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In this study, we constructed a tripartite evolutionary game model for a large-scale enterprise (A), a small-scale enterprise (B), and a government, based on finite rationality and information asymmetry. First, we investigated and analyzed the stakeholders of low-carbon technology innovation (LCTI) and constructed a tripartite evolutionary game model. Then, we analyzed the conditions for the stability of different equilibrium points. Finally, we carried out numerical simulations using MATLAB 2020b to analyze the evolutionary processes and patterns of the different parameters. We found that the government’s strategic choice is always to participate in LCTI, which is conducive to a healthy LCTI environment. Governments can participate in LCTI consortiums through policy-driven reward and punishment mechanisms. Our simulation demonstrated that the size of enterprises influences their choice of LCTI strategy, and government reward and punishment mechanisms influence both large and small firms. Large firms are more prepared to accept the risks of LCTI and maintain a reciprocal LCTI strategy even in a high-cost scenario, while small firms are more sensitive to the costs and benefits. Thus, government reward and punishment mechanisms should take full account of small-scale enterprises. We propose a sound reward and punishment mechanism for LCTI that limits the opportunistic behavior of enterprises. Therefore, this paper is a complement to theories such as innovation systems and provides new thinking for low-carbon technology innovation cooperation of enterprises. Meanwhile, the reward and punishment mechanism proposed in this paper has important practical value for the government.

The vulnerability patch R&D has become an important part of information security governance. An effective collaboration with software vendors in patch R&D is of great significance to reduce the existence time of information security risks. This works aims to explore the relationship between vulnerability information disclosure and patch R&D of software vendors. The data regarding the vulnerability and software vendors is gathered from third-party vulnerability sharing platforms, including (China's national information security vulnerability database, CNNVD) and Tianyacha.com. Based on the theory of organizational information processing, linear regression model and Cox proportional risk regression model are built for appropriately addressing the research questions. The results show that the vulnerability disclosure of the third-party sharing platform can improve the patch R&D probability of software vendors. The information processing requirements, such as vulnerability information attention, vulnerability score and whether vulnerabilities are disclosed in advance accelerate the vulnerability patch R&D. The enterprise information processing capability indicators, including the industry dependence of software product customers and the staff size of software vendors accelerate the patch R&D. The number of products affected by the vulnerabilities and the number of software copyrights of software vendors have no significant impact on patch R&D. [ABSTRACT FROM AUTHOR]