The participation of villagers in rural public affairs is vital for the sustainable development of rural communities and the improvement of villagers’ quality of life. Various factors, including individual characteristics, village environment, social forces, and national policies, influence villagers’ willingness to participate. However, empirical research on the determinants of villagers’ participation in public affairs often overlooks the role of grassroots leadership. To address this gap, this study proposes a theoretical model examining the relationship between transformational leadership and villagers’ willingness to participate in public affairs. Using a sample of 438 villagers from Jiangsu Province in eastern China, structural equation modeling and regression are employed to analyze the data. The findings indicate that moral modeling, leadership charm, and personalized care directly impact villagers’ willingness to participate in public affairs or indirectly influence it through leadership identification, which acts as a mediator. Furthermore, shared vision enhances the positive relationship between leadership identification and villagers’ awareness of public affairs participation. These findings provide valuable insights for village cadres seeking to promote active participation in public affairs among villagers. Specifically, gaining recognition through virtuous conduct, capabilities, and strong cadre–villager relationships serves as a foundation for influencing villagers’ willingness to participate in public affairs, while shared vision plays a strategic role in mobilizing villagers based on their identification with leaders.

The automobile industry serves as the primary industry for national development and also enhances manufacturing development strategies. Its source of power comes from the maturity and operational efficiency of the technology innovation network within the industry. Firstly, this paper takes the automobile industry of the Yangtze River Economic Belt as the research object and depicts the topological structure of the 1985–2015 Industry-University-Research Cooperation Innovation Network (IURCIN) from the perspective of space-time evolution. Then, based on the 2015 industrial network characteristic data, the paper analyses the impact of individual network characteristics such as intermediate centrality, structural hole limitation, and cooperation intensity on the innovation performance of the respective network. At the same time, it adds innovation activity, per capita gross regional product (GRP), and research and development (R&D) fund input intensity as control variables in the quantitative analysis. The results show that the centrality and structural hole limitation significantly and positively affect the subject innovation performance. The cooperation intensity and the subject innovation performance have an inverted U-shaped relationship. The innovation activity has a positive effect on the subject’s innovation performance. Furthermore, per capita GRP and R&D expenditure intensity on the main innovation performance is not significant. Finally, the countermeasures and suggestions are put forward to promote the innovation performance of each subject in the IURCIN.

Economic development is mainly dependent on fossil fuels. The massive use of fossil fuels has led to changes in the climate environment, in which the deterioration of air quality has affected people’s daily lives. This paper introduces the green growth level as a control variable to explore the connection between carbon dioxide emissions and the level of economic growth. It uses the EKC algorithm and VEC model to analyze Nanjing city’s data from 1993 to 2018. Given the data availability, the ARIMA algorithm was used to project carbon emissions for 2019–2025. It is found that the EKC curve of Nanjing City shows an N-shape, and the growth of economic level will cause the enhancement of carbon dioxide emissions. Carbon emissions will reach 7,592,140 tons in 2025. At present, we are in an essential stage of transition from N-shape to inverted U-shape, and this paper makes several recommendations based on the findings.

Knowledge learning is a vital pre-factor and the driving force of green enterprise innovation; hence, meriting the numerous academic research and accumulated relevant literature. In this paper, the meta-analysis methodology was used to explore the direction and intensity of the influence of knowledge learning on green enterprise innovation, taking 32 independent documents as research samples. Meta-analysis results showed that the search breadth and the search depth of green resources and the green resources absorption and integration have significant positive effects on the green innovation of enterprises, among which green resources absorption and integration were the most important. Further, the research on the moderating effect found that the measurement method of green innovation affected the relationship between knowledge learning and green enterprise innovation; however, the moderating effect of the research object was not pronounced.