Xiao Liu

The low-carbon development (LCD) of energy markets not only serves as a critical enabler in combating global climate change and advancing the green economy but also enhances global industrial competitiveness. Grounded in prospect theory, this study develops a tripartite evolutionary game model involving three core energy market stakeholders, i.e., energy enterprises, the public, and the government, to investigate the determinant factors and decision-making mechanisms underlying the LCD of energy enterprises, with subsequent simulation analyses conducted through MATLAB R2024a. The research findings indicate that loss aversion serves as the primary driver for energy enterprises’ adoption of LCD strategies. Public supervision demonstrates optimal effectiveness only under conditions of low risk and low loss, while risk sensitivity remains the dominant factor influencing the government’s strategic choices. Notably, government incentives combined with public supervision demonstrate significant synergistic effects in accelerating the corporate transition toward LCD. Accordingly, the government should actively promote LCD strategies to mitigate transformation risks for energy enterprises while concurrently optimizing regulatory frameworks to reduce public supervision costs and amplify incentive benefits, thereby fostering active public participation in LCD.

The mechanism of cooperative innovation (CI) for high-tech firms aims to improve their technological innovation performance. It is the effective integration of the internal and external innovation resources of these firms, along with the simultaneous reduction in the uncertainty of technological innovation and the maintenance of the comparative advantage of the firms in the competition. This study used 322 high-tech firms as our sample, which were located in 33 national innovation demonstration bases identified by the Chinese government. We implemented a multiple linear regression to test the impact of CI conducted by these high-tech firms at the level of their technological innovation performance. In addition, the study further examined the moderating effect of two boundary conditions—big data capabilities and policy support (PS)—on the main hypotheses. Our study found that high-tech firms carrying out CI can effectively improve their technological innovation performance, with big data capabilities and PS significantly enhancing the degree of this influence. The study reveals the intrinsic mechanism of the impact of CI on the technological innovation performance of high-tech firms, which, to a certain extent, expands the application context of CI and enriches the research perspective on the impact of CI on the innovation performance of firms. At the same time, the findings provide insight for how high-tech firms in the digital era can make reasonable use of data empowerment in the process of CI to achieve improved technological innovation performance.