded low-energy consumption, low pollution, and low emissions as the main directions of future economic development strategies [1,2]. A regression model was established by taking undesired output as the dependent variable and the influence of the GTFP of the logistics industry as an independent variable to explore the relationship between the independent variable and the dependent variable In this way, the actual situation of the development of the logistics industry in Jiangsu Province under the low-carbon background was investigated more scientifically and effectively, which provides references for the construction of ER and the formulation of green logistics development policies in China
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