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.

In recent years, a large number of digital platform enterprises with ecological strategies have flourished and many valuable research works have emerged. However, different research fields use their own terms, theories and methods, resulting in theoretical differences on many important issues, which will not only cause ambiguity in practice, but also hinder the further development and integration of digital platform ecosystem researches. Therefore, this study attempts to systematize the researches related to digital platform ecosystem by sorting out the core views of different research schools. On this basis, this study further expounds the possible research directions of digital platform ecosystem in the future, contributes knowledge to the development of platform ecosystem in the face of the digital economy. Some existing studies have confused the concepts of "platform", "platform ecosystem" and "digital platform ecosystem" to a certain extent. This study firstly clarifies the definitions of platform, platform ecosystem and digital platform ecosystem to avoid the ambiguity and confusion of similar concepts. At the same time, this study selects 368 papers that are highly relevant to digital platform ecosystem based on the differences in the concepts and research content of platform, platform ecosystem and digital platform ecosystem, and conducts a statistical analysis of the literature. Secondly, this study compares the research focus of the economic perspective, technical perspective, organizational perspective and strategic perspective by reviewing the literature related to digital platform ecosystem. The economics perspective views platforms primarily as two-sided markets, focuses on the network effects, platform competition and interdependence of different aspects of the market. Technical perspective emphasizes platform as system, focusing on technical architecture design and architecture evaluation of digital platform ecosystem. The organizational perspective explicitly conceptualizes the platform and complementors as a unique form of organization: the meta-organization. The strategic perspective views platforms as a way to find strategic coherence, focuses on the organizational and governance characteristics specific to platform to extend existing strategy theory and examine how complementors use different strategies to influence their value creation activities. Finally, this study proposes directions for more in-depth exploration of future research themes, research content and research design from the perspectives of economics, technology, organization and strategy, integrates the views of various research schools and proposes a theoretical framework, in order to promote the development and integration of future research. This study attempts to break through the isolation between research schools through a systematic literature review to achieve the sorting and integration of cross-disciplinary research. It contributes to the future study of digital platform ecosystem that closely links platform phenomena with classical theories, realize dynamic, richer research scenarios and diverse research methods. The main contributions of this study are as follows. Firstly, drawing on previous researches, we provide definitions and conceptualizations of platform, platform ecosystem and digital platform ecosystem to avoid confusion between these three concepts. Secondly, this study summarizes the core issues of existing research from economic perspective, technical perspective, organizational perspective and strategic perspective, and helps to generate more dialogue across different research schools. Thirdly, we propose possible future research directions for the digital platform ecosystem and hope to make more rapid progress in future research and practice, thus contributing to the study of the digital platform ecosystem.

“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.

Algorithmic fairness is a topic of common concern in the field of artificial intelligence and information ethics. It comes from algorithm bias in algorithm decision systems. Although technicians have made much progress in avoiding algorithm bias, there is still no consensus on how to achieve algorithm fairness at the technical level. Based on this, this research first analyzes the methods and defects of realizing algorithmic fairness from the technology path. Then, along with the current criticism and revision of realizing algorithm fairness from the single technology path, it proposes a social research framework based on Brey's "Disclosive Computer Ethics", which provides a more reasonable solution to the problem of algorithmic fairness.

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 current complex and severe internal and external environment brings unprecedented impact to enterprise development, and how to improve enterprise innovation resilience has become a realistic problem to be solved. This paper clarifies the connotation and characteristics of enterprise innovation resilience and its measurement method, discusses the factors affecting enterprise innovation resilience, and analyzes the theoretical mechanisms affecting enterprise innovation resilience from several perspectives of providing redundant resources, alleviating financing constraints, and optimizing resource allocation. For further validation, venture capital, an important variable influencing firm innovation resilience, is selected for empirical analysis. Based on the quarterly data of listed companies in GEM from 2009 to 2020, this paper combines the patent data, venture capital data and venture capital organization data. It is found that venture capital support significantly enhances firms' innovation resilience, and this effect is heterogeneous by firm characteristics. In addition, the type, nature, and location of venture capital play a significant moderating role on this effect. Based on the research results, relevant policy recommendations are proposed, which provide useful references for firms to survive and grow in turbulent times.

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.

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.

The emergence of knowledge broker makes it possible to cross the "Caplan Gap" between academia and politics, which hinders the translation of knowledge into policy. Then, what is knowledge broker? How does it facilitate the transformation of knowledge into policy? The paper first explores the origins, connotations and types of knowledge broker, and then explores the knowledge management function, bridging function, two-way capacity building function, coherence seeking function and knowledge navigation function of knowledge broker in the alignment network linking academia and politics from the perspective of "structural hole" theory. However, knowledge broker also faces three highly challenging and controversial issues, namely, vulnerability, double marginality, and moral hazard. Theoretically clarifying the issue of knowledge broker has important implications for bridging the "last mile" from academic research to public policy in China.

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.

Does ChatGPT signal an "intelligent revolution"? It is urgent to make a systematic analysis. In this paper, starting from the evaluation of ChatGPT's innovation effects, the comparative analysis found that it belongs to progressive innovation and disruptive innovation in the series of OpenAI products; Compared with similar competitive products, it is indeed a breakthrough innovation and subversive technological innovation. In contrast to AI in China, ChatGPT has become an important part of the "bottleneck" technology system. It is found that ChatGPT, as a "production tool", forms a paradigm revolution in the supply of productivity tools in the field of artificial intelligence. At the same time, it also realizes the paradigm change of AI ethical governance. The organizational structural elements generated for ChatGPT can be traced back to the organizational mission of OpenAI, the flat project-oriented organization, the financing mechanism innovation with limited returns, and the efficient coupling of open source and crowdsourcing R&D organizational solution in the process of AI ModelOps. These findings lay a solid foundation for further research on ChatGPT's political, economic, and social impacts and related technological 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 present paper constructs an evaluation index system to measure the development level of the digital economy, and then measures the provincial manufacturing carbon emission efficiency based on the MinDS model of global benchmark technology. Then, the present paper empirically tests the impact of the digital economy on China's manufacturing carbon emission efficiency, and conducts a comparative analysis of the action paths. The present paper found that the development level of China's digital economy tends to rise during the survey period. But there is a significant digital divide, which is reflected in the high development level of the eastern region, while the development of the central and western regions is relatively low. The carbon emission efficiency of China's manufacturing shows a upward trend, and presents a spatial pattern of gradual decline from east to west. The digital economy has a significant U-shaped nonlinear impact on the carbon emission efficiency of manufacturing, supporting the hypothesis of EKC. The path test results show that the labor efficiency enhancement effect and the energy factor saving effect are two feasible paths to play the digital carbon reduction effect, and the latter has a relatively greater carbon reduction effect.

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.

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.

From the perspective of technological evolution, the metaverse is the conceptual expression of the technology bodies combination in the development of digital information technology to a certain stage.From the "internal perspective", it is proposed that the essence of metaverse technology is purposeful programming of information digital phenomena and the core mechanism of technology evolution is "combinatorial evolution".Based on this, following the paradigm of complex economics and technology economy, through the research methods of theoretical application, inductive-inference and analytical speculation, the paper demonstrates that the metaverse technology and its industrial development are the internal logical relationship of circular and mutual promotion.The main conclusions are: the metaverse is an integrated expression of the development of the complex digital economy industry; The metaverse technology can promote the subversive change of the industrial structure of the digital economy.Among them, the key technology component module of the the metaverse, the mechanism and path of technological self creation and innovation, the algorithm of technological aggregation and economic formation, and the technology and economy redomained to promote industrial development were clarified.Studying the internal logical relationship between "technology essence and industrial development" has important thinking direction and practical reference significance for many stakeholders such as government industrial planning departments, technology developers, enterprise manufacturers and investors.

With the rapid development of science and technology, the challenges faced by science and technology ethics are increasing. Science and technology ethics governance has become a highly concerned topic for governments, academia and the public. One of the outstanding problems is how to deeply integrate "science and technology" and "ethics". With this as the starting point, this paper systematically analyzed the current situation and problems of the integration of "science and technology" and "ethics" in China's science and technology ethics governance, and explored the value and significance of the deep integration of "science and technology" and "ethics", and put forward countermeasures and suggestions for the deep integration of "science and technology" and "ethics" from three aspects: strengthening the education and popularization of science and technology ethics, building a dialogue mechanism and exchange platform, and improving the science and technology evaluation and incentive mechanism, with the aim to promote the construction of China's science and technology ethics governance system and the healthy and orderly development of science and technology.

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.