DEA-SBM shows that all countries are efficient-related economic and environmental effects through the transport sector. This study employs two approaches: first, data envelopment analysis based on slack-based measure (DEA-SBM), and second, an advanced econometric method, i.e., cross-sectional dependence autoregressive distrib- uted lag (CS-ARDL). Furthermore, the role of transport- related climate change mitigation technology is also investigated using five inputs and three outputs for the period 2000 to 2020 for 35 OECD countries. Unlike existing studies that investigate environmental and economic impacts separately, this study analyzes the joint effect of economic and environmental factors. Thus, transport efficiency related to the economy and the environment needs to be measured. However, it drastically contributes desirable and undesirable outputs to the economy and the environment due to its rapid development. Transport sector is a vital aspect of economic and environment. (3) The decoupling analysis showed that only 3 provinces are strongly decoupled, 20 provinces are weakly decoupled, and the regional carbon emissions are quite different. The effects of the energy structure began to show a downward trend in three regions after 2015. Although the effect of the energy structure is negative, it failed the significance test. Development of the logistics industry has limited the inhibition of carbon emissions in the central and western regions.
The effect of intensity in the eastern region dropped sharply in 2015, with negative effects after that year. The effect of energy intensity has great volatility. (2) The level of economic development has the greatest impact on carbon emissions, and it has the effect of promoting carbon emissions in three regions logistics development effects have the characteristics of first driving and then restraining emissions in the three major regions. The conclusions are as follows: (1) China’s carbon emission levels vary greatly from region to region, with the highest distribution pattern in the east and the lowest in the west, while the growth rate in the east is also the highest.
This paper selected the data from 2010 to 2020 to measure the carbon emissions of the logistics industry in different regions of China, decomposed the influencing factors of carbon emissions in China’s logistics industry based on the LMDI model, and, finally, conducted a decoupling analysis of carbon emissions and the development of the logistics industry.
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