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Artificial intelligence governance exhibits a striking paradox: while major jurisdictions converge rhetorically around concepts such as safety, risk, and accountability, their regulatory frameworks remain fundamentally divergent and mutually unintelligible. This paper argues that this fragmentation cannot be explained solely by geopolitical rivalry, institutional complexity, or instrument selection. Instead, it stems from how AI is constituted as an object of governance through distinct institutional logics. Integrating securitisation theory with the concept of the dispositif, we demonstrate that jurisdictions govern ontologically different objects under the same vocabulary. Using semantic network analysis of official policy texts from the European Union, the United States, and China (2023-2025), we trace how concepts like safety are embedded within divergent semantic architectures. Our findings reveal that the EU juridifies AI as a certifiable product through legal-bureaucratic logic; the US operationalises AI as an optimisable system through market-liberal logic; and China governs AI as socio-technical infrastructure through holistic state logic. We introduce the concept of struc

Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices. To address the gap, this study proposes a deep learning-based framework to monitor the spatiotemporal changes of UVs in China. Specifically, semantic segmentation of multi-temporal remote sensing imagery is first used to map evolving UV boundaries, and then post-demolition land use is classified into six categories based on the "remained-demolished-redeveloped" phase: incomplete demolition, vacant land, construction sites, buildings, green spaces, and others. Four representative cities from China's four economic regions were selected as the study areas, i.e., Guangzhou (East), Zhengzhou (Central), Xi'an (West), and Harbin (Northeast). The results indicate: 1) UV redevelopment processes were frequently prolonged; 2) redevelopment transitions primarily occurred in peripheral areas, whereas urban cores remained relatively stable;

This paper first introduces China's legal framework regulating facial recognition technology (FRT) and analyzes the underlying problems. Although current laws and regulations have restricted the development of FRT under some circumstances, these restrictions may function poorly when the technology is installed by the government or when it is deployed for the purpose of protecting public security. We use two cases to illustrate this asymmetric regulatory model, which can be traced to systematic preferences that existed prior to recent legislative efforts advancing personal data protection. Based on these case studies and evaluation of relevant regulations, this paper explains why China has developed this distinctive asymmetric regulatory model towards FRT specifically and personally data generally.

This study assesses the global hydrogen import potential in 2050 by looking at the renewable hydrogen production potential in prospective import-oriented countries. Renewable energy potentials calculated with a GIS based model and 2050 primary energy consumption projections are used to identify candidate importers by comparing it with expected demand. Two approaches are applied: (1) a meta-analysis of literature on hydrogen production potential and demand for the identified countries, and (2) detailed regional analyses for the United States and China. The results suggest limited prospects for a fully global hydrogen market, although certain countries, such as Germany, Italy, and the Netherlands, are likely to remain net importers. By contrast, many Asian countries have enough renewable resources to decarbonise their energy systems. China and the United States may follow divergent pathways: while the United States is well placed to meet its overall demand, using its own resources, it can still benefit from regional connections with neighbouring countries. China is facing strong demand in its eastern regions, so it may source cost-effective renewable hydrogen from foreign countries i

This paper identifies the impact of China-U.S. science and technology (S&T) friction on knowledge flows in different fields, using data on invention patent applications from China, the U.S., Europe, and the World Patent Office (WPO) along with machine-learning-based econometric methods. The empirical results find that the negative impacts of China-U.S. S&T frictions on cross-border knowledge flows are confined to a limited number of technology areas during the period of our observation. This paper further explores the characteristics of the negatively impacted technology areas, and the empirical results show that technology areas that rely more on basic scientific research, where the distribution of U.S. scientific and technological strength is more concentrated, and where the gap between U.S. and Chinese science and technology is narrower, are more likely to be the victims of the Sino-U.S. S&T friction.

Beyond the mainstream discussion on the key role of China in the global AI landscape, the knowledge about the real performance and future perspectives of the AI ecosystem in China is still limited. This paper evaluates the status and prospects of China's AI innovation ecosystem by developing a Triple Helix framework particularized for this case. Based on an in-depth qualitative study and on interviews with experts, the analysis section summarizes the way in which the AI innovation ecosystem in China is being built, which are the key features of the three spheres of the Triple Helix -governments, industry and academic/research institutions-as well as the dynamic context of the ecosystem through the identification of main aspects related to the flows of skills, knowledge and funding and the interactions among them. Using this approach, the discussion section illustrates the specificities of the AI innovation ecosystem in China, its strengths and its gaps, and which are its prospects. Overall, this revisited ecosystem approach permits the authors to address the complexity of emerging environments of innovation to draw meaningful conclusions which are not possible with mere observation

Knowing which countries contribute the most to pushing the boundaries of knowledge in science and technology has social and political importance. However, common citation metrics do not adequately measure this contribution. This measure requires more stringent metrics appropriate for the highly influential breakthrough papers that push the boundaries of knowledge, which are very highly cited but very rare. Here I used the recently described Rk index, specifically designed to address this issue. I applied this index to 25 countries and the EU across 10 key research topics, five technological and five biomedical, studying domestic and international collaborative papers independently. In technological topics, the Rk indices of domestic papers show that overall, the USA, China, and the EU are leaders; other countries are clearly behind. The USA is notably ahead of China, and the EU is far behind China. The same approach to biomedical topics shows an overwhelming dominance of the USA and that the EU is ahead of China. The analysis of internationally collaborative papers further demonstrates the US dominance. These results conflict with current country rankings based on less stringent in

The United States and China will play an important role in navigating safety and security challenges relating to advanced artificial intelligence. We sought to better understand how experts in each country describe safety and security threats from advanced artificial intelligence, extreme risks from AI, and the potential for international cooperation. Specifically, we compiled publicly-available statements from major technical and policy leaders in both the United States and China. We focused our analysis on advanced forms of artificial intelligence, such as artificial general intelligence (AGI), that may have the most significant impacts on national and global security. Experts in both countries expressed concern about risks from AGI, risks from intelligence explosions, and risks from AI systems that escape human control. Both countries have also launched early efforts designed to promote international cooperation around safety standards and risk management practices. Notably, our findings only reflect information from publicly available sources. Nonetheless, our findings can inform policymakers and researchers about the state of AI discourse in the US and China. We hope such work

As digitalization increases, countries employ digital diplomacy, harnessing digital resources to project their desired image. Digital diplomacy also encompasses the interactivity of digital platforms, providing a trove of public opinion that diplomatic agents can collect. Social media bots actively participate in political events through influencing political communication and purporting coordinated narratives to influence human behavior. This article provides a methodology towards identifying three types of bots: General Bots, News Bots and Bridging Bots, then further identify these classes of bots on Twitter during a diplomatic incident involving the United States and China. Using a series of computational methods, this article examines the impact of bots on the topics disseminated, the influence and the use of information maneuvers of bots within the social communication network. Among others, our results observe that all three types of bots are present across the two countries; bots geotagged to the US are generally concerned with the balloon location while those geotagged to China discussed topics related to escalating tensions; and perform different extent of positive narrati

Major shifts in the global system of science and technology are destabilizing the global status order and demonstrating the capacity for emerging countries like China and India to exert greater influence. In order to measure changes in the global scientific system, we develop a framework to assess the hierarchical position of countries in the international scientific collaboration network. Using a machine-learning model to identify the leaders of 5,966,623 scientific teams that collaborated across international borders, we show that Chinese scientists substantially narrowed their leadership deficit with scientists from the US, UK, and EU between 1990 and 2023 in absolute terms. Consequently, China and the US are on track to reach an equal number of team leaders engaged in bilateral collaborations between 2027 and 2028. Nevertheless, Chinese progress has been considerably slower in per-collaborator terms: after adjusting for the number of non-leaders from each country, our models do not predict parity between the US and China until after 2087. These dynamics extend to 11 critical technology areas central to ongoing diplomacy between the two nations, such AI, Semiconductors, and Adva

As a powerful and rapidly advancing dual-use technology, AI offers both immense benefits and worrisome risks. In response, governing bodies around the world are developing a range of regulatory AI laws and policies. This paper compares three distinct approaches taken by the EU, China and the US. Within the US, we explore AI regulation at both the federal and state level, with a focus on California's pending Senate Bill 1047. Each regulatory system reflects distinct cultural, political and economic perspectives. Each also highlights differing regional perspectives on regulatory risk-benefit tradeoffs, with divergent judgments on the balance between safety versus innovation and cooperation versus competition. Finally, differences between regulatory frameworks reflect contrastive stances in regards to trust in centralized authority versus trust in a more decentralized free market of self-interested stakeholders. Taken together, these varied approaches to AI innovation and regulation influence each other, the broader international community, and the future of AI regulation.

Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China. In this paper, we examine China's AI development process, demonstrating that it is characterized by rapid learning and differentiation, surpassing the export-oriented growth propelled by Foreign Direct Investment seen in earlier Asian industrializers. Our data indicates that China currently leads the USA in the volume of AI-related research papers. However, when we delve into the quality of these papers based on specific metrics, the USA retains a slight edge. Nevertheless, the pace and scale of China's AI development remain noteworthy. We attribute China's accelerated AI progress to several factors, including global trends favoring open access to algorithms and research papers, contributions from China's broad diaspora and returnees, and relatively lax data protection policies. In the vein of our research, we have developed a novel measure for gauging China's imitation of US research. Our analysis shows that by 2018, the time lag between China and the USA in addressing AI research topics had evaporated. This finding suggests that China has effectively bridged a significant

We used natural language processing to analyze a billion words to study cultural differences on Weibo, one of China's largest social media platforms. We compared predictions from two common explanations about cultural differences in China (economic development and urban-rural differences) against the less-obvious legacy of rice versus wheat farming. Rice farmers had to coordinate shared irrigation networks and exchange labor to cope with higher labor requirements. In contrast, wheat relied on rainfall and required half as much labor. We test whether this legacy made southern China more interdependent. Across all word categories, rice explained twice as much variance as economic development and urbanization. Rice areas used more words reflecting tight social ties, holistic thought, and a cautious, prevention orientation. We then used Twitter data comparing prefectures in Japan, which largely replicated the results from China. This provides crucial evidence of the rice theory in a different nation, language, and platform.

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.

This review identifies challenges and effective strategies to decarbonize China's rapidly growing transportation sector, currently the third largest carbon emitter, considering China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. Key challenges include rising travel demand, unreached peak car ownership, declining bus ridership, gaps between energy technology research and practical application, and limited institutional capacity for decarbonization. This review categorizes current decarbonization measures, strategies, and policies in China's transportation sector using the "Avoid, Shift, Improve" framework, complemented by a novel strategic vector of "Institutional Capacity & Technology Development" to capture broader development perspectives. This comprehensive analysis aims to facilitate informed decision-making and promote collaborative strategies for China's transition to a sustainable transportation future.

In the field of quantitative finance, volatility models, such as ARCH, GARCH, FIGARCH, SV, EWMA, play the key role in risk and portfolio management. Meanwhile, factor investing is more and more famous since mid of 20 century. CAPM, Fama French three factor model, Fama French five-factor model, MSCI Barra factor model are mentioned and developed during this period. In this paper, we will show why we need adjust group of factors by our MAXFLAT low-pass volatility model. All of our experiments are under China's CSI 300 and CSI 500 universe which represent China's large cap stocks and mid-small cap stocks. Our result shows adjust factors by MAXFLAT volatility model have better performance in both large cap and small cap universe than original factors or other risk adjust factors in China A share. Also the portfolio constructed by MAXFLAT risk adjust factors have continuous excess return and lower beta compare with benchmark index.

The rising popularity of ChatGPT and other AI-powered large language models (LLMs) has led to increasing studies highlighting their susceptibility to mistakes and biases. However, most of these studies focus on models trained on English texts. Taking an innovative approach, this study investigates political biases in GPT's multilingual models. We posed the same question about high-profile political issues in the United States and China to GPT in both English and simplified Chinese, and our analysis of the bilingual responses revealed that GPT's bilingual models' political "knowledge" (content) and the political "attitude" (sentiment) are significantly more inconsistent on political issues in China. The simplified Chinese GPT models not only tended to provide pro-China information but also presented the least negative sentiment towards China's problems, whereas the English GPT was significantly more negative towards China. This disparity may stem from Chinese state censorship and US-China geopolitical tensions, which influence the training corpora of GPT bilingual models. Moreover, both Chinese and English models tended to be less critical towards the issues of "their own" represent