Carbon emissions in the logistics industry: driving factors and decoupling effects.

The explosive growth of the logistics industry has led to an increase in energy consumption and carbon emissions. To reduce emissions and increase the efficiency of the logistics industry, we studied the driving factors and decoupling effects of carbon emissions of logistics industry (LICE). First, an energy coefficient method is used to calculate the LICE. Second, the Logarithmic Mean Divisia Index (LMDI) decomposition method is used to decompose the driving factors of LICE into five types. Third, the decoupling model is used to explore the decoupling relationship between economic growth and LICE. Considering Anhui Province as an example, this study describes the method's implementation process based on a comparison of the four Yangtze River Delta provinces and cities. The results indicate that the growth rate of LICE in Anhui Province has decreased during the research period, from 9.7% in 2013 to 2.1% in 2021; however, the Tapio decoupling elasticity has been approximately 0.4 for the last 2 years, remaining in a weak decoupling stage from economic development. The LMDI decomposition results indicate that the average contribution of economic level to carbon emissions is 1.763. This study proposes some solutions and recommendations for the logistics industry's low-carbon development to offer methodological and theoretical support for LICE research.

Data-driven evaluation and optimization of the sustainable development of the logistics industry: case study of the Yangtze River Delta in China.

In this study, a data-driven way is proposed to evaluate and optimize the sustainable development of the logistics industry (LI). Based on a comprehensive consideration of economic, societal, and environmental factors, an evaluation index system was established for the sustainable development of the logistics industry (LISD). Logistics industry-related data were collected from the Yangtze River Delta (YRD) from 2011 to 2020. The anti-entropy method was used to determine the index weight and process the data. Furthermore, the coupling harmonization degree and barrier degree models were used to analyze the coordinated development of each subsystem and identify key obstacles. Our results indicate that there are significant temporal and spatial differences in the level of LISD in YRD, with Shanghai (score 0.4834) being the best and Anhui (score 0.4553) the worst, showing a wave-like evolution in time. The coupling and coordination states among the subsystems are significantly different, with that of environmental benefits and other subsystems being poor. Moreover, innovation ability and environmental benefits are the main obstacle factors of this system. Based on the results of this study, targeted optimization countermeasures are put forward and evaluation indicators and research methods are suggested, which will provide the government and practitioners decision support, as well as provide theoretical and methodological support for LISD.