China is one of the world’s largest energy consumers and carbon emitters, and the situation of carbon emission reduction is serious. This paper forecasts the future trend of China’s carbon emissions by constructing a system dynamics model of China’s carbon emissions. The results show that China cannot fulfill its commitment to peak its carbon emissions in 2030 as scheduled. Secondly, the Logarithmic Mean Divisia Index model (LMDI) was used to analyze the influencing factors of China’s carbon emissions. The contribution rates of the five factors to China’s carbon emissions are as follows: economic development (226.30%), technological innovation (−105.92%), industrial structure (−26.55%), population scale (11.44%) and energy structure (−5.28%). Finally, this paper formulates five carbon emission reduction paths according to the size and direction of various factors that affect China’s carbon emissions. The paths of carbon emission reduction were simulated by using the system dynamics model of China’s carbon emissions. It is found that technological innovation is the key pathway for China to realize its commitment to carbon emission reduction. Slowing economic growth will delay the arrival time of peak carbon emissions and increase the intensity of carbon emissions. Optimizing the industrial structure, reducing the population scale and adjusting the energy structure can reduce the peak and carbon emissions in China, but the effect is small.

A country’s manufacturing industry is often an important route for national prosperity, but it is also a conduit by which a digital economy may become truly useful. This is so the deep integration of the digital economy and manufacturing industry can enhance independent innovation efficiencies, promote the development of advanced manufacturing clusters, and constantly spawn new models, forms of business, and industries. Consequently, it is crucial to improve China’s global manufacturing value chain. This article starts with an analysis of the development status and competitiveness of the digital economy in China and abroad. It establishes a structural equation and uses the latest data from the World Input-Output and Asian Development Bank databases. It introduces new variables, such as digitization, research and development (R&D) investment, and industrial scale to empirically analyze China’s manufacturing industry’s global value chain (GVC). The results show that the digitization of China’s manufacturing industry can increase the forward participation of GVC in the manufacturing industry to improve the division status of GVCs. Analyses suggest that due to insufficient R&D investment in the division of labor in the GVCs, China’s manufacturing industry is prone to low-end lock-in, inefficient industrial structures, and weak innovation ability. Consequently, the following suggestions are proposed: China’s manufacturing industry needs to accelerate digital transformation, increase R&D investment, actively participate in the division of labor in the GVCs, and enhance core competitiveness.

Revenue is one of the hottest topics in the field of open-source innovation. Can open-source innovation really bring more revenue to firms? What affects the revenue from open-source innovation? Based on the perspective of product homogenization and the enterprise-strength gap, these questions are answered in this study using theoretical analyses and the construction of a game model to explore the influence of product homogeneity and the strength gap between firms regarding the revenue from open-source innovation. The results show that enterprise homogeneity and the revenue from open-source innovation are not linearly related. High homogeneity does harm the revenue from open-source innovation, while the revenue is relatively high when the homogeneity is moderate. Additionally, it was also identified that the strength gap between firms has a negative influence on the revenue from open-source innovation. The wider the strength gap is, the greater the revenue loss of the weaker firms and, thus, the lower the total revenue of the two firms will be. This paper provides a reference for research on enterprise revenue from open-source innovation and the selection of participants in open-source activities.