Extensive development leads to the decline of ecological well-being, and it is necessary to improve the urban ecological well-being performance (EWP). This paper adopted the Super-slack-based measure (Super-SBM) model to evaluate the EWP of 285 Chinese prefecture level cities from 2011 to 2017. The exploratory spatial data analysis method (ESDA) was used to explore the spatial and temporal evolution characteristics of the EWP, and then the spatial Durbin model (SDM) was adopted to analyze the driving factors of the EWP. The results show that the trend of the overall average EWP has experienced a stage evolution process of “upward → downward → upward”. The urban EWPs have significant spatial agglomeration and path dependence. The economic development level and technological progress had the positive impacts on the EWP, and the urbanization level, economic extroversion and industrial structure had the negative impacts on the EWP. The result reveals that there was a “U-shaped” relationship existing between urbanization level and the EWP. The negative spatial spillover effect of urbanization level on the EWP was significant. The corresponding policy implications were put forward. This study will provide strategic guidance for policy makers to optimize and enhance the urban EWP.

Objectives: There is currently a discrepancy between consumers' understanding and practice of green consumption, resulting in inadequate levels of engagement. It is crucial for enterprises to design persuasive green advertisements to enhance consumers' willingness to make green purchases.Methodology: This research employs the perspective of time movement and the S-O-R theoretical model. Two scenario experiments were conducted alongside a questionnaire survey to examine the effects of green advertising message framing (gain vs. loss), time metaphors (ego-moving vs. time-moving), and approach-avoidance motivation on consumers' willingness to make green purchases.Findings: When green advertisements utilize a gain-framing, ego-moving metaphor effectively enhance consumers' willingness to purchase. Conversely, loss-framing paired with time-moving metaphor better promote green consumption behaviors. Approach and avoidance motivations mediate the effects between message framing and time metaphors.Conclusion: Enterprises should consider the matching effects of different information types when designing green advertisements. Specifically, aligning gain-framing with ego-moving metaphor and loss-framing with time-moving metaphor can significantly enhance consumer purchase intentions. Additionally, marketers should focus on consumers' psychological motivations, as approach-avoidance motivation affects the impact of advertising message combinations on purchasing willingness.Implications: The findings elucidate the psychological pathways influencing consumers' green purchasing decisions, assisting enterprises in optimizing advertising message strategies and offering theoretical and practical recommendations for effective green advertising design.

How to effectively release the carbon reduction effect of artificial intelligence(AI) is the urgent task to promote China to achieve carbon peak and carbon neutrality. In this study, we systematically investigated the impacts and mechanisms of action of AI on CO­₂ emissions by constructing econometric models using dynamic panel data from 30 provinces in mainland China from 2006 to 2019. We found that the use of AI significantly reduced CO­₂ emissions under various robustness tests, though such reductions were regionally heterogeneous, with the strongest effects observed in central followed by eastern China; no effect was observed in western China. Further analysis of mediating effects revealed that AI reduced CO₂ emissions by promoting advanced industrial structures and the rationalization of industrial structures, as well as through more ecological industrial structures and by mediating effects among regions. In particular, high-quality advanced industrial structures exhibited reduced CO­­₂ emissions with the use of AI. This study provides theoretical evidence for the reduction of CO­­₂ emissions via AI and provides references for the orderly development of AI technologies and the promotion of carbon peaking and neutrality in China.