With the emergence of a large number of waste electronic products and the enhancement of social awareness of environmental protection, the recycling of waste electronic products has become one of the pressing issues of social concern. Government environmental regulation is an important policy to promote the development of the remanufacturing industry. In this paper, we study the government levies recycling and disposal fees on original products for environmental governance and establish two game models based on the perspective of maximizing social welfare with no government regulation and a tripartite liability system. The optimal decisions on wholesale, retail prices and quantity of original and remanufactured products, as well as the recycling and disposal fee are analyzed under both models. Based on the numerical results, the impact of the main parameter (such as the responsibility sharing ratio) on the decisions and profits of the parties is discussed. The results show that (1) the wholesale and retail prices of remanufactured products are not affected by government regulation; (2) the tripartite liability system can increase the output of remanufactured products and reduce the output of original products while cutting the profits of remanufacturing supply chain members, and increasing social welfare; (3) government’s optimal recycling and disposal fee is not related to the sharing ratio. The study can provide practitioners with suggestions for ways to develop environmental regulation.

Confronted with the constraints of global carbon reduction mandates and the widespread implementation of cap-and-trade (CAT) policy, competing manufacturers face critical choices in carbon quota trading, such as engaging in external markets or internal agreements. We develop a duopolistic game model comprising a low-carbon manufacturer (M_G) and a traditional manufacturer (M_T) under a CAT framework. In a perfect carbon quota trading market, manufacturers simultaneously cooperate and compete, facing a strategic choice between external trading through the open carbon market and internal trading agreements. We investigate how the low-carbon development level, carbon quota surplus, and internal carbon price affect their choices of carbon quota trading modes. Analytical results indicate that in the scenario where M_G’s quota surplus is insufficient to fully meet M_T’s demand, both manufacturers can achieve Pareto improvement in their respective profits within a certain range of internal carbon prices. Otherwise, the internal trading agreements may only guarantee an increase in their aggregate profits. A numerical analysis based on the actual situation of China’s steel industry verifies the theoretical conclusions.

The efficiency of scientific and technological achievement transformation is constrained by supply–demand matching challenges. Concurrently, Artificial Intelligence (AI) offers novel pathways for digital-intelligence service platforms to mitigate this challenge. To resolve AI investment decision problems of such platforms, this study constructs a bilateral matching model involving high-quality/low-quality technology providers and high-capability/low-capability technology seekers. Based on expected value theory and Stackelberg games, it derives optimal AI investment strategies for the Commercial Platform (platform’s expected revenue maximisation objective) and the Public Welfare Platform (social expected revenue maximisation objective). Findings indicate that higher AI investment contributes to a rise in the matching probability between high-quality providers and high-capability demanders. Owing to incomplete benefit internalization, platforms of different types show divergent willingness for AI investment. The AI investment level of the Commercial Platform is lower than that of the Public Welfare Platform, which results in losses of expected matching value. Furthermore, declines in AI technology costs and reduced external selection value of suppliers will drive platforms to raise their AI investment intensity. This research provides theoretical foundations for optimising AI strategies in online technology transfer service platforms and informing targeted government interventions.