CO2 emissions from China accounted for 27 per cent of global emisions in 2019. More than one third of China's CO2 emissions come from the thermal electricity and heating sector. Unfortunately, this area has received limited academic attention. This research aims to find the key drivers of CO2 emissions in the thermal electricity and heating sector, as well as investigating how energy policies affect those drivers. We use data from 2007 to 2018 to decompose the drivers of CO2 emissions into four types, namely: energy structure; energy intensity; input-output structure; and the demand for electricity and heating. We find that the demand for electricity and heating is the main driver of the increase in CO2 emissions, and energy intensity has a slight effect on increasing carbon emissions. Improving the input-output structure can significantly help to reduce CO2 emissions, but optimising the energy structure only has a limited influence. This study complements the existing literature and finds that the continuous upgrading of power generation technology is less effective at reducing emissions and needs to be accompanied by the market reform of thermal power prices. Second, this study extends the research on CO2 emissions and enriches the application of the IO-SDA method. In terms of policy implications, we suggest that energy policies should be more flexible and adaptive to the varying socio-economic conditions in different cities and provinces in China. Accelerating the market-oriented reforms with regard to electricity pricing is also important if the benefits of technology upgrading and innovation are to be realised.

Green technology innovation (GTI) requires a large capital investment, while the role of these capital investments in promoting GTI needs to be further confirmed. To improve GTI, universityindustry alliances (U-Is) in green innovation ecosystems engage in knowledge-sharing behaviors and form different knowledge-sharing strategies based on changes in cooperation modes. Employing a differential game, this study explores the utility of multichannel funding for innovation revenues in different cooperative modes of U-Is and the impact on revenue distributions. This article considers three game models in five cases: Nash noncooperative game with no multichannel funding, Nash noncooperative game with external funding but no government subsidies, Nash noncooperative game with multichannel funding, Stackelberg game, and cooperative game. Solving the game model and applying the numerical analysis results in certain interesting conclusions. Our research finds that, first, in the cooperative game, the strongest willingness to share knowledge occurs in the university-industry alliance, in which the total revenues of both parties reach the Pareto optimum. Second, multichannel funding can serve as an incentive mechanism for enterprises and universities to improve the knowledge-sharing willingness, the GTI level, and the revenues of the two players, while the utility of the multichannel funding is strongest in the cooperative game. In addition, in the Stackelberg game, enterprises share subsidies with universities, which stimulates their willingness to share knowledge, and both parties' revenues are better than they are in the three cases of noncooperation. Eventually, the revenue-sharing ratio of the enterprise has a smaller threshold, and the university can share more benefits relative to the absence of the multichannel funding, which helps balance the U-I in the green innovation ecosystem. These conclusions make a substantial contribution to the selection of cooperation modes and the formulation of revenues distribution contracts in university-industry alliances.INDEX TERMS University-industry; multichannel funding; green technology innovation; knowledge sharing; green innovation ecosystem

Purpose With the evolution of the turbulent environment constantly triggering the emergence of a trust crisis between organizations, how can university–industry (U–I) alliances respond to the trust crisis when conducting green technology innovation (GTI) activities? This paper aims to address this issue. Design/methodology/approach The authors examined the process of trust crisis damage, including trust first suffering instantaneous impair as well as subsequently indirectly affecting GTI level, and ultimately hurting the profitability of green innovations. In this paper, a piecewise deterministic dynamic model is deployed to portray the trust and the GTI levels in GTI activities of U–I alliances. Findings The authors analyze the equilibrium results under decentralized and centralized decision-making modes to obtain the following conclusions: Trust levels are affected by a combination of hazard and damage (short and long term) rates, shifting from steady growth to decline in the presence of low hazard and damage rates. However, the GTI level has been growing steadily. It is essential to consider factors such as the hazard rate, the damage rate in the short and long terms, and the change in marginal profit in determining whether to pursue an efficiency- or recovery-friendly strategy in the face of a trust crisis. The authors found that two approaches can mitigate trust crisis losses: implementing a centralized decision-making mode (i.e. shared governance) and reducing pre-crisis trust-building investments. This study offers several insights for businesses and academics to respond to a trust crisis. Research limitations/implications The present research can be extended in several directions. Instead of distinguishing attribution of trust crisis, the authors use hazard rate, short- and long-term damage rates and change in marginal profitability to distinguish the scale of trust crises. Future scholars can further add an attribution approach to enrich the classification of trust crises. Moreover, the authors only consider trust crises because of unexpected events in a turbulent environment; in fact, a trust crisis may also be a plateauing process, yet the authors do not study this situation. Practical implications First, the authors explore what factors affect the level of trust and the level of GTI when a trust crisis occurs. Second, the authors provide guidelines on how businesses and academics can coordinate their trust-building and GTI efforts when faced with a trust crisis in a turbulent environment. Originality/value First, the interaction between psychology and innovation management is explored in this paper. Although empirical studies have shown that trust in U–I alliances is related to innovation performance, and scholars have developed differential game models to portray the GTI process, building a differential game model to explore such an interaction is still scarce. Second, the authors incorporate inter-organizational trust level into the GTI level in university–industry collaboration, applying differential equations to portray the trust building and GTI processes, respectively, to reveal the importance of trust in CTI activities. Third, the authors establish a piecewise deterministic dynamic game model wherein the impact of crisis shocks is not equal to zero, which is inconsistent with most previous studies of Brownian motion.