No abstract

The low-carbon supply chain is key to promoting sustainable development and solving environmental pollution. Government policies related to lowering carbon emissions deeply affect supply chains. This paper builds a supply chain decision-making model under three different regulatory policies: a pure carbon tax, a pure low-carbon subsidy, and a mixed policy with both a carbon tax and a low-carbon subsidy, then compares and analyzes the impacts of these three different regulatory policies on carbon emissions, manufacturer and retailer income, and marginal profit in order to determine the best course of action with respect to supply chain decision-making. Our results indicate that the supply chain decision-making model under the mixed carbon tax and low-carbon subsidy policy results in a unique Nash equilibrium solution between the retailer subsidy rate and the manufacturing carbon reduction rate in a non-cooperative game. Although a carbon tax is beneficial to the ecological environment, retailer income increases slightly as the carbon tax coefficient increases before declining rapidly. Manufacturer income has a negative linear relationship with carbon tax, and an excessive amount of carbon tax increases the burden on companies. Therefore, the government must establish reasonable standards for carbon tax collection while offering moderate low-carbon subsidies at the same time as a means of optimizing social welfare.

This study utilized panel data from 31 provinces in China from 2006 to 2020 to investigate the impact of the digital economy on the upgrading of the manufacturing industry’s global value chain. Two types of spatial weighting matrices were used to construct SAR, SEM, SAC, and SDM models. The results revealed that technological innovation plays a direct mediating role in the upgrading of the manufacturing industry, and the global value chain has a positive regulatory effect on the relationship between the digital economy and the manufacturing industry’s upgrading. Under the economic distance spatial weighting matrix, the spatial spillover effect of the digital economy on the manufacturing industry’s global value chain is not significant, whereas, under the geographic distance spatial weighting matrix, the digital economy has a positive and significant spatial spillover effect. The SDM model showed the best explanatory effect. This implies that geographic spatial dependence has a significant impact on the upgrading of the manufacturing industry’s industrial structure, and it is positively influenced by nearby provinces. Understanding the impact mechanism and spatial spillover effects of the digital economy on the manufacturing industry’s upgrading can help promote efficient, fair, and balanced regional development. It can also aid in constructing a new domestic and international “dual circulation” development pattern that evolves with the global manufacturing value chain, sharing the dividends of the digital economy’s impact on the global value chain’s development.