Driving Factors and Spatial Temporal Heterogeneity of Low-Carbon Coupling Coordination between the Logistics Industry and Manufacturing Industry

Abstract

The low-carbon coupling coordination of the logistics industry and manufacturing industry is an ecological innovation organization that integrates economic benefits, social benefits, and ecological benefits under strict carbon emission constraints. In order to control or reduce the carbon dioxide emission of the two industries, it is very important to understand the driving factors of emission change and formulate effective carbon policy. The Yangtze River Delta has developed manufacturing clusters and a perfect logistics system. The Yangtze River Delta region is taken as an example. Firstly, the coupling coordination model is used to calculate the low-carbon coupling coordination scheduling of the region. Then, the spatiotemporal geographically time-weighted regression model (GTWR) is used to explore the spatial heterogeneity of driving factors of low-carbon coupling coordination. The empirical results show the following: the low-carbon coupling coordination in the Yangtze River Delta is at a good coordination, and each driving factor has a positive effect on the coupling coordination. From the regional city level and time change level, the regression coefficients of each driving factors are analyzed, and it is found that the impact of driving factors on low-carbon coupling is significantly different between large cities and small and medium-sized cities, and the spatial heterogeneity of driving factors is significant. Specifically, the marginal impact of human capital, technological progress, and urbanization level on the low-carbon coupling between logistics and manufacturing in the Yangtze River Delta is increasing year by year; the marginal impact of international trade, industrial policy, and foreign investment on the Yangtze River Delta is decreasing year by year; and the marginal impact of capital investment and infrastructure on the Yangtze River Delta is relatively stable. Finally, according to the heterogeneity of driving factors in cities of different sizes, the corresponding development suggestions are put forward.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

“…From the perspective of response, since transportation facilities are directly related to the layout and utilization of logistics land, this paper uses highway length and road network density to measure the impact of the response to the supply of transportation facilities on logistics land [68]. And given the fact that logistics mainly serves the manufacturing and service industries, this makes the proportion, size and distribution of industrial and commercial land in the city negligible, so this paper also borrows the proportion of industrial land and the proportion of commercial land to express their impact on logistics land [69].…”

Research on the decoupling effect and driving factors of carbon emissions in Hubei province's industry：based on the perspective of developing new quality productive forces

“…Yi and Niu established a VRA model based on data from Hubei Province from 1986 to 2019 to analyze the impact and impact of development mode and industrial structure on carbon emissions in Hubei Province 25 . Wang and Guo et alcomprehensively used the Tapio decoupling index and LMDI decomposition method to calculate the decoupling index of economic development and carbon dioxide emissions from 2000 to 2019 at the overall and inter provincial levels in China, and analyzed the driving factors of decoupling 26 .…”