Over the past few decades, the probability of climate anomalies has increased significantly, and large-scale extreme warming events have occurred globally. Climate change has gradually become a severe challenge for humanity, drawing widespread attention from society. Although climate change has increasingly become a consensus within the scientific community in recent decades, skepticism about its authenticity still exists in some parts of society. Many scholars attribute climate skepticism to a lack of scientific knowledge. Others argue that cultural factors, such as values and ideologies, play a more critical role. Addressing climate change is an urgent task that requires collective action from all members of society. This study aims to investigate whether citizens’ scientific literacy significantly influences their perception of climate change. The Science Comprehension Theory posits that scientific literacy facilitates the development of a scientific understanding of climate change, with scientific knowledge playing a key role. On the other hand, the Cultural Cognition Theory emphasizes that sociocultural factors, such as values and ideologies, are crucial in shaping individuals’ per-ceptions of climate change. It argues that citizens’ indifference to climate change and their non-scientific perceptions are not necessarily caused by a lack of scientific knowledge. This study draws on data from the 2023 Science, Technology and Society Barometer Survey, a nationally representative survey in China, to analyze Chinese citizens’ attitudes and perceptions. After processing the data and removing missing samples, the study includes 2986 valid samples. An ordered logistic regression model is employed to conduct an empirical analysis of the relationship between scientific literacy and individuals’ perceptions of climate change and to explore the mechanisms underlying this relationship. The findings demonstrate that scientific literacy has a significant positive impact on individuals’ perceptions of climate change. Mechanism analysis reveals that improvements in scientific literacy positively influence perceptions of climate change by enhancing scientific interest, trust in science, and trust in scientists. Heterogeneity analysis indicates that the impact of scientific literacy varies across individual characteristics, such as gender, educational attainment, and age. Specifically, the positive effect of scientific literacy on climate change perception is stronger for men than for women, for individuals with a bachelor’s degree or higher compared to those with lower education levels, and for younger individuals compared to older ones. At the regional level, scientific literacy positively influences climate change perceptions in eastern, central, and western regions of China. The magnitude of this effect is highest in the eastern region, followed by the western and central regions. However, the positive influence of scientific literacy in the northeastern region is not statistically significant. These findings suggest that the impact of scientific literacy on citizens’ perceptions of climate change is complex, requiring attention to individual heterogeneity and regional differences. The results of the empirical analysis validate and enrich the explanatory power of the Science Comprehension Theory, emphasizing the critical role of scientific literacy in shaping individuals’ perceptions of climate change. To address climate change, It is necessary to strengthen climate change education, promote the concept of low-carbon lifestyles, and build professional scientific communities to advance science popularization, improve citizens' scientific literacy, and enhance their trust in science. Furthermore, it is essential to translate awareness into practice, guiding behavioral changes to tackle climate change as a global issue. This study underscores the importance of scientific knowledge and literacy in forming individuals’ perceptions of climate change. By utilizing nationally representative data from Chinese citizens, it validates and enriches the Science Comprehension Theory while addressing gaps in related research in China. Although some studies downplay the explanatory power of scientific knowledge and literacy, emphasizing the role of values, ideologies, and religious beliefs in shaping perceptions of climate change, this study demonstrates that, even when sociocultural factors play a role, scientific literacy remains a robust explanatory factor. Enhancing citizens’ scientific literacy is an essential approach to addressing climate change.

The success of open-source large models such as DeepSeek and Llama demonstrates that technological innovation and industrial development in AI will inevitably rely on an open-source innovation ecosystem. Open-source licenses are a key element of governance in that ecosystem; in essence, they are contracts for the transfer of rights shaped by the rightsholder's genuine manifestation of intent, and their terms are legally binding on the public with respect to the use, reproduction, modification, and distribution of "open-source resources." Because open-source large models are widely used across the AI ecosystem, enterprises' insufficient understanding and improper performance may not only trigger intellectual-property (IP) infringement risks but also trap them in compliance dilemmas where investment and return fall out of balance; there is an urgent need for compliance guidance. At present, however, there is still a lack-both domestically and internationally-of systematic research on compliance strategies for open-source licenses at the user side of large models.This paper surveys representative open-source licenses in the large-model field worldwide, identifies IP risks that may arise from license breaches through the lens of the AI value chain, and analyzes countermeasures using an integrated "law-technology-information science" paradigm. In the data pre-training stage, open-source large models typically use massive datasets from open databases and the publicly accessible internet to perform pre-training, thereafter serving as the foundational base models for downstream "service-facing" large models. Because the relationship between model parameters and training data is a nonlinear mapping that sharply reduces the feasibility of traditional cleansing techniques, and because the aggregation of massive data produces abnormally high costs for identifying rightsholders-combined with many jurisdictions' tendency to afford exemptions to open-source model developers and the absolute disclaimers commonly found in open-source licenses-the responsibility for managing IP infringement risks in the pre-training stage has shifted from the development side to the user side. Inadequate cleansing of rights-information in training data is the principal cause of IP infringement risk at the pre-training stage.During the development and use stage, differences exist both among different categories of open-source licenses and among clauses within the same category-for example, divergent obligations among permissive licenses, divergent prohibitions among weak-copyleft (restrictively permissive) licenses, and ambiguous trigger boundaries for strong-copyleft ("infectious") licenses in the large-model context. An unclear understanding of open-source licenses is the primary cause of IP infringement risk in this stage.At the output stage, open-source large models typically must rely on code subject to open-source licenses for training, and the model's propensity to memorize training data can lead to the dissemination of content that is substantially similar to the training code. A lack of control over derivative content is a principal cause of IP infringement risk at this stage.In the data pre-training stage, because AI training data are massive in scale, rightsholders are dispersed, entitlement structures are complex, and the information is often unstructured, traditional community-based review mechanisms struggle to achieve effective provenance tracing; users of open-source large models must therefore build an autonomous rights-governance framework. Enterprises should establish a full-lifecycle rights-information cleansing mechanism that integrates "technical filtering + explicit allocation of rights and responsibilities + data compliance." In the development and use stage, users of open-source models should develop a deep and accurate understanding of the various open-source licenses and, on that basis, use auxiliary license-selection tools-such as ChooseALicense and License Differentiator-to make choices aligned with their commercial preferences. They should also focus on the typological features and risk profiles of permissive licenses, weak-copyleft (restrictively permissive) licenses, and strong-copyleft ("infectious") licenses, and establish differentiated compliance management systems for each. In the output-management stage, in light of the particular risk-response needs associated with outputs from open-source large models, enterprises should construct a risk-management framework along four dimensions: data preprocessing, model training, generation control, and user oversight.

With the deepening implementation of the innovation-driven development strategy, new types of research and development (R&D) institutions, serving as pivotal platforms for the full integration of Industry-University-Research (IUR), play a central role in facilitating the efficient flow of innovation resources, accelerating the commercialization of scientific and technological achievements, and driving industrial transformation and upgrading. Their internal governance directly influences the rational allocation of research resources, the innovative vitality of researchers, and the market-oriented application of research outputs, thereby profoundly influencing the overall effectiveness, resilience, and competitiveness of the national innovation system. However, these institutions are confronted with complex governance challenges, including multi-stakeholder participation, ambiguous organizational boundaries, and dynamically evolving objectives that linear and static governance theories cannot adequately address. While prior studies have examined macro-level issues, such as organizational model innovation and policy support systems, as well as micro-level concerns, including governance structure optimization and incentive design, they have not adequately captured the dynamic interactions among internal governance elements or the underlying mechanisms by which organizations adapt to environmental changes.

To tackle these challenges, this study proposes an analytical framework based on the theory of Complex Adaptive Systems (CAS) to investigate the internal governance logic of new types of R&D institutions. We selected six representative domestic and international cases and applied grounded theory methodology to systematically examine their governance practices and optimization pathways. The findings reveal that the internal governance of new types of R&D institutions aligns with a dynamic framework under CAS, characterized by "system identification → stakeholder interactions → resource flows → adaptation mechanisms." This framework elucidates how these institutions attain effective governance and sustainable development in complex environments.

In terms of system identification, these institutions implement a multi-tiered framework encompassing institutions, departments, and individual actors, which underpins effective collaboration and enables the strategic allocation of resources. Regarding stakeholder interactions, the internal governance of these institutions operates as a complex adaptive system involving multiple stakeholders, whose effective functioning relies on dynamic interactions both among internal actors and between the organization and its external environment, exhibiting characteristics of complexity and nonlinearity. With respect to resource flows, the sustainability of internal governance depends on the circulation of diverse elements—financial, informational, material, knowledge-based, and human resources—among governance actors and between the institution and its environment. This process ultimately establishes a self-reinforcing cycle in which resource flow drives innovation, which in turn promotes further flow. Finally, concerning adaptive mechanisms, these new types of R&D institutions demonstrate a dynamic capacity to respond to changes in internal and external conditions, which can be conceptualized as a closed-loop process of perception, decision-making, implementation, and feedback.

Based on these findings, the study offers the following policy recommendations:(1) Strengthen the construction of system identification to clarify the collaborative orientation among multiple stakeholders; (2) Optimize resource allocation and flow mechanisms to streamline multidimensional interaction channels; (3) Enhance mechanisms for resource circulation to ensure efficient allocation of key elements, (4) Establish a closed-loop adaptive mechanism to improve dynamic responsiveness. These recommendations provide comprehensive and actionable guidance for enhancing the internal governance, innovation efficiency, and long-term strategic development of new types of R&D institutions.

More and more technology-based enterprises are frequently adopting open science practices. According to traditional economic theory, open science has always been the behavioral norm and value principle of universities and research institutions, while the open science behavior of enterprises may reduce the benefits they obtain from innovation monopoly. Faced with the active phenomenon of open science in enterprises and the insufficient explanatory power of traditional theories, it is particularly necessary and urgent to study the logic and rationality of open science behavior in enterprises. On the basis of literature review, this article adopts a combination of bibliometric and manual coding methods. Through the analysis of relevant literature in the Web of Science database, it is found that: (1) research on open science in enterprises is shifting from phenomenon focus to mechanism cognition; (2) The open science behavior of enterprises can be interpreted from three behavioral logics, namely scientific research driven logic, market competition driven logic, and collective innovation driven logic. (3) From the perspective of the internal mechanism of whether enterprises can gain competitive advantages in open science behavior, analyze and propose the rationality of the triple logic of open science behavior for enterprises. The research conclusion provides a more systematic perspective for a deeper understanding of the increasing number of enterprises engaging in open science behavior; To promote open innovation, policy references have been provided from a theoretical perspective.

In the context of a new wave of technological revolution and industrial transformation, China has intensified its efforts in basic research and original innovation, with strategic emerging industries and future industry sectors experiencing rapid development. Innovation consortia—collaborative organizations led by enterprises and involving universities, research institutes, and other stakeholders—have emerged as critical vehicles for integrating the innovation and industrial chains and achieving breakthroughs in key core technologies. Despite significant progress in establishing such consortia—over 1,000 have been launched nationwide since 2019, including 24 at the national level coordinated by the State-owned Assets Supervision and Administration Commission—challenges remain. Many local policies lack detailed implementation guidance, and issues such as unclear mechanisms for risk sharing, benefit distribution, and intellectual property rights have led to phenomena of “alliance without integration” or “cooperation without sharing.” As a result, the actual effectiveness and development level of many innovation consortia remain suboptimal. To address this gap, this study systematically investigates the factors that influence the construction and performance of innovation consortia in China.

Building on the strategic triangle framework—which integrates resource, institutional, and industrial dimensions—this research adopts a mixed-methods approach combining grounded theory and machine learning to identify and prioritize key influencing factors. First, through open coding of policy documents, academic literature, and case studies, 36 initial factors were extracted and categorized into three dimensions: resource (e.g., scientific and technological human resources, innovation resources, market-oriented resources), institution (e.g., internal governance mechanisms, outcome sharing and protection systems, government support policies), and industry (e.g., industrial cluster advantages, industrial chain structure, regional upgrading demands). A theoretical model was then constructed to conceptualize how these factors collectively shape the development level of innovation consortia.

To validate and rank the importance of these factors, empirical data from 1,734 publicly announced innovation consortia were collected and analyzed. Among them, 356 consortia that include listed enterprises were selected as the analytical sample due to the availability of more reliable and quantifiable performance indicators. A random forest algorithm—a robust machine learning technique suitable for handling complex, non-linear relationships among variables—was applied to assess the relative importance of each factor. The results were further refined by incorporating subjective evaluations from domain experts to ensure contextual accuracy and practical relevance.

The findings reveal that scientific and technological human resources and industrial cluster advantages are the two most influential factors in enhancing the development level of innovation consortia. These are followed by market-oriented resources and innovation resources, which also play a substantial role. At the institutional level, the system for sharing and protecting innovation outcomes, internal governance mechanisms within the consortium, and government incentive and support policies are moderately important, indicating that well-designed institutional arrangements are essential for sustaining collaboration. In contrast, factors such as regional industrial upgrading needs and basic characteristics of the industrial chain exhibit relatively lower importance, suggesting that while macro-industrial contexts matter, they are less directly impactful than resource endowments and institutional design.

This study contributes to both theory and practice. Theoretically, it extends the application of the strategic triangle framework to the context of innovation consortia and bridges qualitative insight with quantitative validation through an integrated methodology. Practically, it provides evidence-based policy recommendations, including strengthening the foundation of high-quality talent and R&D resources, leveraging existing industrial clusters to foster collaborative ecosystems, and refining institutional mechanisms to enhance trust and coordination among consortium members. By identifying the hierarchical structure of influencing factors, this research offers a systematic reference for policymakers and practitioners aiming to improve the quality and effectiveness of innovation consortia in China.

The rapid development of intelligent technologies is propelling humanity toward an intelligent society, bringing about profound changes in production, lifestyle, organizational structures, and social order. These transformations have given rise to numerous new challenges, making the governance of intelligent society an urgent research agenda. While existing studies have addressed key risks associated with intelligent technologies—including algorithmic abuse, the digital divide, and issues of trust and fatigue—relatively less attention has been paid to the transformative impact of intelligent recommendation systems on public information exposure, cognitive patterns, and behavioral preferences. The widespread use of personalized recommendations often results in individuals being repeatedly exposed to homogeneous content, leading to the formation of information cocoons, exacerbating cognitive polarization, and increasing the risk of societal fragmentation. This presents a significant challenge for intelligent social governance.

Traditional analyses of public cognition and attitude formation are often based on the rational actor model, assuming that individuals make decisions through cost-benefit calculations. However, such perspectives tend to overlook the psychological and emotional mechanisms involved in information processing. Recent studies from the fields of psychology and behavioral science have begun to explore how emotional preferences, identity, ideology, and personality traits influence the relationship between information exposure and cognitive polarization. Nevertheless, little attention has been given to the role of perceived external social environments, especially perceptions of societal threats. Psychological research indicates that perceived threats in the social environment significantly shape individual cognition and attitudes. In today’s digital landscape, where intelligent technologies dominate, threat-inducing and emotionally charged content is more likely to attract attention and be widely disseminated. This can amplify public anxiety and perceptions of insecurity, thereby intensifying cognitive polarization. Accordingly, this study seeks to bridge the gap by examining how threat perceptions and information exposure interact to influence public cognitive polarization.

This paper introduces the perspective of external threat situations and explores how they, together with information exposure, jointly influence public cognitive polarization. Using the application of facial recognition technology in “pedestrian red-light violation exposure” as a case study, this research constructs a 2 (no threat situation vs. threat situation)×2 (homogeneous information vs. heterogeneous information) between-subjects experiment. A total of 1,281 participants were recruited online for the experiment. The results indicate that exposure to homogeneous information strengthens the public’s original views, thereby intensifying cognitive polarization, while exposure to heterogeneous information weakens the public’s original views, reducing cognitive polarization. Compared to the absence of external threat situations, threat situations significantly weaken the depolarization effect brought about by exposure to heterogeneous information. Additionally, for individuals of different genders and average annual incomes, threat situations play varying roles in the process by which information exposure influences polarization.

The findings demonstrate that the external environment in which the public is situated, including threat perceptions caused by social unrest, economic crises, crime, disease risks, and unexpected events, may have a significant impact on changes in cognitive polarization during information exposure. This deserves further attention and research. Moreover, when the government addresses viewpoint polarization in cyberspace, it should not only promote the public’s exposure to more heterogeneous information that challenges their original views and encourages diverse thinking, but also pay attention to the external environment the public faces. This can help reduce public anxiety and threat perception, thereby enhancing the role of heterogeneous information in mitigating viewpoint polarization.

Richard R. Nelson is founder of modern evolutionary economics and a leading figure in innovation economics. Starting from a critical re-evaluation of neoclassical economics, Nelson pioneered the development of evolutionary theories of innovation, driving a significant paradigm shift in innovation studies. His research spans areas such as corporate innovation, industrial innovation, national innovation systems, and long-term economic growth. Notably, he profoundly elucidated the crucial role of the co-evolution of technology and institutions in long-term economic development from an evolutionary perspective, establishing a vital cornerstone for contemporary theories of innovation and development and policy-making research. To commemorate Nelson’s outstanding contributions to innovation studies, this paper systematically reviews his thoughts on innovation development and, based on this, discusses the significant insights they offer for research on innovation and development theory and policy-making in China.

Information consumption, which accelerates the integration of consumption chains with supply chains, industrial chains, and value chains through digital technology, has become a defining feature of digital economic development and a key engine driving high-quality economic growth. To promote information consumption, the State Council issued the “Guiding Opinions on Further Expanding and Upgrading Information Consumption to Continuously Unleash the Potential of Domestic Demand” in 2017. This document emphasized deepening the integration of information consumption with innovation, entrepreneurship, and the “Internet Plus” initiative, while encouraging core technology R&D and service model innovation. The 2021 “Outline of the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035” highlighted the need to cultivate new forms of consumption, including information consumption, digital consumption, and green consumption. The 2024 State Council Opinions on Promoting High-Quality Development of Service Consumption called for accelerating the construction and upgrading of information consumption experience centers and launching a series of new information consumption projects. Consequently, advancing information consumption has become a key component of the national innovation strategy. The Ministry of Industry and Information Technology approved 104 national information consumption pilot cities in 2014 and 2015, respectively, deploying pilot initiatives in infrastructure, consumption models, and core technologies to support information consumption development. How, then, do information consumption-driven policies at all government levels simultaneously advance information consumption development and digital technology accumulation while elevating urban digital technology innovation levels? What are their underlying mechanisms? Addressing these questions not only provides a theoretical framework and empirical evidence for evaluating the policy implications of information consumption pilot city development but also offers valuable insights into leveraging demand-driven innovation to enhance urban digital technology innovation capabilities.

Drawing upon demand-driven innovation theory, this paper constructs an analytical framework encompassing foundational elements, core entities, and ecosystem effects. It elucidates the potential mechanisms through which information consumption-driven policies empower urban digital technological innovation. Empirical validation employs a multi-period difference in differences model, utilising multiple datasets including urban datasets, patent databases, national tax survey databases, and listed company databases, alongside information consumption pilot policies. Findings reveal that information consumption-driven policies significantly elevate urban digital technology innovation levels. Mechanism analysis indicates these policies drive innovation through multiple pathways: attracting talent clusters, fostering enterprises as innovation agents, and strengthening governmental external ecosystem safeguards. Heterogeneity analysis demonstrates that such policies primarily enhance digital innovation in regions with high marketisation levels, cities below tier-two status, and areas possessing robust digital industrial foundations. Synergy analysis indicates that digital finance, digital economy policies, and information infrastructure support can generate significant synergistic effects with information consumption-driven policies through financial empowerment, policy guidance, and facility support. The findings of this study provide novel and viable pathways for technological innovation in the digital era, offering important insights for unleashing urban digital technology innovation vitality under information consumption-driven frameworks. Based on the above conclusions, this paper proposes the following policy implications: First, refine top-level institutional design and resource support systems to establish standardized policy closed-loop mechanisms. Second, build collaborative innovation mechanisms among talent, enterprises, and government to stimulate endogenous motivation among micro-level entities. Third, implement tailored, tiered advancement strategies based on the distinct resource endowments of different cities.

Since the introduction of the Kyoto Protocol and its continuation through the Paris Agreement, environmental governance has remained a central theme in global policy discourse. In China, environmental pollution has emerged as a significant constraint to high-quality economic development. In response, green innovation has become an essential driver of the country's green transformation. As the primary agents of innovation, enterprises play a pivotal role in advancing green practices. Enhancing firms' green innovation capacity has thus become a critical challenge in the current stage of sustainable transition. While the Chinese government has adopted a wide range of environmental policy tools to stimulate corporate green innovation, the actual effectiveness of these policies largely hinges on the presence of a sound legal and enforcement framework. In this context, the development of an effective environmental judiciary system is not only a key component of the broader green policy framework but also holds significant practical relevance for achieving a sustainable economic transformation. This study exploits a quasi-natural experiment involving the staggered establishment of environmental courts across prefecture-level cities in China beginning in 2007. Using a multi-period difference-in-differences (DID) methodology, we empirically investigate how the presence of environmental courts influences local firms’ green innovation practice and further explore the underlying mechanisms.

Our findings reveal several important insights. First, the establishment of environmental courts significantly enhances corporate green innovation, with a more substantial impact observed on substantive green innovation compared to symbolic initiatives. Second, rather than being driven solely by passive compliance due to regulatory vulnerability, firms exhibit proactive self-regulation in response to institutional signals. Specifically, the creation of environmental courts is interpreted by firms as a strong governmental commitment to environmental governance, prompting them to adjust internal strategies accordingly. This adjustment is aimed at improving legitimacy and securing long-term competitive advantage through substantive innovation. Third, this effect is more pronounced in state-owned enterprises and in firms whose management teams exhibit a long-term orientation. Fourth, the study identifies a spillover mechanism whereby executive interlocks—shared executives across different firms—facilitate the diffusion of green innovation practices, resulting in a broader systemic impact beyond individual firm responses.

This study underscores the vital role of environmental judicial institutions in promoting corporate green innovation, especially substantive green innovation. It adds to the literature on corporate social responsibility by highlighting firms’ proactive responses to institutional change, rather than reactive behavior to violations or external pressure. From a risk management and policy perspective, environmental courts help reduce uncertainty, enhance regulatory credibility, and incentivize long-term strategic investment in green innovation. Policy implications include the need to strengthen environmental judicial capacity through professionalization, improved case-handling efficiency, and the development of digital platforms such as online courts. Enhancing the signaling effect of institutional reform—through transparent litigation, public trials, and active media engagement—can further motivate firms’ strategic adjustments. Encouraging state-owned enterprises to serve as role models and fostering inter-firm knowledge diffusion through executive networks and board-level interactions can help scale up green innovation across industries. Corporate leadership should also embrace long-term environmental accountability and embed sustainability into core governance practices to support sustainable development.

Since the reform and opening up, Chinese economy has been developing rapidly, Gross Domestic Product (GDP) surpassed that of Japan in 2010 and has remained the second largest in the world for consecutive years ever since. However, a host of issues have emerged in the process of economic scale expansion, such as high total carbon emissions and relatively low energy utilization efficiency. According to the Report on Statistical Analysis of Green and Low-Carbon Patents (2024) released by the China National Intellectual Property Administration, the cumulative number of published green and low-carbon patent applications in China reached 573,000 from 2016 to 2023, with an average annual growth rate of 10.0%. Although the number of green and low-carbon patent applications published in China and their proportion of the global total showed an overall upward trend, China's green innovation is characterized by a phenomenon of "growing quantity alongside declining quality". At the same time, the World Intellectual Property Organization (WIPO) released the Global Innovation Index 2024, which shows that China ranks 11th worldwide in innovation capacity. Nevertheless, the Global Energy Review 2025 published by the International Energy Agency (IEA) shows that China remained the largest coal consumer globally. This implies that there may still be a significant gap between China's green innovation efficiency and that of developed countries. China's green innovation is now caught in a dilemma where quantity is growing while quality is declining and efficiency remains low. Therefore, how to break through the predicament of "low quality and low efficiency" in green innovation among Chinese enterprises has become a critical issue, which is essential to achieving the carbon peaking and carbon neutrality goals and boosting the overall performance of China's innovation system.

It has become an important issue in theory and reality to promote the deep integration of enterprise digitalization, intelligence and greening, and solve the dilemma of green innovation. Based on the listed companies in Shanghai and Shenzhen A-shares from 2014 to 2023, this study explores the impact and mechanism of digital intelligence on enterprise green innovation from the dual perspective of "improving quality and expanding quantity" as well as "enhancing efficiency". The results show that: (1) Digital intelligence can not only promote the quantity and quality, but also improve efficiency, and the conclusion still holds after testing for robustness. (2) The mechanism testing shows that digital intelligence breaks through the green innovation dilemma by optimizing the innovation resources allocation and reducing information asymmetry and other means. (3) Heterogeneity analysis shows that high environmental regulation areas, high-tech industries, state-owned and large enterprises, digital intelligence plays a more significant effect on the improvement of the green innovation. (4) The effect consequences study shows that the promotion of enterprise green innovation by digital intelligence can further improve the economic, environmental, and social responsibility performance of the enterprise. The conclusion of this study provides useful insights for leveraging digital intelligence to drive enterprise green innovation, achieving the dual-carbon goals and boosting the overall performance of China's innovation system, and shaping new driving forces and advantages for high-quality development of enterprises.