2024年前11个月,我国货物贸易进出口总值39.79万亿元,同比增长4.9%,出口超过23万亿元,同比增长6.7%;全国新设立外商投资企业52000多家,同比增长8.9%。中央经济工作会议将“扩大高水平对外开放,稳外贸、稳外资”作为2025年重点任务之一。
对外经济贸易大学政府管理学院副教授高照钰在人民日报“大家谈·向着教育强国奋进”栏目发表心得:提质扩容,夯实民生保障力——完善终身学习服务体系。
Policy diffusion based on learning mechanisms has fascinated political science and public administration scholars for a long time. A robust and growing body of studies have identified the existence and importance of learning mechanisms in policy diffusion. However, there are still some gaps that need to be further improved. First, scholars identify the learning mechanism mainly based on indirect evidence, such as geographical proximity and successful innovation policies adopted by other jurisdictions, which lacking direct and systematic evidence. Second, little is known about how the hierarchical power structure affects the leap from learning behavior to policy adoption. This study provides direct evidence for the promoting effect of intergovernmental learning on policy diffusion by analyzing case of Chinese local financial subsidy policies for new energy vehicles. The empirical results reveal that policy learning in the form of site visits among local governments significantly promotes the policy diffusion, but superior government policy strategy attenuates the influence of interlocal learning on policy diffusion. Also, the initiators and themes of policy learning affect the learning-diffusion linkage, portraying the conditional effects and nuanced dynamics of interlocal policy learning in eliciting policy diffusion.
The rapid development of generative artificial intelligence (AI) has attracted global attention and posed challenges to existing data governance frameworks. The increased technical complexity and expanded scale of data usage not only make it more difficult to regulate AI but also present challenges for the current legal system. This article, which takes ChatGPT’s training data and working principles as a starting point, examines specific privacy risks, data leakage risks, and personal data risks posed by generative AI. It also analyzes the latest practices in privacy and personal data protection in China. This article finds that while China’s governance on privacy and personal data protection takes a macro-micro integration approach and a private-and-public law integration approach, there are shortcomings in the legal system. Given that the current personal data protection system centered on individual control is unsuitable for the modes of data processing by generative AI, and that private law is insufficient in safeguarding data privacy, urgent institutional innovation is needed to achieve the objective of “trustworthy AI.”