China, the world leader in automobile production and sales, confronts the challenge of transportation emissions, which account for roughly 10% of its total carbon emissions. This study, utilizing real-world vehicle data from three major Chinese cities, assesses the impact of Battery Electric Vehicles (BEVs) on air quality. Our analysis reveals that BEVs, when replacing gasoline vehicles in their operational phase, significantly reduce emissions, with reductions ranging from 8.72 to 85.71 kg of CO₂ per vehicle monthly. The average monthly reduction rate is 9.47%, though this effect is less pronounced during winter. Advanced BEVs, characterized by higher efficiency and newer technology, exhibit greater emission reduction benefits. While private BEVs generally contribute positively to environmental outcomes, taxi BEVs, due to their intensive usage patterns, show less environmental advantage and may sometimes worsen air quality. Looking ahead, we project substantial emission reductions from the replacement of gasoline vehicles with electric alternatives over the next decade. Policymakers are urged to adopt proactive measures, focusing on promoting medium to large electric vehicles and fostering the use of private and ride-hailing electric vehicles.

Few studies have been conducted to perform an in-depth examination of cyberbullying roles and to identify multifaceted factors associated with cyberbullying roles. Using Latent Class Analysis, the current study identified four cyberbullying roles (i.e., cyberbully, cyber victim, cyberbully victim, and noninvolved) based on the data from the 2009–2010 Health Behavior in School-Aged Children study in the United States. Multinomial logistic regressions were constructed to determine the social-ecological factors related to different cyberbullying roles. Certain individual, family, peer, and school factors were found to be associated with distinctive cyberbullying roles. There were common and unique predictors observed that distinguished cyberbullies, cyber victims, and cyberbully victims. Findings suggest the importance of considering both common and unique factors in intervention strategies targeting specific cyberbullying roles. Improving the effectiveness of cyberbullying intervention and prevention programs also requires attention to multifaceted domains and tailoring programs to the population served.

As learners are able to perceive interactivity when interacting with instructors or peer learners in traditional learning environments, learners are similarly able to perceive interactivity when interacting with artificial intelligence (AI) in AI-supported learning environments. Advancements in AI, such as generative AI including ChatGPT and Midjourney, enhance learners’ perceived interactivity, thereby facilitating learning through AI-enabled interaction. However, there is no scale in education for measuring perceived interactivity of learner-AI interaction. This study develops a 17-item scale to assess the extent to which a learner perceives interactivity with AI from four dimensions: responsiveness, personalization, learner control, and learning engagement. The sample group included 422 Chinese university students for the first application and 306 university students for the second application. Both the exploratory factor analysis and the confirmatory factor analysis verified the factor structure of the scale. The Cronbach’s alpha value for the whole scale was 0.948, whereas the Cronbach’s alpha values for the four dimensions ranged between 0.820 and 0.915. Results suggested that this scale was a reliable and valid instrument. This study also found that perceived interactivity of learner-AI interaction was significantly associated with AI tools, learners’ behavioral intentions to use AI in learning, months of using AI in learning, and average duration of using AI in learning each time, and not associated with ages, genders, education levels, and fields of education. Finally, theoretical and practical implications are discussed.