LAUSR

One of the objectives of coupling coordination between the logistics industry and manufacturing industry (hereinafter referred to as “two industries coordination”) is to improve the competitiveness of the manufacturing industry. Using the slacks-based method (SBM) and coupling coordination degree model, the coupling coordination scheduling between the logistics industry and manufacturing industry is measured, and the coupling coordination measurement system of the logistics industry and manufacturing industry under low-carbon constraints is constructed. The DEA Malmquist model is used to measure the level of manufacturing upgrading, and a threshold regression model is used to study the mechanism of the two industries’ coordination on manufacturing upgrading. The empirical study found that during 2009-2019, the average coordination of the two industries in the Yangtze River Delta was 0.64, which was basically in the stage of good coordination, and the coupling level showed a gradual upward trend. From 2009 to 2019, the average total factor productivity index of the manufacturing industry was 1.23, showing an upward trend. The coordination of the two industries can promote the upgrading of the manufacturing industry, and there is an obvious inverted U-shaped relationship. There is a double threshold for industrial scale and technological innovation. When the scale of the manufacturing industry is between 79.1 billion yuan and 97.4 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater. When the R&D expenditure is less than 9.89 billion yuan, the impact of industrial coupling coordination on the production efficiency of the manufacturing industry is greater. The study found that the larger the industrial scale of the manufacturing industry is, the more conducive it is to the coordination of the two industries, and the coupling coordination does not increase with the increase in R&D investment. The manufacturing industry is mainly light industry, so the proportion of optimal assets is relatively low under low-carbon constraints. The upper limit of optimal science and technology expenditure shows that there is the problem of excessive investment of optimal science and technology funds under low-carbon constraints.