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.

Analyzing the evolution law of carbon emissions is particularly important for the designation of policies on energy conservation and emission reduction. Based on the regional division of China, this paper uses a spatial panel model to find the causes of the differences in carbon emission, and the non-parametric model, logarithmic mean Divisia index (LMDI) model and the extended STIRPAT model to analyze the relevant influencing factors in detail. From the studies in this paper, there come the following conclusions: (1) The environmental Kuznets curve (EKC) in the eastern region resembles the national EKC, demonstrating the same “N” pattern. However, the “upside-down U” pattern in the middle and western regions not only confirms the assumption of EKC in some Chinese regions but also demonstrates the effective restraint in high energy consumption and high emission levels when narrowing down the gaps between the central and western regions and the eastern regions. (2) In addition, good education can effectively suppress the increase in carbon emission, and every 1% increase in the proportion of educated people (college and above) results in emission reduction, respectively, by 0.22%, 0.51% and 0.44% in the eastern, central and western regions of China. (3) Significantly, the effect of tertiary industry structure on carbon emissions is positive, reflecting the trend of “deterioration” of China’s industrial structure over long time scales. This study functions positively in understanding the evolutionary pattern of regional carbon emissions and proposing differentiated policies on emission reduction.