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

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