Science and technology ethics is a value criterion and code of conduct, which must be complied in activities of scientific research and technological development. The report of the 20th National Congress of the Communist Party of China proposes to improve the system of scientific and technological innovation. Strengthening the governance of science and technology ethics is an important way to achieve this goal. It is important for sci-tech self-reliance and self-strengthening to strengthen the ethical governance of science and technology. The ethical governance of science and technology in China faces challenges and situations such as enhancing international competitiveness in science and technology, strengthening ethical regulation in emerging technology fields, and balancing scientific and technological innovation with ethical regulation in a reasonable manner. The paper analyzes the following issues exist in the current ethical governance of science and technology in China, that is, policies and regulatory mechanisms are incomplete, ethical review standardization and talents are lacking, scientific and research person and the public and news media are lack of guidance. Finally, the strategies and proposals to strengthen ethical governance of science and technology are advanced. The strategies and proposals can provide some references for formulation of a science-technology ethical governance system which is suitable for Chinese characteristics.

In the process of the construction of "Digital China" and "Beautiful China", this paper takes the balanced panel data of 274 cities at prefecture level and above in China from 2011 to 2019 as samples, and on the basis of analyzing the characteristics of digital economy itself, discusses the impact of digital economy on urban green development from two aspects of action mechanism and threshold effect. The study found that digital economy significantly promoted urban green development, and this promoting effect was more obvious in eastern cities, smart city pilot cities and cities with strong intellectual property protection. Further research shows that digital economy can promote urban green development by promoting the servitization of economic structure, stimulating green technology innovation and enhancing public environmental concern. With the extensive construction of the current digital infrastructure, social electricity consumption and carbon emissions continue to increase, and the impact of digital economy on urban green development has a non-linear feature of diminishing marginal effect. The research conclusions of this paper not only actively promote cities to embrace the new paradigm of green development, but also provide a useful reference for the construction of an intensive and low-carbon modern infrastructure system and the realization of digital green coordinated development.

“SRDI” small and medium-sized enterprises (S-SMEs) are an important vehicle for implementing innovation-driven strategies. In view of the lack of research on the micro-level of S-SMEs' innovation performance in academic circles, this paper examines the significant key factors and configuration effect that influence S-SMEs' innovation performance from the perspective of complex causal effect analysis, mixing NCA, empirical regression and QCA methods, and refines the corresponding improvement paths. It is found that (1) R&D capability, financing environment and market competitiveness significantly affect the innovation performance of S-SMEs; (2) there are five configuration paths that enhance the innovation performance of different types of S-SMEs; (3) when financing is blocked, either small-scale production or improving their own market competitiveness can enable S-SMEs to maintain a high level of innovation performance target. This paper attempts a new way of thinking of complex causal effect analysis, which provides some theoretical and practical references for the study of S-SMEs' innovation performance in the future.

Improving the policy system is a necessary prerequisite for strengthening the institutional guarantee of scientific and technological ethics governance. Taking 45 policies on technology ethics governance issued by national departments in China from 1999 to 2022 as research samples, an analysis of the external attributes of policies reveals that the temporal evolution of policy intensity showed three peaks in 2001, 2007, and 2020. The institutional types of policy issuing departments cover a diverse range. The number of publications is led by the health management department, accounting for 43%. Constructing a three-dimensional policy content analysis framework for the governance process of science and technology ethics (X) - policy tools (Y) - policy objects (Z). Using the quantitative analysis method of policy texts, it was found that the number of policy texts focused on science and technology ethics supervision in the dimension of science and technology ethics governance process was 48.2%, with system change tools accounting for 35.2%, authoritative tools accounting for 34.2%, and policy object dimensions mainly focusing on science and technology institutions accounting for 52.3%; The X-Y two-dimensional analysis found that the supervision and management, feedback and improvement processes comprehensively utilized five types of policy tools, while reporting and disclosure only used two types of policy tools: incentive tools, symbolic tools, and persuasive tools; X-Z two-dimensional analysis found that the stages of education consultation, supervision and management, and investigation and processing all covered various policy objects, while the policy objects in the reporting and disclosure stage lacked precise requirements for relevant government management departments and scientific and technological institutions; The overall distribution of policy texts in three-dimensional interactive analysis is uneven. Propose suggestions such as improving the policy system of scientific and technological ethics governance to provide institutional guidance for regulating the order of scientific and technological innovation, constructing a linkage and implementation mechanism of scientific and technological ethics governance centered on the characteristics of national strategic scientific and technological forces, and establishing a full process supervision chain support policy implementation efficiency based on scientific data digitization platform for scientific and technological activities.

The Low-altitude Economy is an important component of the New Quality Productive Forces, and the sound and sustainable development of the Low-altitude Economy is based on strict safety guarantees. Emerging safety risks derived from Low-altitude Economy activities include aviation safety risks, public safety risks, national information security risks and personal privacy security risks, and are characterized by the plurality of generating and bearing subjects, the arbitrariness of generating time and space, and the asymmetry of risk realization costs and governance costs. As far as the relationship between safety risks and the development of the Low-altitude Economy is concerned, the complete logical chain of risk evolution suggests an interaction with the development of the Low-altitude Economy itself. First, the development of economic efficiency leads to airspace reform. Secondly, airspace reform leads to changes in the powers (rights) structure. Thirdly, the diversification of powers (rights) leads to the evolution of safety risks. Finally, the emerging safety risks hinder the development of the Low-altitude Economy. However, China’s existing legal system, governance mechanisms, and governance tools cannot effectively regulate and prevent new safety risks caused by the Low-altitude Economy. With regard to the legal system, the existing legal system lags behind practice at the macro level, and some of the rules are characterized by rigidity. In addition, some of the new legal documents are fragmented and even contradictory. In terms of governance mechanisms, there are problems such as chaotic governance institutions, unclear responsibilities and a single structure of governance bodies. In terms of governance tools, there are still problems such as difficulties in law enforcement and high enforcement costs. The concept of Agile Governance aims to change the way policy operates in the fourth industrial revolution, and it is characterized by more progressive features. First, it is responsive. Agile Governance is more time-sensitive than the traditional plan-based policy-making approach. On the one hand, Agile Governance places more emphasis on rapid response to the object of governance and real-time optimization of governance behaviors, thus achieving the dual efficacy of immediate handling and risk prevention. On the other hand, Agile Governance can also realize the continuous iteration of governance policy and governance itself. Secondly, the multi-stakeholders of governance. Agile Governance advocates the adoption of a new paradigm that goes beyond government governance and allows more stakeholders to participate in governance. As a policymaker, the government should share the “workload” of constructing, monitoring and optimizing the governance system or policy with private entities and citizens’ groups in order to realize the checks and balances among different stakeholders and to make the process of policy-making and the policies formulated more inclusive and human-centered. Agile Governance’s characteristics of rapid response, multi-stakeholders, and continuous iteration reveal its natural interrelationship with emerging safety risks. In order to promote the high-quality and sustainable development of the Low-altitude Economy on the basis of effective prevention of emerging safety risks, firstly, the construction of “hard law” and “soft law” should be coordinated to help construct a governance legal system that balances economic development and safety protection; secondly, the powers and functions of the main public governance authorities should be clarified and synergy should be strengthened, and the effectiveness of the autonomy of other multi-stakeholders should be emphasized; and thirdly, it is necessary to innovate governance tools by improving infrastructure and promoting the integration of technological tools in order to achieve mutual good governance of emerging safety risks.

Generative artificial intelligence (AIGC) is being integrated into social life and is participating in knowledge production. It also creates new digital inequalities, and scholars are concerned about deepening the digital divide. This article explores the theoretical context of artificial intelligence and its impact on society and tries to answer how AIGC shapes the "AI divide" in the context of the development of Digital China. At the technical level, the "AI divide" involves three dimensions: data, algorithms, and computing power. Data sets serve as the foundation of AIGC, similar to soil. The scale parameters, source channels, content types, and even languages of the data sets invisibly shape the "AI divide". The algorithm model consists of self-supervised learning and human feedback reinforcement learning. It is similar to the roots and stems of a plant, which determines the vitality of AIGC to bloom and bear fruit. Computing power is AIGC's information infrastructure, connecting the real world (chips) and the virtual world (data and algorithms), determining the processing speed of computing operations and the depth of integration in the digital industry. As modern technology becomes increasingly dominant in shaping our understanding of the world and history, social stratification theory can help us understand the impact of the "AI divide". The first level involves the divide among individuals, specifically between ordinary users, in accessing and utilizing AI technology. This includes language barriers, paywalls, practical skills, willingness to use, perception of benefits, and the desire to learn. The second aspect pertains to human-machine differentiation, which, on the surface, refers to the distinction between humans and autonomous AI agents. However, at its core, it represents the divide between the majority who lack technological initiative and the minority who wield it due to their control over digital resources. It refers to using digital resources by countries, regions, or individuals to participate in the development, share knowledge, and overcome differences in capabilities for autonomous growth. To bridge the "AI divide", it is important to establish an independent and self-sufficient technological innovation system. We should also explore and optimize application scenarios to make the most of this system. China needs to take the lead in AI development in the future and promote the construction, transformation, and application of AIGC resources by different enterprises, scientific research institutions, and industry organizations. It must also establish a fair and standardized platform governance ecosystem and create a good technology atmosphere. Equality should be embedded into AIGC's technology process, including algorithm design, training data selection, model generation, and optimization. This will give people of different countries, ethnic groups, and classes the right to participate in scientific development. Finally, China should plan more diverse ecological alliance forms and participate in cooperation platforms under multilateral frameworks such as the United Nations, the World Trade Organization, and Asia-Pacific Economic Cooperation. China should transform games and competitions into consultation and cooperation, connect wider upstream and downstream resources, and try to build a global AI governance community. The article proposes the concept of the "AI divide", which deepens the third-generation digital divide theory in artificial intelligence and has significant practical implications for understanding the development of digital China and the economic and social transformation.

At present,the security and stability of the global manufacturing industry chain are facing unprecedented challenges.How to improve the resilience of the industry chain and strengthen the construction of the industry chain has become an urgent problem to be solved.Based on the balanced panel data of 30 provinces and cities in China from 2007 to 2017,from the perspective of industrial transformation and upgrading and innovation chain construction,this paper studies the impact of collaborative agglomeration of manufacturing industry and science and technology service industry (hereinafter referred to as collaborative agglomeration of two industries) on the resilience of manufacturing industry chain,and discusses how the collaborative agglomeration of two industries affects the resilience of manufacturing industry chain by influencing technological innovation,On this basis,we further explore the regulatory role of innovation ecosystem symbiosis in this mechanism.The research finds that: the collaborative agglomeration of two industries can significantly promote the resilience of the manufacturing industry chain by influencing technological innovation.The symbiosis of innovation ecosystem can effectively adjust the impact of the collaborative agglomeration of two industries on the resilience of the manufacturing industry chain.The collaborative agglomeration of two industries can not only directly affect the toughness of the manufacturing industry chain,but also effectively adjust the impact of technological innovation on the toughness of the manufacturing industry chain.Based on the above research conclusions,relevant suggestions are put forward to enhance the resilience of the manufacturing industry chain.

The post-Moore era marks the period following the breakdown of “Moore's Law,” which predicted that microprocessor performance would double approximately every two years. In this era, integrated circuits are approaching the physical limits of material density and process technology, while energy consumption costs are rising significantly. The onset of the post-Moore era has led to three major technological shifts. First, in advanced silicon-based semiconductor manufacturing, the first-mover advantage of leading nations, regions, and companies is gradually eroding. Second, new competitive arenas are emerging in advanced packaging technologies and the development of alternative materials for silicon-based chips. Third, the rise of the RISC-V architecture is challenging the longstanding market dominance of Intel x86 and ARM architectures. The post-Moore era has also reshaped the competitive landscape, characterized by oligopolistic market structures and the increasing nationalization of key industry players. These developments offer new opportunities for China’s integrated circuit industry. In light of China’s burgeoning digital economy, which continuously creates new scenarios and markets, we propose that the Ecosystem strategy for the development of China's integrated circuit industry in the post-Moore era should encompass four types. First, mature ecosystem, which adheres to the singular technological path of More Moore, and the goal is to enhance the industry's autonomy and controllability, as well reducing the risk of being subjected to critical constraints. Second, scenario-driven ecosystem, which follows the integrated technological path of More than Moore and More Moore, and the aim is to leverage China's rich application scenarios to expand the boundaries of the integrated circuit industry ecosystem, while continuously promoting the industry's digital transformation and achieve autonomous control over the integrated circuit industry ecosystem from the end of key application scenarios. Third, future industry ecosystem, which integrates all three technological paths, with a key focus on breakthroughs in Beyond CMOS technology, and the objective is to capitalize on the advantages of a new nationwide system, to plan moderately ahead, and to seize the high ground in future international competition. Fourth, internationalized ecosystem, which emphasizes expanding China's diversified industrial ecosystem into the international market, and the purpose is to actively integrate into the global innovation ecosystem, further expand the scope of scenario-driven ecosystems, and enhance the resilience of the ecosystem. To address the challenges in the development of China's integrated circuit industry in the post-Moore era, it is essential to leverage the advantages of China's super-large scale market and its new nationwide system. First, in the domain of mature ecosystems, reconstructing the industrial chain, along with the support and guidance of industrial policies, can help build a second ecosystem that is autonomous, controllable, and compatible. Second, based on the combined technological approach of System-on-Chip (SoC) and System-in-Package (SiP), the rich application scenarios can be utilized to expand the boundaries of the industrial ecosystem. Simultaneously, by infiltrating through scenario applications, it is possible to gain dominance in these emerging industrial ecosystems. Third, proactively layout advantageous industrial ecosystems around future industries. Additionally, there should be innovation in the organizational models of industrial internet and the promotion of industry-level data element market construction, ensuring that various production relationships meet the development needs of key core technologies in future industries. Fourth, based on the characteristics of dominant countries and dominant enterprises in different segments of the industrial chain, China can adjust its strategies targetedly to actively integrate into the global innovation ecosystem and international industrial ecosystems.

Abstract:The process of scientific research activities have a progressive, derived feature of complex structure.The scientific research innovation chain-data chain-publishing chain are closely connected and iterative.The evolution of data ecosystem and its model evolution is crucial to promoting scientific research innovation and expanding knowledge dissemination. On the basis of conceptual backtracking, this paper uses the theory of data ecosystem to elaborate the different data-driven logics of scientific research innovation and academic publishing. According to the different data thinking, data institution,data subject relationship, data management structure, data circulation environment and data management methods, the research data ecosystem can be divided into three gradual stages: closed, expanded and collaborative. Summarize the development trend of research data ecosystem from single to ecological, from fragmentation to system, from unilateral to collaborative, from point-to-point to integration, from manual to intelligent. A theoretical model for the evolution of scientific research data ecological model is proposed, and provide a new idea for promoting the overall quality improvement of each element and link of scientific research ecosystem with scientific data ecological governance.

“Algorithmic Capitalism” refers to a contemporary form of capitalism with intelligent algorithms at its central control, representing the iterative upgrade of digital capitalism under the new technological revolution. Unlike previous forms of capitalism, algorithmic capitalism follows the logic of intelligence, featuring new characteristics of trans-regional, full-process, automation, and implicit dominance. From the perspective of core structure, algorithmic capitalism follows a four-element structure of “algorithm-platform-data-capital”, where algorithms serve as the “efficient” element, platforms as the “formal” element, data as the “material” element, and capital as the “final” element. Algorithmic capitalism has the dual significance of progress and limitation, bringing more mobility and social change while also triggering serious negative ethical effects. Research on algorithmic capitalism holds significant theoretical and practical value in the current context.

The AI-driven new paradigm of scientific research is becoming a crucial driver for China to achieve technological competitive advantage and emerge as a leading science and technology powerhouse. With the development of the new generation of AI technology, the growing importance of AI in driving technological advancements and fostering interdisciplinary collaboration is gaining increasing attention. Many countries around the world have emphasized the importance of AI in promoting scientific exploration and have strengthened policy deployment and guidance across various domains, including establishing specialized research institutions, providing financial support, fostering talent development, leveraging data resources, innovating in technology, and collaborating internationally. The new paradigm of scientific research driven by artificial general intelligence has catalyzed transformations in research organizational patterns through the empowerment of large-scale AI models. The AI-driven new paradigm of scientific research represents a transformation of traditional research methods and processes, creating a knowledge-centered system with diversified stakeholder participation and a symbiotic collaboration between humans and machines. While the significance of adopting AI-driven research paradigms is widely acknowledged, existing studies have not thoroughly explored their theoretical foundations from epistemological and methodological perspectives. This paper investigates the AI-driven new research paradigm from a dual perspective of ‘Knowledge Evolution Theory’ and ‘Paradigm Theory’, systematically elucidating the main characteristics of the new research paradigm, and building a research framework for the AI-driven scientific paradigm. Knowledge evolution theory focuses on the specific mechanisms, evolutionary laws and influencing factors of knowledge growth and evolution. Paradigm Theory research on a collection of rule systems universally adopted by the scientific community to ensure efficient and orderly operation of research activities. Building on this framework, the paper conducts a multi-case analysis of the exploratory application of the AI-driven research paradigm. The study reveals that the AI-driven research paradigm encompasses elements such as research tools, research organization models, diverse application scenarios, and governance of research applications. Specifically, AI research tools optimize traditional research processes through substitution effects, enhancement effects, and autonomous effects. The AI-empowered research platform organization model is transforming the infrastructure, organizational structure, collaboration mechanisms, and talent composition of existing research paradigms. Multidisciplinary research bottlenecks provide training and iterative application scenarios for large models. Simultaneously, governance of AI research applications under the new paradigm needs to be refined based on the technological, content, and social attributes of AI. The study outlines the policy implications for developing a new AI-driven paradigm in scientific research based on its findings. First, it is essential to establish a policy framework for the "AI-driven new paradigm of scientific research". Second, there is a need to enhance database infrastructure, advance computing capabilities, and foster the development of basic models for artificial general intelligence. Concurrently, efforts should focus on enhancing decentralized large-scale model platforms and other novel scientific research models that align with the integration of artificial general intelligence applications. Finally, attention must be focused on the governance of AI applications in scientific research. This study offers theoretical support and empirical insights for the coordinated formulation and implementation of the ‘AI-driven new paradigm of scientific research’ policy.

The suppression of physical inventory in the United States is becoming increasingly frequent, and studying the micro impact mechanism of physical inventory on enterprise innovation has become increasingly important. This article takes 28 entities related to the physical inventory as the research object, and uses the grounded method to construct a "short-term long-term" dual feedback impact mechanism model for enterprises under the impact of the physical inventory. Research has found that: (1) the US entity list is essentially a precise "dynamic targeted" strike, consisting of three links: suppression targets, sanctions measures, and dynamic adjustments, including eight specific sanctions measures, essentially forcing Chinese enterprises to maintain a low level of competitiveness; (2) The impact process is manifested as the evolution from short-term effects to long-term effects: suppressing short-term effects mainly affects four aspects: supply chain, business chain, R&D interruption, and academic exchange. Enterprises take stressful measures to respond; The suppression of short-term effects will gradually evolve into long-term effects: increased demand, market distortion, insufficient innovation, and information scarcity, while strategic measures must be taken to address long-term effects; (3) There are two key paths for the government to respond to the suppression of the US physical inventory: emergency relief policies → short-term effects (supply chain+business chain) → early warning mechanisms, dual demand traction → long-term effects (production line collaboration+core technology research and development) → strategic breakthroughs. The key to path 1 is to help enterprises overcome the risk of capital interruption and death, while the key to path 2 is to fully utilize the favorable conditions for increased demand for domestically produced equipment. This article fills the gap in the research on the impact of physical inventory on micro enterprise processes, providing reference for the government to formulate strategies and how enterprises can respond to the impact of physical inventory.

Fintech ethics is the cornerstone of high-quality development of financial services.. In view of the lack of research on systematic governance ， this paper uncovers the intricate hierarchical structure and evolutionary process through an analysis of the meaning of fintech ethics. This includes the hidden nature of technology, the diversity of its applications, the broad range of its social impacts and vertical interactions across various levels, as well as the horizontal phases of Introductory period, technological invasion, and convergence volatility and cyclical iterative growth. Our research proposes a collaborative governance structure that is guided and preventive during the convergence and volatility period. It highlights the role of a triad of government-led collaborative governance agents that connect the market and individuals. This approach emphasizes the cascade of legal intervention, market regulation, and guided ethical embedding to tackle critical issues such as policy failure, uncontrolled risk, and lack of ethical awareness. By presenting a comparative analysis of practical examples in the world's major economies, it provides recommendations for governance in China's present time with implications for promoting financial reform measures.

New quality productivity is a powerful driving force and supporting force to promote the high-quality development of enterprises. This view has been widely recognized by the community. Accelerating the development of new quality productivity will help enterprises enhance their competitive advantage and realize sustainable development. However, the existing literature focuses on exploring the connotation characteristics and value significance of new quality productivity, and there are few empirical research models to explore its driving forces. Some studies believe that the digital intelligence transformation is the core driving force to lead the innovation and development of enterprises, and whether the digital intelligence transformation can improve the new quality productivity level of enterprises is an urgent topic to be discussed. Based on the dynamic resource-based view, using the data of China A-share listed companies from 2015 to 2022, this paper empirically analyzes the influence and heterogeneity of digital intelligence transformation on the new quality productivity of enterprises, and tests the mediating role of absorptive capacity and the moderating role of competitive intensity. It is found that: (1) the digital intelligence transformation has a significant impact on the improvement of the new quality productivity level of enterprises, and this conclusion still holds after various robustness tests. (2) the digital intelligence transformation can improve the absorptive capacity and new quality productivity of enterprises, and the absorptive capacity plays a mediating role in the relationship between the digital intelligence transformation and new quality productivity of enterprises. (3) the competitive intensity plays a positive role in moderating the relationship between the digital intelligence transformation and the new quality productivity of enterprises. (4) heterogeneity analysis shows that, compared with state-owned enterprises, enterprises in the central and western regions and in recession, non-state-owned, eastern regions, growing and mature regions can promote the improvement of new quality productivity by implementing digital intelligence transformation. The theoretical contributions of this study are as follows: first, the existing literature focuses on the connotation characteristics and value significance of new quality productivity, while there are few empirical research models to explore its driving factors. This study examines the influence of digital intelligence transformation on the new quality productivity level of enterprises through empirical research, which not only provides theoretical reference for promoting the transformation of digital intelligence and improving the new quality productivity level of enterprises, but also enriches the empirical research on the relationship between digital intelligence transformation and new quality productivity of enterprises. Second, based on the dynamic resource-based view, the theoretical model of "digital intelligence transformation-absorptive capacity-new quality productivity of enterprises" is constructed, and the intermediate transmission mechanism of absorptive capacity is deeply analyzed, which opens the "black box" in the process of digital intelligence transformation and empowerment of new quality productivity of enterprises. Thirdly, by including the industry competitive intensity at the macro level as a moderating variable, the boundary conditions of the impact of digital intelligence transformation on the new quality productivity of enterprises are further clarified, which is helpful to clarify the complex and diverse relationship between them and enrich the relevant research on the dynamic resource-based view.

This paper explores the impact of USA technology blockade on China's technical cross-border mergers and acquisitions (M&A) from third-party economies by using the difference-in-difference (DID) model based on the SDC M&A database. We find that USA technology blockade against China has increased China's efforts to implement technological cross-border M&A in third-party economies. The mechanism analysis shows that this is the result of the combination of demonstration effect decreasing China's technological M&A and substitution effect which encouraged that in third-party economies highly alternative or closely linked to China's high-tech sector. In addition, the political and economic relationship between the host country and USA is not a key factor of the effect we interested. Heterogeneity analysis finds that the medical and telecommunications and software service fields are positively stimulated by the USA technology blockade, but the energy fields are the opposite.

Although more and more important tasks and decisions have been entrusted to the algorithm, the computational complexity and opacity of the algorithm make it difficult for users to understand the decision-making process and results of the algorithm, resulting in their difficulty in trusting the algorithm, and even the phenomenon of "algorithmic aversion". Accordingly, algorithmic transparency is often seen as the foundation of trustworthy artificial intelligence and has received considerable attention in academic debates over the past few years. However, at the practical level, there are many challenges in implementing algorithmic transparency, which may even trigger certain ethical risks. Based on this, this study analyzes the challenges and risks at the practical level of algorithmic transparency, and points out that at least three dimensions of disclosure, review, and design can be used to solve the current practical difficulties.

The U.S. has long employed technology export controls to hinder the progress and competitive edge of Chinese high-tech enterprises. In recent years, the U.S. has intensified these efforts through the use of Entity List Sanctions, a highly precise and targeted export control mechanism. Between 2018 and March 2023, it imposed 24 Entity List Sanctions on Chinese entities, impacting over 680 Chinese high-tech companies and entities operating in the computer, communications, defense and military, aerospace and aviation industries. By employing this technology blockade, the U.S. aims to impede the progress of independent innovation within Chinese high-tech enterprises. As a result, the impact of U.S. Entity List Sanctions has garnered significant attention and discussion within Chinese academia and industry. However, existing research on these sanctions is primarily theoretical, lacking sufficient empirical findings based on large samples. Moreover, the results from theoretical studies and infrequent empirical analyses often diverge. Therefore, a comprehensive analysis is necessary to examine how U.S. Entity List Sanctions affect Chinese enterprises’ independent innovation, considering both theoretical and empirical perspectives. To address this research gap, our study analyzes the short- and long-term effects of U.S. Entity List Sanctions on Chinese companies’ independent innovation. We also explore the boundary conditions of these effects, examining risk perception, diversification, and resource constraints. Our research primarily focuses on A-share computer and communication industry listed companies from 2013 to 2021. The findings of our study indicate that U.S. Entity List Sanctions have a negative impact on Chinese firms’ independent innovation, primarily driven by short-term effects that diminish over time. Furthermore, our analysis reveals that firms exhibiting positive risk perception, high diversification, and ample resources are better equipped to resist and overcome the constraints imposed by U.S. Entity List Sanctions on their innovation efforts. This study offers practical contributions in several areas. Firstly, it highlights the importance of Chinese confidence in overcoming the challenges posed by U.S. Entity List Sanctions. Secondly, it emphasizes the significance of a positive mindset among Chinese enterprises when approaching these sanctions, encouraging a rational assessment of associated risks while fostering enthusiasm and initiative for team-based innovation. Thirdly, the study advocates for proactive exploration of diversified business models by Chinese companies, facilitating cross-field, cross-business, and cross-product transformations to drive technological evolution and continuous innovation. Lastly, it underscores the need for increased government support for corporate innovation and the alleviation of resource constraints faced by enterprises.

Scientific and technological talents (S&T talents) are the key driving forces to accelerate innovation and economic development. The mobility of S&T talents can enhance the spread and diffusion of knowledge, improve resource integration, and lead to technological innovation. China faces challenges in terms of S&T talents such as brain drain, low talent mobility and uneven talent distribution. Therefore, eliminating obstacles to talent mobility and improving the mechanisms for talent allocation have become important policy goals for China. Regional integration is an important policy tool under China’s urban agglomeration development strategy, which helps eliminate barriers to talent mobility. Based on large-scale publication data of more than 1 million talents, this study uses panel data of 41 cities in the Yangtze River Delta from 2000 to 2018, applies a staggered difference-in-differences model, and explores the effect of regional integration policy on S&T talent mobility. Specifically, we propose three research questions: (1) did regional integration policy in the Yangtze River Delta influence S&T talent mobility within the region; (2) what heterogeneities exist in the impact of regional integration policy on S&T talent mobility; (3) how the city-level development and living environment influences the effectiveness of regional integration policy. This study finds a significantly positive impact of regional integration policy on S&T talent mobility within cities and across cities. The positive effect is more prominent among talents in hard sciences, and those who moved between non-neighboring cities, as well as those who moved between cities in different provinces. Moreover, a larger promoting effect of regional integration policy is observed for cities with a population of over 5 million people. Additionally, this study suggests that regional integration policy slightly increased the net inflow of S&T talents in the cities with populations of less than 5 million population. Besides, this study demonstrates that the number of high-quality universities, innovation output, the development of a third industry, and the introduction of high-speed rail in cities strengthened the positive effect of regional integration policy on S&T talent mobility. These findings suggest a clear Matthew effect in terms of the promoting effect of regional integration policy on S&T talent mobility. This is because large-sized cities and cities with better development and living environment for S&T talents benefit more from regional integration policy in terms of S&T talent mobility. The findings of this study have multiple policy implications for optimizing talent distribution and improving regional governance efficiency. Small and medium-sized cities, and the cities with less advantageous environments for S&T talents’ development and living, should take a more active role in regional integration development to mitigate the Matthew effect concerning S&T talent mobility. They can leverage their proximity to major cities, and collaborate closely with talent center cities to enhance the spillover effect of regional integration policy on talent mobility. By creating favorable environments for scientific research, economic development, and living, they can promote the effectiveness of the impact of regional integration policy on S&T talent mobility, and ultimately improve the overall competitiveness of talents in the region.