High-quality guided development and environmental forcing are two routes for China’s green development, and each has different focuses and outcomes. The aim of this study is to clarify coordinated mechanisms to reveal the reasons and determine paths for China’s green development. A research framework for synergetic evolution is established, and the Haken model is applied to analyze the different effects of high-quality development that guide resource- and environmental-forcing mechanisms. This research showed that: (1) the preferred route and key factor in China’s green development is high-quality developmental guidance; (2) high-quality development and environmental-forcing mechanisms are non-coordinated, and while the former might coordinate with the latter, the latter does not; (3) mutual promotion of high-quality developmental guidance and the resource mechanism was not observed; (4) the critical point coordinated value is 0.5686 for China’s green development. Eastern China possesses a relatively high level of green development, while the north, east, west, and central areas of the country still have much progress to make.

Under the dual pressure of energy transformation and environmental protection, how to use the innovative network and enhance technical innovation (TI) are significant problems for new energy vehicle (NEV) enterprises in China. Based on the patent data of China’s NEV industry from 2001 to 2022, combined with the logical framework of “Patent Analysis—Network Evolution—Empirical research”, this study deeply discusses the influence of enterprises’ innovative network on TI. It is shown that there is an inverse U-relationship between the cooperation breadth of the enterprises, and that the appropriate cooperation breadth favors TI. At the same time, structural holes, knowledge diversity and technical value have a significant positive impact on TI. In addition, the study performs a series of robustness tests and heterogeneity checks. The conclusions are conducive to further improving the study of the relationship between innovative networks and TI, which is important both theoretically and practically for promoting the energy transformation of automotive enterprises.

Based on various factors in information sharing between innovation alliance groups, this paper analyzes innovation information sharing under the influence of various factors and its evolutionary path to provide a reference for the decision-making of innovation information sharing between innovation alliance groups. Firstly, the paper analyzes the main factors influencing alliance group innovation information-sharing behavior. Secondly, based on the evolutionary game theory, an evolutionary game model of innovative information sharing under the cooperative trust degree of group decision-makers is constructed, and the related stable strategies are given. Finally, the sensitivity of innovative information-sharing strategies to key influencing factors is analyzed with the help of actual case data. The results show that a reasonable amount of innovation information sharing and fair profit distribution can improve the innovation information-sharing behavior among the industry–university–research alliance. Considering the trust degree of vertical partners, the alliance group pays more attention to the profit distribution ratio of collaborative innovation of the innovation alliance than the profit amount. When horizontal partner trust is considered, horizontal cooperation trust can promote information sharing among enterprises, universities, and research institutes in the short term but impedes information sharing among groups in the long term.

Energy transition plays a crucial role in supporting sustainable economic growth and the reduction in carbon emissions. In fact, China implemented the national sustainable development experimental zone policy to achieve sustainable development goals, including an energy transition. This paper divided the energy transition dimension into energy consumption and carbon dioxide emissions based on the perspectives of energy input and output. Furthermore, using panel data for 214 cities at the prefecture level in China from 2006 to 2019, the study measured the impact of the national sustainable development experimental zone policy on energy transitions by employing a difference-in-difference (DID) model and an intermediary effect model. The results showed that the national sustainable development experimental zone policy reduced energy consumption and carbon dioxide emissions and accelerated energy transition. The conclusions still held after a series of robustness tests. Additionally, the results of the heterogeneity analysis of different experimental zone types indicated that, compared with prefecture-level experimental zones, county experimental zones play a more obvious role in reducing energy consumption and carbon dioxide emissions. In addition, the results of the heterogeneity analysis of the urban geographical location showed that the national sustainable development experimental zone policy had different negative effects on urban energy consumption and carbon dioxide emissions in different regions, and the impact of policy on energy transition was experienced, in decreasing order, by the western, central, and eastern regions. The results of the mechanism verification indicated that the national sustainable development experimental zone policy can affect energy consumption and carbon dioxide emissions via technological progress and upgrading industrial structure, which had a relatively high aggregation order in the variables deployed.

According to their resource ownership and resource acquisition channels, this paper constructs an evolutionary game model of enterprises’ technological innovation investment channels considering managers’ expectations. This results in the strategy of non-alliance firms along the first channel having a significant impact on the evolutionary equilibrium. For non-alliance enterprises, the difference in the amount of investment in the R&D stage has the same impact on the probability of such enterprises following the first channel. The number of active managers, group capacity, the difference in managers’ expectations, and the difference in the amount of technological innovation investment in the R&D stage have significant differences in the possibility of choosing the first channel between the two types of enterprises. The research conclusions can provide valuable references for enterprises to make technological innovation decisions.