With the rapid development of China's competitiveness in science and technology (S&T) innovation in recent years, the United States has embarked on a campaign of suppression and blockade against China in high-tech fields, such as chips, 5G, and supercomputing. Existing studies lack systematic analyses about the background and mechanism of U.S. S&T strategy against China. Based on the major events in the field of S&T in the U.S.-China relations, the typical actions and public policies of the U.S. S&T strategy against China, the article analyses the underlying logic and internal mechanism of the U.S. S&T strategy against China in the context of the chip industry. The study finds that the underlying logic of the U.S. S&T strategy against China is dominated by the “small yard and high fence”, embedded in the “combination punch mechanism” combining “run fast” and “keep away”. The study provides theoretical and practical implications on international S&T relations and innovation policy.
Cutting-edge technologies, epitomized by artificial intelligence, embody characteristics of high risk and high investment. In the development process, a tension arises between the “certain” national strategy and the “uncertain” original exploration. Establishing an innovative organizational model guided by national mission objectives and leveraging the effectiveness of innovation networks is crucial for achieving technological self-reliance in China. This study selects the National Artificial Intelligence Research Institute of the United States as a case study and identifies the mechanism through which it achieves exploratory research under national guidance as follows: ①At the macro-level, top-level strategic decisions are implemented through departmental coordination, embodying national missions and visions. ②At the meso-level, a “distributed aggregation” innovation network is constructed to expand the participation of innovation actors. ③At the micro-level, an operational mechanism involving multiple stakeholders, such as administrative leadership and industry experts, is implemented to integrate strategic guidance with free exploration. Subsequently, this study tentatively proposes a theoretical framework for Cutting-edge technology organizations that balance national strategy and original exploration, consisting of “top-level design—innovation network—operational mechanism”. And in conjunction with practical applications, we propose a cutting-edge technology innovation organization model suitable for China. This model offers policy insights for the development of key common technology and future technology.
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.
The ethical governance of science and technology has become an important agenda for the development and governance of contemporary society. However, the ethical governance of S&T in different countries is not only influenced by their own institutional structures and cultural traditions, but also closely related to their stages of S&T development, which has led to differences in the ethical governance systems and their interrelationships in different countries. Based on a comparative perspective, this paper explores the differences and connections in the formation and operation of ethical governance patterns among S&T advanced countries, as well as between S&T advanced countries and latecomers, and proposes two modes, namely, the "differential mode" under the similar level of development, and the "misalignment mode" under the different levels of development, and uses these modes as the basis for the discussion of "ethical dumping" and global ethical governance of science and technology, so as to provide theoretical support for the development of China's ethical governance system of science and technology.
Based on the framework of innovation adoption, this study proposes a method for identifying disruptive technology that simultaneously satisfies the internal and external characteristics of innovation to identify potential disruptive technologies from the relative nature of innovation. First, Bertopic theme model and patent text information mining techniques are used in stages to obtain new technology functions and discontinuous technology standards respectively, in order to extract the internal characteristics of innovation of disruptive technologies; Second, the CD index, a measure of disruption, is used to extract the external characteristics of innovation in disruptive technologies; Third, the disruptive technologies that simultaneously satisfy the internal and external characteristics of an innovation are identified and classified into new market, low-end and high-end disruptive technologies as well. Finally, taking patents in the field of energy storage as an example, we verify the feasibility and effectiveness of the disruptive technology identification method in this paper. The identification method in this paper is able to match the definition of disruptive technology with its identification, express the interaction between technological innovation and market adoption, and distinguish different types of disruptive technologies. This identification method could provide a reference for enterprises to determine whether a new technology has a disruptive impact.
Due to its versatility and permeability, artificial intelligence technology will also bring serious challenges to China's economic, social, political and other fields, such as the failure of traditional bureaucratic supervision and how to distribute public responsibilities for algorithms and data. It has affected many parties including governments, enterprises and individuals. Neither purely technical solutions nor written legislative supervision can effectively deal with artificial intelligence ethical issues and governance dilemmas. As a new path, artificial intelligence ethical governance can reshape resource allocation methods and organizational system innovation to better deal with the depth and complexity of artificial intelligence ethical risks.
Based on Weber's rational dichotomy analysis framework, we deconstruct the value rationality and instrumental rationality contained in artificial intelligence ethical governance, and analyze the basic concepts of artificial intelligence ethical governance. The value rationality dominated by artificial intelligence ethics enables us to carefully examine the future of technological governance, laws and policies, and understand human needs for new artificial intelligence innovations from a unique perspective; the instrumental rationality dominated by artificial intelligence governance enables us to advance more pragmatically Thinking and solving ethical issues, thereby improving the overall benefits of the development of artificial intelligence technology. On the basis of the two-dimensional rational distinction, artificial intelligence ethical governance absorbs the idea of public interest and advocates a governance path from value to tool and then to value. From the two-dimensional way of thinking of value rationality and instrumental rationality, the concept of artificial intelligence ethical governance includes five core elements: human-centered value orientation. Adhere to the basic principles of scientific and technological ethics. Adhere to the principles of fairness, responsibility and transparency. The objects of artificial intelligence ethical governance include not only the systems produced by artificial intelligence technology, involving artificial intelligence computing power, algorithms, data, etc., but also the scenarios of artificial intelligence application. Multi-agent participation. Diversification of governance tools.
The two-dimensional rationality of artificial intelligence ethical governance radiates into the corresponding governance behavior, resulting in the dual dimensions of value orientation and practical orientation of ethical governance. The realization of the value direction of artificial intelligence ethical governance can be approached from two levels: governance concept and governance logic. This is a layered result based on the dual rational fusion of artificial intelligence ethical governance. The value rationality of artificial intelligence ethical governance not only requires the guidance of traditional governance concepts, but also relies on the support of the organizational structure and power structure in the governance logic. In terms of governance concepts, by popularizing the consensus on artificial intelligence ethical values and clarifying the goals of artificial intelligence ethical governance, the overall ethical governance awareness of society is improved; in terms of governance logic, the organizational structure is compressed and the power structure is reorganized to enhance the collaboration and flexibility of the governance structure. . The practical approach to artificial intelligence ethical governance should focus on the two dimensions of governance tools and governance subjects. This is the result of artificial intelligence ethical governance absorbing institutional rationality. The establishment of an appropriate institutional environment relies on the exchange of information and close interaction among multiple governance subjects. In terms of governance subjects, strengthen the integration of domestic artificial intelligence ethical governance subjects and establish an international artificial intelligence ethical governance partnership; in terms of governance tools, maintain tracking of iterations of artificial intelligence technology, enrich artificial intelligence ethical governance methods, and build artificial intelligence ethical governance tools system.
Enterprise basic research on enterprises is an important cornerstone of the construction of a technological country, which helps to promote the deep integration of scientific research and industrial development. Compared with developed countries, there exist a lack of basic research investment in Chinese enterprise. To improve enterprise’s investment in basic research is vital for indigenous innovation in China. Based on the perspective of knowledge spillover, this study constructs the correlation data between science publication and patent science citations of Chinese manufacturing listed companies from 2000 to 2022, exploring the knowledge spillover mechanism of enterprise technology literature on focal enterprise patents and on competitive enterprise patents. We suggest that basic research can be viewed as the input of applied research and development. However, knowledge spillover may result in incentive distortion of enterprise’s basic research investment. By distinguishing knowledge internal use and knowledge spill-out, we study the incentives, mechanisms and market effect of Chinese enterprise’s basic research.
The sample of the research is listed manufacturing enterprises in China’s A share stock market. We use the enterprises which has at least one year of positive R&D investment and at least one patent application. This gives us a non-balanced panel of 29247 firm-year observations with 2902 unique firms. Patent data is from Incopat database. Publication data is from CNKI database. Financial data is from CSMAR database. We match data from different databases. To identify the relationship between patent citations in enterprise basic research publications, first, 186148 patents with scientific citations were obtained after cleaning the original 1.8 million patents. Considering the situation where multiple patents refer to the same scientific literature, the original information of cited scientific literature was ultimately cleaned out. Secondly, considering that there may be some errors in the titles of scientific papers in CNKI and Incopat databases, fuzzy matching was performed on the title of articles and patent cited scientific literature in CNKI. Using Natural Language Processing (NPL) and Python_ Gensim library, we calculate the similarity between two database paper name strings. Using jieba word segmentation on the literature title strings of the two datasets, we construct an index dictionary of corpus features, and use a word bag model to construct a similarity matrix for fuzzy matching.
We use twoway fixed effect model in our empirical research. Research findings are: (1) Knowledge internal use promotes focal enterprises to conduct basic research, while knowledge spill-out decreases focal enterprises from conducting basic research. (2) The citation of competitive enterprise patents to focal enterprise science literature can help improve the innovation performance of focal enterprises. (3) Compared with knowledge internal use, knowledge spill-out in basic research significantly decrease the financial performance of the focus enterprises. Robustness checks using Heckman model gain the same results. Heterogeneity test indicates that knowledge spillovers has a more significant impact on the basic research of non-state-owned enterprises. Basic research knowledge internal use promotes the applied research of high-tech industries and small-scale enterprises, while basic research knowledge spill-out promotes the applied research of non-state-owned enterprises and large-scale enterprises. This article reveals the incentives, spillover mechanisms, and market effects of Chinese enterprise basic research. The conclusions provide policy implications for consolidating the important position of enterprises in scientific and technological innovation, and guiding the deep integration of basic research and applied research.
The hazards of “publish or perish” have garnered significant attention. However, the impact of publication pressure on the direction of scientific research through researchers’ cognition and behavior remains underexplored. As the primary agents of scientific activities, researchers act as intermediaries in the scientific reward system, facilitating the positive development of scientific research. Both the Merton School and the Paris School argue that the operational logic of science rewards depends on the institutional environment that shapes the behavior of researchers. However, their perspective overlooks the fact that researchers’ reflexive cognition may be altering the trajectory of scientific development from the bottom up.
Reflexivity refers to an individual's reflection on past situations, which actively influences the events in which they participate. This is a constructive process where reality impacts cognition, and cognition, in turn, generates new realities. Publication pressure is not merely a product of the environment acting on the individual; it also drives individuals to construct new realities based on their cognition. Therefore, this paper explores the impact of publication pressure on scientific research, emphasizing the reflexive role of researchers in the scientific enterprise and re-examining whether science rewards are oriented towards a responsible and innovative development path.
This paper addresses the issue of reflexivity in the science reward system and constructs the reflexive relationship of “science reward and evaluation system - publication pressure - scientific research.” Publication pressure is intensified by the scarcity of resources highlighted through scientific reward mechanisms and the increased publication requirements of a quantitatively oriented research evaluation system. Researchers reflexively choose to deviate from the optimal development of scientific research in terms of research attitudes (diffusion of responsibility in collaborative research), research topic selection (marginalization of repetitive research), and research writing (positivity bias in academic language). The excessive focus on innovation within scientific reward mechanisms overlooks the need for responsible, objective, and reproducible scientific research. Therefore, it is crucial to manage responsibility proactively and promote a shift in publishing practices to encourage pragmatic innovation.
Exploring the effect of basic research on firms’ scientific breakthroughs can help firms asses the effectiveness of investments in basic research. However, the lack of fine-grained measurement method of basicness has hindered this research. To this end, using Level Score (LS) to measure the basicness of a single paper, this paper explores the influence of basic research on firms’ scientific breakthroughs using regression analysis based on the publication data of global biomedical firms from 2015 to 2017. The results show that, first, the number of basic research papers and applied research papers referenced by biomedical firms can significantly and positively affect the number of papers they produce, in line with the basic input-output relationship. Second, basic knowledge plays a key role in the output of firms’ scientific output. Enhancing the average basicness of knowledge inputs facilitates biomedical firms to produce more scientific breakthrough papers. Finally, we also find that in terms of research types, a more diverse knowledge combination increases the likelihood of producing scientific breakthroughs. This paper empirically validates the contribution of basic research to scientific innovations. In addition, this paper can provide a scientific basis for firms in the field of biomedicine to increase their investment in basic research and enhance their basic research capacity, and also provide certain insights for the formulation of related scientific and technology policies.
Technological innovation of enterprises, especially breakthrough technological innovation, is an important engine to drive the high-quality development of China's economy and realize Chinese-style modernization. The establishment of Pilot Free Trade Zones (FTZs), with breakthrough institutional innovation as its core task, provides a suitable external institutional environment for promoting breakthrough technological innovation of Chinese enterprises under the new development pattern. The article utilizes the relevant data of A-share listed companies matched to the prefecture-level city level from 2009 to 2020, regards the establishment of the Pilot Free Trade Zone (FTZ) as a quasi-natural event, and employs a multi-period double-difference method to explore the effect of the establishment of the PFTZ on the breakthrough technological innovations of the listed companies registered in the zone. The study finds that the breakthrough institutional innovation after the establishment of FTZ can significantly promote the breakthrough technological innovation of listed companies registered in the zone, and this conclusion still holds after various robustness tests; in-depth analysis of the mechanism of action reveals that the breakthrough institutional innovation of the FTZ can promote enterprises to realize the breakthrough technological innovation by alleviating the financing constraints faced by listed enterprises in the zone, promoting the cooperation between the industry, academia, and research institutes, and facilitating the agglomeration of human capital for breakthrough technological innovation, thus promoting enterprises to realize the breakthrough technological innovation, and promoting enterprises' development of human capital. The further heterogeneity analysis finds that, on the one hand, FTZs have a more significant impact on the breakthrough technological innovation of non-state-owned enterprises and large-scale enterprises, and on the other hand, FTZs that were established earlier and geographically located in the coastal area have a stronger effect on the promotion of breakthrough technological innovation of the enterprises in the zone.
The evolution of science and technology is one of the most important and complex issues in academia. With the accelerating integration of scientific, technological, economic, and social development, industrial technological innovation has become a focal point of theoretical research. However, existing studies have primarily focused on the influencing factors of industrial innovation performance from the perspectives of policy support, technological development, and market economy, often neglecting the crucial role of scientific research at the forefront of industry. This study, from a science-driven perspective, takes the 5G communications industry—a typical science-driven industry—as an example. It creatively combines the fsQCA method with simulation modeling analysis to better depict the evolutionary process from scientific research to technological innovation.
This research has yielded several valuable conclusions. Firstly, the complex and dynamic evolutionary process between science and technology is a process where quantitative changes in science lead to qualitative changes in technological innovation. This process is influenced by multiple factors such as scientific research investment and output, the intensity of scientific linkage, scientific collaboration networks, and firm scientific capabilities. The interaction between these influencing factors can accelerate the evolutionary process and shorten the evolutionary cycle. Science-based innovation is the core condition for transforming scientific research into technological achievements and is the decisive factor for industrial technological breakthroughs. The diffusion and application of science-based innovation results spur a large number of technological innovations, thereby pushing the industry into a rapid development cycle. Moreover, the heterogeneous effects of multi-actor participation in scientific research on the evolutionary process of technological innovation performance are evident. In the early stages of industrial development, scientific exploration mainly relies on universities and research institutions. However, firm participation in scientific research can effectively promote the transformation of science into technology and subsequent commercialization. On the basis of scientific breakthroughs, firm participation—particularly the scale of participation—plays a more critical role in promoting technological innovation performance. Additionally, this study proposes three configuration paths for enhancing technological innovation performance: all-element science innovation-driven type, dual-element scientific ecosystem-driven type, and single-element scientific linkage absorption type. Different development paths can be selected based on the varying conditions of science-driven factors in a country or industry.
Based on the research conclusions, this paper offers several policy recommendations for developing science-driven industries in China. These include constructing a science-driven national innovation system, creating new types of industry-university-research collaborative innovation organizations, emphasizing the cultivation of science-based innovation, and optimizing the layout of scientific research investment to leverage its guiding value. These recommendations have strong practical implications.
In summary, this study provides a comprehensive analysis of the evolutionary process from scientific research to technological innovation in the 5G communications industry, highlighting the multifaceted influences and interactions that drive this evolution. The findings underscore the importance of scientific research as a foundational element in industrial technological innovation and offer actionable insights for policymakers and industry stakeholders aiming to foster science-driven industrial development.
Users have been recognized as a vital business resource and strategic asset for enterprises. Crafting an exceptional user experience has increasingly become the core tenet of product innovation. However, a pleasurable user experience often seems to be at odds with the commercial development of products. Therefore, this study, grounded in paradox theory, explores how enterprises can manage the dialectical relationship between the "small and beautiful" user experience orientation and the "large and comprehensive" corporate commercial orientation, revealing the micro-mechanism through which user experience indirectly drives product innovation. Through a longitudinal case study of WeChat, this paper proposes a dichotomy of user experience dimensions for the experience economy era—strong functionality/service and strong emotion—and constructs a dynamic equilibrium model for experience-driven innovation flow. The study finds that product innovation can be driven by either strong functionality/service or strong emotional user experience, through one or several composite architectural innovation methods such as incremental, radical, modular, and architectural, and evolve within different experience network models such as individualisation, integration, combination, and ecosystem. This approach allows for the creation of an ultimate user experience while gradually exploring product commercialization. The appropriate sequence and pace of different experience network models during the evolution process will facilitate enterprises to utilize experience-driven product innovation from the top down, providing insights for product innovation in the era of the experience economy.
Based on the background of "talent competition" and the perspective of regional talent policies, this paper uses natural language parsing (NLP), text mining to collate the quantitative data of 3308 local talent policies in China from 2002 to 2021, designs the quantitative standard, process and evaluation system of talent policy under the dimension of "multi region-multi policy", on this basis, combined with individual micro data, investigates the influence of urban talent policy on talent migration decision. The findings are as follows: 1. The improvement of the comprehensive score of urban talent policy can significantly increase the probability of the city being selected by talents. From the perspective of cities, the effect of talent policy is more obvious in smaller and non-capital cities. In terms of individual characteristics, the group of highly-educated, high-income and the young and middle-aged talents aged 25-54 are more sensitive to the change of the comprehensive score of talent policy. 2. Specific to the four types of policy links, There are significant differences in the actual effect of talent policies issued by cities of different geographical regions. The policies of introducing, retaining and employing talents in eastern coastal cities and southern cities can significantly affect the inflow of talents, while in inland areas, the effect of the policy of cultivating and employing talents is not obvious, in northern areas, only the policy of retaining talents can make a difference. 3. From the perspective of the three types of policy tools, different age groups and different types of talent groups also have different preferences. From the perspective of age, the talent group under 35 years old is more sensitive to short-term and subsidized content changes such as subsistence allowance and salary benefits, while the talent group over 35 years old is relatively more concerned about children admission and personal career development and other safeguard and development policies. In terms of different types of talents, the innovative and entrepreneurial talents are very concerned about changes in development policies such as financial support and innovation carrier construction, the enterprise management talents are more concerned about incentive policies such as subsistence allowance and personal income tax incentives. Rural practical talents pay more attention to safeguard policys, such as household policies.
Modern high-tech enterprises are simultaneously embedded in various types of networks, yet there is limited research analyzing how these networks coevolve. This paper adopts a dual network embedding perspective and utilizes data from 29,145 shareholders and 35,597 directors of 338 listed public companies in the Chinese biotechnology industry from 2009 to 2021 to construct shareholder relationship networks and interlocking directorate networks. Employing social network analysis and Temporal Exponential Random Graph Model (TERGM), the paper investigates the evolutionary paths of organizational dual network embedding. The findings indicate that during the evolution of the shareholder relationship network, the internal structure becomes increasingly compact, exhibiting small-world characteristics, while the interlocking directorate network demonstrates relatively fewer internal connections, resulting in an overall sparse structure. Both networks' evolutions exhibit significant spatial homogeneity characteristics. Moreover, the establishment of relationships in the shareholder relationship network demonstrates ownership heterogeneity, indicating a higher probability of connection between state-owned and non-state-owned enterprises. The dual network embedding process demonstrates a cross-network Matthew effect, where enterprises with a greater number of connections in one network are more likely to establish new connections in the other network. By adopting a dual network embedding perspective, this study moves beyond the confines of single-network view, reflecting the practical embedding of high-tech enterprises in different networks. It examines the endogenous formation mechanisms of the shareholder relationship network and the interlocking directorate network, offering a clear depiction of the evolutionary trajectory of dual networks.
In the era of artificial intelligence, the very idea of the technological delegation could be focused on discussing the issue of artificial intelligence acting on behalf of humans and accepting tasks entrusted by humans while temporarily suspending the question of the qualification of artificial agents. Then, artificial intelligence could complete a part of the action plan on behalf of humans, rather than the whole action. With the development of artificial intelligence, the scope of technological delegation gradually expands from the mimeomorphic action to the behavioral groups, and ultimately extends to the entire action coordinate system, and make the machine a complete agent. Through technological delegation, humans and artificial intelligence have formed a special human-machine interaction relationship, which has brought about problems such as distance and anonymity, and created new action levels, thereby having important and complex impacts on morality. Since moral good and evil have arisen from the practice of human-machine interaction, correspondingly moral responsibility should also ascribe to the human-machine joint actors. The combination of action responsibility and social role responsibility can continue to allocate joint responsibility to different individual actors.
The global landscape of AI presents both opportunities and obstacles. Over the decades since the concept of Artificial Intelligence (AI) was initiated, AI has witnessed waves of development, from early attempts to create human-like conversational agents to the recent surge in deep learning and big data. The 21st century has seen remarkable breakthroughs, with applications spanning various industries, including technology, healthcare, and education. Efforts to address AI's impact on society are evident in the development of ethical guidelines and regulations. Countries and regions around the world are working on refining legal frameworks for AI. While research and technology advancements are rapid, the commercialization of AI encounters persistent barriers. Despite significant progress, ethical debates concerning AI's interaction with human society are still heated, and AI applications also grapple with unresolved issues in pertinent scenarios. For the technological innovation problem, large language models, such as those based on the Transformer architecture, highlight the struggle for efficient data utilization and the associated cost of developing advanced algorithms. For the commercialization practice, the burst of the AI investment bubble and discussions about AI potentially replacing traditional labor further complicate the industry's trajectory. As the AI industrial ecosystem evolves, there is a need for coordinated solutions and the development of comprehensive industry standards to propel the AI industry toward sustained and healthy growth. This paper concludes significant hurdles as follows: Firstly, there is a misalignment between AI model demands and industry integration, resulting in a talent gap and high commercialization costs. Secondly, there is a structural imbalance in talent supply and demand, with a shortage of high-quality AI professionals. Lastly, on the supply side of AI models, technical bottlenecks and limitations are hindering broad applications. Additionally, the lack of well-established ethical standards and industry norms globally poses challenges in governance, impacting the acceptance and effective utilization of AI applications. The journey of AI from its conceptualization to the present day reflects a continuous struggle between technological advancements and the complexities of societal integration. The "i7C" framework, which integrates seven key elements: data for computing, computing arithmetic, computing power, computing knowledge, computing scenario, computing talents, and ethics in computing, is proposed to address these obstacles. The "i7C" framework builds a robust AI infrastructure with hardware capabilities such as data for computing, computing arithmetic, and computing power. With computing knowledge and scenarios, it promotes the commercial application of AI technologies. Besides, it systematically cultivates computing talents, fully implements industry application standards, actively responds to ethical concerns in society, and forms a strong cultural bond to drive collaborative and mutually beneficial efforts among all members of the ecosystem. Thus, the AI industry ecosystem achieves sustainable development. To achieve the sustainable development of the AI business ecosystem, core enterprises in the AI industry ecosystem should collaborate with leading partners to collectively build AI infrastructure, drive the commercial application of AI technologies, and together foster a sustainable cultural bond to attract and stimulate more AI ecosystem partners. The AI industry faces challenges in implementation, unclear regulations, and a costly, low-sharing environment. To address this, the “i7C” framework is essential for AI to integrate seamlessly and empower diverse industries.
In the era of digital economy, the statistical accounting of data elements is a fundamental task for the strategy for digital economy and development plan for data elements. However, theoretical research and statistical practice haven’t yet reached a consensus on the statistical accounting of data elements. Based on the research on the concept and development of data elements, this paper based on the logic of “input-transformation-output”constructs an evaluation index system from the three dimensions of data infrastructure facilities, data transformation capability and data industry application to analyze the development level of the data elements from 2013 to 2020 in 30 provinces and three major regions of China. The results reveal that: first, the overall development level of China’s data elements has been improving year by year, but there are significant differences among the three regions, with an obvious “high in the east and low in the west” stepwise decline feature. The development level of data elements in China shows a step-down trend from the east to the west, but the growth of the development level of data elements shows an "inverted V-shaped" trend: the central region changes the fastest, followed by the west, and the eastern region changes the slowest. There are obvious differences in the development level of data elements in the eastern and central regions, but with the acceleration of the development of data elements in the central and western regions, regional differences appear a "leveling effect", which effectively alleviates the trend of expanding the difference in the development level of data elements in the eastern and central regions, and breaks the "Matthew effect" of "the strong remain strong, the weak remain weak". Second, it shows an overall spatial pattern of “east-central-west” in decreasing order. The differences within regions are the main source of the overall differences, with the contribution rate of the eastern region being the highest. In addition, the Gaussian kernel density curve shows clear regional differences in the development of each dimension of data elements development. Third, the overall difference of the development level of data elements in China has showed a fluctuating downward trend during 2013-2020, the regional difference has changed from inter-regional difference to intra-regional difference, and the intra-regional difference in eastern, central and western regions decreased from high to low. At last, the spatial distribution of the whole development of data elements and the level of data Industry application shows a non-equilibrium and progressive evolutionary trend, with significant spatial agglomeration features. From perspective of the dimensions of data elements development, the level values of data infrastructure facilities, data transformation ability, and data industry application show an overall growth trend, the growth rate of data industry application is the most significant among the three dimensions. All in all, the development of data elements in China has made certain progress, but the problem of uneven, inadequate and uncoordinated regional development is prominent. In the future, targeted policies should be formulated according to local conditions to promote coordinated regional development and accelerate the development of data elements in order to fully release the dividend of data elements and achieve high-quality development. While firmly promoting the construction of digital infrastructure, the government should focus on the integration and development of the digital economy and the real economy, optimize resources allocation in order to realize the coupling of digital industrialization and industrial digitalization. The research results enrich and improve the literature on the evaluation index system for the development of China’s data elements, providing practical reference for further promoting the construction of digital economy.
Public participation is irreplaceable in many paths of ethical governance in science and technology. Analyzing the concept, purpose, and benchmark of public participation can support the institutionalization of public participation. The idea of public participation develops dynamically with the progress of practice and theory, and its connotation is constantly enriched, tending to emphasize the universality and representation of the public and the depth and diversity of participation. Public functional departments and all participants in the field of science and technology jointly establish the ethical principles regulating science and technology activities. The public is the main body of action in the ethical governance of science and technology. It can contribute opinions to science and technology policies, supervise science and technology activities, and even contribute creative power. Public participation in the ethical governance of science and technology should at least aim at the five values of science, welfare, justice, respect, and efficiency. The knowledge extension should get the support of the public, and technological progress should benefit the public. Individual, social, and human well-being should be equally valued, and protecting research participants is as important as the research's scientific and social value. With an in-depth understanding of the public attributes and social impact of science and technology activities, public participation has become necessary for the ethical governance of science and technology. Public participation is needed to ensure distributive justice, return justice, and procedural justice in scientific and technological activities. It is necessary to listen to public opinions, accept public supervision, and rely on public action to achieve technology for good and respect human and civil rights. The public could provide a holistic view of the multiple benefits of scientific and technological activities. The criteria for public participation in the ethical governance of science and technology should include proper setting of objectives, fair selection of representatives, appropriate design of forms, an open and transparent process, and effective results. Public participation activities should have more well-defined objectives to achieve their intended goals effectively. When designing a public participation activity, we should intentionally select public representatives to ensure fairness until we establish a culture of active public participation. The format of public participation activities should be customized to the local conditions while relying on existing research to choose the appropriate method based on the action's objectives. The researcher and the research organization are responsible for initiating invitations or accepting requests for public participation. Still, the specific form must be proportionate to the particular objectives, and there should not be a moral obligation for researchers to give unconditional support to any form of public participation. From the perspective of procedural fairness, the process of public participation activities should be open and transparent. On the one hand, this is to expand the number of participants, broaden the breadth, and strengthen the depth of participation. It is essential to scrutinize public involvement as a means of ethical governance. Each public participation activity must have clear objectives. In some cases, the public may not have a legal right to participate in the ethical governance of science and technology, or there may be a lack of clear channels for such participation. However, where the public does have a legal right, it is necessary to provide clear participatory procedures openly to involve the public in the ethical governance of science and technology.
