Universities in China have achieved remarkable results in technological research, playing a leading role in constructing an independent knowledge system. However, university technological achievements still face the bottleneck of relatively low rates of industrial transformation. Addressing the challenge of translating university research outcomes requires multi-party collaboration, establishing a public transformation platform involving diverse actors such as universities, enterprises, and technology transfer institutions, and improving the specialized service system for technological achievement transformation. Therefore, public data openness emerges as a novel solution, offering new possibilities for technology transfer. Public data openness refers to the process by which governments integrate public data resources and make them equally accessible to the public. It grants the public the rights to know, query, and utilize data, facilitating better resource discovery and industrial upgrading by societal actors, thereby creating commercial and social value. The transformation of university technological achievements is a “university-enterprise” collaborative innovation process. For enterprises, public data openness provides access to data resources at lower cost. The data elements not only empower enterprise R&D but also reduce the uncertainties they face. For universities, public data openness helps eliminate information barriers between universities and enterprises, and facilitate the coupling of university achievements with market demand. In this regard, investigating whether and how public data openness influences the technology transfer in higher education institutions is of significant importance for enhancing university technological development.

This study employs the launch of government data platforms as a quasi-natural experiment. Based on data covering Chinese university patent transfers from 2009 to 2023, a multi-period difference-in-differences (DID) model is used to investigate the impact of public data openness on the technology transfer of universities. The research findings demonstrate that public data openness positively promotes the transformation of university technological achievements. This conclusion still holds true after passing the parallel trend test, placebo test, double machine learning causality test, psm-did test and removing other policies’ effect. The mechanism test demonstrates that (1) Public data openness facilitates the coupling of university achievements with enterprise demand, promoting transformation through university-industry collaboration. And public data openness increased the number of university-industry jointly applied patents.(2) Public data openness effectively reduces transaction costs between universities and enterprises and alleviates the time lag in technology transfer. And public data openness exerts a stronger promoting effect in regions with protracted conversion delays. Heterogeneity analysis reveals that the impact of public data openness on university achievement transformation is more pronounced in regions with higher data openness quality and better digital infrastructure. Furthermore, its promoting effect is more obvious for invention-type patents and high quality patent transformations. This study bridges a significant gap in the literature by empirically demonstrating that public data openness facilitates university technology transfer.By providing rigorous evidence on how it overcomes the “last-mile barrier” in this process, our findings expand the research horizon on open public data governance.

This study provides the following insights for leveraging public data openness to optimize university technology transfer service systems. At the government level, authorities should accelerate public data disclosure while guiding multi-stakeholder participation in platform development to establish a cross-sectoral “communication bridge”. Differentiated policy support must be implemented through targeted interventions and technical guidance for regions with inadequate digital infrastructure, thereby bridging regional divides in technology commercialization. Universities should proactively deepen industry-academia partnerships through joint R&D initiatives to boost industrial applicability of innovations, while comprehensively showcasing research outputs via dedicated platforms spanning fundamental research to market applications to enhance external visibility. Prioritizing high value patent development tightly aligned with market needs remains critical for overcoming commercialization bottlenecks. For enterprises, dynamically monitoring university research outputs and acquiring relevant technologies through patent transfers can mitigate information asymmetry induced delays. Companies must also proactively articulate technical requirements, budgetary parameters, and collaboration frameworks to establish closed-loop innovation cycles.

The Not-In-My-Backyard (NIMBY) effect, as a typical governance challenge in environmentally sensitive infrastructure projects, fundamentally stems from the structural imbalance between technological rationality and socio-ethical values. From the perspective of socio-ethical governance, this study takes the Hangzhou Jiufeng Waste-to-Energy Project as a case to systematically analyze the internal mechanisms and practical pathways of its transformation from the "NIMBY effect" to a "Neighborhood-Benefit Project," based on field investigations. By adopting a "macro-engineering" philosophy to reconstruct governance paradigms, the Jiufeng Project repositions waste incineration as an organic integration of technological and social engineering, establishing a NIMBY governance mechanism through dual dimensions of technological innovation and social reform. Through tripartite synergies of technological advancement, economic compensation, and social collaboration, the project expands its value space to achieve symbiotic enhancement of environmental and social benefits. A dynamic coordination mechanism is developed via stakeholder analysis to formulate rational risk-sharing and benefit-distribution schemes, thereby promoting sustainable interest balance. Additionally, by standardizing the behaviors of government, enterprises, and the public, the project establishes an iterative socio-ethical governance framework. The governance experience of the Jiufeng Project in transitioning from a "NIMBY effect" to a "Neighborhood-Benefit Project" provides a practical model for resolving similar governance dilemmas in comparable infrastructure projects.

With the implementation of the "Artificial Intelligence+" strategy, ChatGPT and DeepSeek intelligent agents will accelerate their commercialization and provide strong impetus for the development of new quality productivity. Intelligent agents have achieved technological transitions from single task processing to multimodal perception, from rule driven to autonomous learning, and from static response to dynamic programming, which has also led to risks of perception loss, decision-making disorder, and execution disorder. The alignment of values is related to whether intelligent agents can truly serve humanity, and has certain legitimacy, necessity, and feasibility. However, intelligent agents face the practical dilemma of "how to align" and "which values to align with", which is due to the lack of a sound value alignment system. In view of this, it is urgent to adopt a collaborative paradigm of technical regulation, ethical adaptation, and legal governance, that is, to construct a dynamic value recognition system, a systematic value adjustment mechanism, and a flexible value regulation program; Adhering to the ethical mission of putting people first, formulating scientifically reasonable ethical principles, and designing scenario based ethical rules; Determine the standard of value alignment, clarify the nature of value alignment, improve the evaluation method of value alignment, and accelerate the formation of new quality productivity to promote Chinese path to modernization.

Interdisciplinary approaches are widely advocated in global science policy as a key strategy for addressing complex social issues. However, evidence linking interdisciplinary research to its impact on the public remains limited. It is generally believed that interdisciplinary research produces solutions to critical problems by recombining knowledge from multiple fields. Yet the public often struggles to appreciate this recombination, since most individuals are not as specialized as patent examiners. To address this gap, our study introduces a more accessible attribute of interdisciplinary scientific knowledge—that of being a public good—alongside its re-combinatory nature, to explain how interdisciplinary citizen science fosters public engagement. Drawing on classic signaling theory from product management research, as well as social cognition theory and science communication theories, we focus on the signaling system formed by citizen science project teams, the public, and society as a whole. Based on this framework, we develop a theoretical model to examine how interdisciplinarity influences the public’s willingness to participate in citizen science projects. According to social role theory, we also consider how a research team’s identity might moderate the effect of interdisciplinarity on engagement willingness. Our study analyzes 880 citizen science projects hosted on Experiment (experiment.com) and engagement data from 37,867 contributors. We employ text‐mining methods—including Latent Dirichlet Allocation topic modeling and Word2Vec—to quantify three dimensions of interdisciplinarity in project titles and descriptions: diversity, balance, and disparity. Validation through manual checks and algorithmic tests confirms the robustness of our quantification approach, demonstrating that text mining effectively identifies disciplinary topics. Empirical results show that interdisciplinarity in citizen science projects promotes public engagement through two mechanisms. First, there is a direct positive effect: projects spanning diverse disciplines attract both broader and deeper public engagement. Second, there is an indirect positive effect: interdisciplinarity reduces a project’s novelty, thereby mitigating novelty’s negative impact on public engagement willingness; this indirect effect, however, pertains only to public engagement breadth. We find that when the research team is affiliated with academia, the direct positive effect of interdisciplinarity on public engagement breadth is strengthened, although no significant moderation appears for public engagement depth. Furthermore, when disciplinary diversity or balance is high, teams composed of both academic and amateur members negatively moderate interdisciplinarity’s impact on public engagement breadth. Under conditions of high disciplinary balance, such mixed teams also negatively moderate the effect on public engagement depth. In contrast, amateur teams’ identity shows no significant moderating effects. Using the Web of Science classification system and project tags, we classify citizen science projects as either natural science-oriented or humanities and social science-oriented. In natural science-oriented projects, interdisciplinarity enhances both public engagement breadth and depth, and the moderating effects of team identity align with our main findings. However, in humanities and social science-oriented projects, interdisciplinarity negatively affects public engagement breadth, though academic team can offset or even reverse this negative effect. This study enriches understanding of how interdisciplinary research influences public engagement. Practically, it offers guidance for government agencies and scientists in managing interdisciplinary citizen science activities.

With breakthrough advances and accelerated industrial expansion in brain-computer interface (BCI) technology, its ethical implications have increasingly garnered academic attention. Based on scientometrics and combined with interdisciplinary and risk-evolution analytical perspectives, this study examines the landscape, knowledge base, and research hotspots in international research on the ethics of brain-computer interface: (1) Publications in research on the ethics of brain-computer interface demonstrate a three-stage evolutionary trajectory, with fluctuations closely linked to technological cycles, social events, and policy shifts, culminating in 2024 as a window of explosive growth. (2) Bioethics, cognitive neuroscience, and machine ethics form a modular interdisciplinary citation network spanning normative values, technical pathways, and human-machine collaboration frameworks, thereby supporting a multidimensional theoretical foundation. (3) Research hotspots have formed clusters in the domains of subjectivity, technology, and society, highlighting the dynamic characteristics of risk evolution. (4) With the deepening of brain-computer integration, issues concerning the neural rights of integrated subjects and social acceptance urgently require attention. Based on the assessment of the current situation, future research should focus on fostering interdisciplinary empirical collaboration, driving industrial optimization and upgrading, and promoting decentralized network governance, so as to contribute to the improvement and development of global research on the ethics of brain-computer interface.

The Third Plenary Session of the Twentieth Central Committee of the Communist Party of China proposed “improving the institutional mechanisms for developing new quality productive forces in accordance with local conditions,” providing a strategic guideline for advancing the transformation and upgrading of regional productivity. As a new form of productive force characterized by innovation-driven dynamics and efficiency-oriented goals, the development of new quality productive forces is crucial for enhancing regional vitality and restructuring economic systems toward high-quality development. However, the implementation of this national strategy at the local level has revealed significant challenges, particularly the trend of policy convergence and homogeneous industrial planning across regions, which risks undermining local comparative advantages and aggravating inefficient competition.

Drawing on the theory of government attention, which posits that the allocation of attention determines the prioritization of policy resources and institutional efforts, this study investigates how local governments interpret and implement the central directive on new quality productive forces. Specifically, the study collects and analyzes political affairs texts related to new quality productive forces, published on the official websites of thirty provincial-level governments between 2023 and 2024. Using topic modeling based on the BERTopic algorithm, generative artificial intelligence techniques, and machine learning methods such as the random forest algorithm, the study systematically identifies the focus areas of government attention and examines their regional variations.

The findings reveal three major patterns. First, local governments demonstrate a structure of “five-dimensional focus and coexistence of dual tendencies” in their attention, covering five domains: new means of labor, new types of laborers, industrial sectors, application scenarios, and enabling environments. This indicates both differentiated governance strategies and converging policy orientations across regions. Second, policy imitation behavior is prevalent and primarily manifests as “late-developing region catch-up” and “peer-level learning.” This results in a spatial paradox in which industrial policy similarity increases with geographical distance, reflecting a widespread tendency to emulate economically advanced regions regardless of local resource endowments or development conditions. Third, regional heterogeneity in governance strategies is evident. Eastern provinces, which possess strong innovation capabilities and relatively high fiscal autonomy, tend to demonstrate desirable convergence characterized by targeted adaptation. In contrast, central provinces, constrained by weak industrial foundations and fragmented development trajectories, are more prone to undesirable convergence and may fall into a governance dilemma of “resource involution.”

To address the risks of excessive policy convergence and foster truly localized development of new quality productive forces, this study proposes three governance recommendations. First, the central government should strengthen top-level design, promote layered and classified policy guidance, improve regional coordination mechanisms, and align national strategies with the specific conditions and needs of localities. Second, local governments should enhance regional cooperation and policy synergy, avoid blind imitation, and build cross-regional industrial cooperation platforms to promote rational division of labor and resource allocation. Third, governments at all levels should coordinate differentiated innovation strategies in key industries with the collaborative development of related sectors, clarify industrial roles based on regional development stages, and prevent inefficient duplication. These recommendations aim to support a more adaptive, efficient, and coordinated institutional framework for the development of new quality productive forces in China.

Against the backdrop of comprehensively advancing the “AI+” initiative and accelerating the achievement of high-level scientific and technological self-reliance, AI for science is becoming a key force in leading the transformation of research paradigms, fostering new productive forces, and reshaping the global landscape of scientific and technological competition. In the second half of 2025, major economies such as the United States, the United Kingdom, the European Union, and Japan intensively released numerous policy documents on AI for science. Although each country chose different development paths due to differences in resources and institutions, they universally elevated AI for science to a national strategic project, exhibiting common trends such as the automation of research processes, the assetization of scientific data, and the construction of sovereign research infrastructure. The United States emphasizes consolidating its technological dominance through a national mobilization system; the United Kingdom focuses on agile breakthrough strategies in areas of strength; the European Union maintains technological sovereignty through pan-European integration; and Japan builds competitive advantages through theory-driven approaches and standards leadership. International experience offers important insights for promoting AI for Science in my country during the “15th Five-Year Plan” period. AI for Science should be approached as a systemic public project, integrating computing power, energy, and research network infrastructure. China's efforts should focus on developing domestically controlled automated experimental platforms and interface standards, improving the institutional framework for the assetization of scientific data, and providing sustained momentum for the development of new productive forces through organized research and the cultivation of interdisciplinary talent.

With the rise of artificial intelligence, AI driven scientific discoveries have become a new paradigm in scientific research. However, whether the research path that relies on data and algorithms can truly constitute “scientific discoveries” still faces fundamental philosophical and methodological questions. Scientific epistemology requires that knowledge claims must undergo falsifiability testing. However, current AI scientific discoveries often appear as “black boxes” due to the lack of interpretability, with outputs mostly statistical predictions rather than logically complete rational claims, resulting in the failure of falsifiability mechanisms: there are no clear theoretical targets to criticize, and scientific debates have become superficial debates on input-output. This reveals a deeper crisis: the traditional scientific cognition based on human intuitive reasoning is difficult to adapt to the complexity patterns revealed by AI. Therefore, resolving this crisis cannot only pursue the transparency of AI, but also requires promoting the expansion of the cognitive boundaries of the scientific community - developing new conceptual systems and formal languages that represent complexity, so as to understand the complex world revealed by AI while maintaining the fundamental mechanisms of scientific theory criticism and evolution.

The artificial intelligence industry is a typical representative of future industries and a strategic emerging industry that leads economic innovation and enhances national competitiveness. As a disruptive technology, generative artificial intelligence has become an important direction for the development of the artificial intelligence industry. Taking 23 provinces and cities in China as the research objects, this paper adopts the TOE theoretical framework and introduces the fsQCA method to explore the high-efficiency development path of China’s generative artificial intelligence industry from a configuration perspective. The research found that: (1) The generative artificial intelligence industry has four typical characteristics: innovation leadership, ecological synergy, radiation empowerment and market explosiveness. (2) There are five paths for the high-efficiency development of China’s generative artificial intelligence industry: organization-environment dual-wheel drive, technology-environment dominant, technology-environment synergy, factor aggregation comprehensive drive, and environment support. (3) Through configuration comparison, it is found that the high-efficiency development of China’s generative artificial intelligence industry requires the coordination of multi-dimensional factors, and the advantage of a single factor is difficult to support its sustainable development. The research conclusions provide policy basis and theoretical guidance for the government to formulate industrial development policies and enterprises to make the next strategic planning.

In recent years, the rapid development of artificial intelligence (AI) technology has made intellectual property rights (IPR) issues the focus of academic research and judicial decisions. This paper reviews the relevant literature and judicial precedents at home and abroad. Facing the future development of AI technologies, from the perspective of innovation stimulation and rights balancing, the paper researches the basic IPR protection issues of AI-generated works, signs and inventions such as the subject qualification, object scope, acquisition standard, rights attribution, implementation and application, and effective regulation. The paper constructs an AI IPR protection institutions system including Subject Qualification, ObjectScope, Acquisition Standard, Rights Attribution, Implementation and Application, and Effective Regulation and puts forward future development countermeasures for the IPR institutions system of AI-generated works, signs, and inventions. It is needed to revise and improve the IPR laws and policies regarding AI timely, stipulate the AI-generated works, signs and inventions generated can obtain IPR protection clearly, determine the IPR ownership of the AI-generated works, signs and inventions following the principle of contract, grant the AI the qualification as generator of works for copyright, the designer of signs for trademark right and the inventor of inventions for patent right, improve the IPR acquisition standards of AI-generated works, signs and inventions, and enhance the application of the IPR principle of good faith and creditworthiness, principle of balance of interests, and principle of anti-abuse regulations in the field of AI.