A field that urgently needs to be studied by the domestic academic community, namely, science-driven innovation was proposed. This type of innovation has many essential differences from the technology-driven innovation, experience-driven innovation, and demand-driven innovation that have been studied in China for many years. At present, the development environment, development needs, and development capabilities of our country have undergone tremendous changes. The relationship between China and leading developed countries in industrial technological innovation has shifted from "competitive cooperation, mutual support" to "parallel competition, system segmentation". Meanwhile, strong innovation, strong industry, and strong people's livelihood have become important issues in building a modern strong country. Under this background, our country must actively and effectively promote science-driven innovation activities. However, the academic community has lacked clear focus research on science-driven innovation in the past. This paper reviews and summarizes the research progress of science-driven innovation at home and abroad, and believes that focusing on the research of science-driven innovation can help improve the quality of China's innovation development and enrich the theory of industrial technological innovation in the Chinese context. Furthermore, it is proposed that to explore the relevant laws of science-driven innovation, to construct the theory of science-driven innovation, the academic community urgently needs to focus on studying six types of management issues: first, the relationship between science and industrial technological innovation; second, the evolution of the "underlying paradigm of science" in science-driven innovation; third, the process model of science-driven innovation, that is, the innovation chain; fourth, the organizational model of science-driven innovation; fifth, the three-level governance system of science-driven innovation; sixth, the industry-level phenomena that should be paid attention to in science-driven innovation.

In the context of the system of digital and real integration, artificial intelligence technology is an important driving force to promote the productivity utilization of real enterprises and achieve the goal of becoming A quasi-natural experiment was conducted based on the establishment of National New Generation Artificial Intelligence Innovation and Development Pilot Zones. Data from Chinese A-share listed companies from 2010 to 2024 were analyzed. The main findings are as follows. First, the establishment of the pilot zones significantly improved enterprise capacity utilization, with an estimated economic increase of approximately 1.04%. Effective empowerment of the real economy toward output enhancement and efficiency improvement was thereby achieved. Second, mechanism tests indicated that artificial intelligence application improved capacity utilization through three channels: operational efficiency enhancement, technology upgrading facilitation, and production-sales fluctuation reduction. Third, pathway tests showed that the pilot policy promoted the optimization of talent structures and increased patent outputs. Fourth, heterogeneity analyses based on enterprise characteristics revealed that the above effects were more pronounced among private enterprises, firms with higher corporate governance levels, and smaller-scale enterprises. Fifth, heterogeneity analyses based on external institutional environments showed that the policy's positive effect on capacity utilization was stronger under conditions of higher marketization, non-coastal regional locations, and operation in strategic emerging industries. Sixth, economic consequence tests indicated that after driving capacity utilization improvement, the pilot policy significantly enhanced corporate economic performance, increased social contributions, and expanded employment scale. An innovative extension was made to the academic literature on the economic consequences of artificial intelligence development. Policy references were also provided for the Party and the state regarding the empowerment, strengthening, and expansion of the real economy through artificial intelligence.

Promoting the "artificial intelligence plus" initiative is considered a crucial leverage for cultivating new quality productive forces, and evaluating its microeconomic consequences is of substantial practical significance for optimizing capital market resource allocation. Corporate bonds and enterprise bonds issued by Shanghai and Shenzhen A-share listed companies from 2010 to 2024 were used as the research sample. The impact of artificial intelligence (AI) on corporate bond credit spreads and its underlying micro-mechanisms were empirically examined. The findings indicate that AI adoption significantly reduces corporate bond credit spreads. Mechanism analysis reveals that mitigating information asymmetry and suppressing debt default risks are vital micro-channels through which AI exerts its credit governance effects. Heterogeneity analysis demonstrates that the mitigating effect of AI on credit spreads is more pronounced among firms with poorer information environments and higher risk exposure, reflecting a distinct "timely assistance" marginal governance effect. Specifically, the reduction in corporate bond credit spreads driven by AI is more significant for enterprises with low levels of information disclosure and smaller firm sizes. Furthermore, this reduction is more prominent for enterprises located in regions with low marketization levels and those with poor internal control systems. The cross-disciplinary research between asset pricing and technological transformation is extended. Empirical evidence and policy implications are provided for the comprehensive implementation of the "artificial intelligence plus" initiative and the promotion of high-quality development in the real economy.

With the rapid advancement and diffusion of new-generation artificial intelligence technologies, Tokens are moving beyond their technical role as internal computational and representational units within AI models. They are becoming a foundational basis for value measurement, value exchange, and value distribution in the intelligent era. A new Token economy paradigm is therefore emerging. Based on value theory, the reconfiguration of traditional value understanding under the Token economy paradigm was examined from the dimensions of value measurement, value exchange, and value distribution. The bidirectional transmission mechanisms of value reconfiguration were further analyzed within and beyond the AI industry. The results show that the Token economy paradigm can enhance quality and efficiency within the AI sector and can enable the transformation and upgrading of other industries. In the Chinese context, a value governance framework for the high-quality development of the Token economy is proposed. It consists of four layers: value-scale sovereignty, value-exchange transmission, value-distribution safeguards, and value-system security. The Token economy paradigm provides theoretical support for understanding value creation, value circulation, and value allocation in the intelligent era. It also provides practical implications for promoting the high-quality development of China's Token economy and strengthening its long-term competitive advantages.

At present, the integration and development of scientific and technological innovation and industrial innovation, along with their same-frequency resonance, are regarded as the two wings for the development of new productive forces. A comprehensive evaluation system was constructed based on the entropy method and the coupling coordination degree model to measure the level of integrated development of scientific and technological innovation and industrial innovation in 31 provinces of China (Due to the lack of data, the statistical data mentioned here do not include the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province.) from 2012 to 2023. Then, the Dagum Gini coefficient decomposition, Kernel density estimation, Moran's I index, and spatial convergence model were used to explore the regional differences, spatiotemporal evolution, and convergence characteristics of the integration development level of the two. It is found that the level of integration of scientific and technological innovation and industrial innovation in China is generally increasing year by year, showing spatial characteristics of "high in the east and low in the west". The regional gap in the integration development level showed a rising trend. The gap within the eastern region was the largest, and the gap between the eastern and western regions was also the largest. The overall difference mainly came from regional disparities. The overall integration level changed from relative dispersion of low values to high concentration of high values, the imbalance increased obviously, and multi-polarization characteristics appeared in the central, western, and northeastern regions. The positive spatial aggregation effect is significant, and absolute β convergence and conditional β convergence existed in the whole country and the four major regions. Consequently, the current situation of the integration of scientific and technological innovation and industrial innovation in China is clarified. Efficient allocation of innovative development resources, optimization of the economic structure, and enhancement of development coordination, and strong support for the construction of a new development pattern are thus provided.

In the digital economy era, the liability of newness is mainly manifested as the inability of datasensitive entrepreneurial activities to enter the market due to data scarcity. Based on the dual attributes of public data as both a data factor and a public service, the effect of open public data on regional datasensitive entrepreneurship was examined over the sample period from 2010 to 2019. A quasinatural experiment based on the launch of open public data platforms by local governments was employed to identify the causal impact. The empirical results indicate that open public data significantly promotes regional datasensitive entrepreneurship and alleviates the liability of newness. Mechanism analysis reveals that open public data releases regional market demand potential, provides more highquality production factors, and improves the business environment. These effects stimulate the market entry of datasensitive entrepreneurs, forming marketenabling, factorenabling, and institutionalenabling effects. Further analysis shows that the positive effect is amplified with better data quality and higher regional information accessibility. Open public data also enhances overall regional entrepreneurial activity. The facilitating effect is stronger in cities with lower degrees of market segmentation. These findings may offer implications for overcoming the liability of newness in the digital economy and for creating a digital environment that supports entrepreneurial activities.

Against the backdrop of the deep integration of the digital economy and the real economy, an industrial network incorporating the digital economy sector was constructed based on China's input-output tables from 2005 to 2023. The efficiency-redundancy framework from ecological network analysis was introduced to measure industrial network resilience, and the resilience indicators were embedded into a temporal exponential random graph model to systematically investigate the evolutionary patterns and formation mechanisms of China's digital economy input-output network resilience. The findings reveal the following. Firstly, China's industrial network has remained in a state of excessive redundancy, with resilience exhibiting a fluctuating upward trend. The concentration of inter-node flows serves as the core driver of resilience fluctuations, and the dominant factor influencing resilience shifted from efficiency change to redundancy change around 2020. Secondly, network evolution exhibits significant path dependence and structural rigidity. Transitive closure facilitates the transformation of indirect linkages, and the contribution of redundant inflows is significantly positive, indicating that industries with diversified upstream supply are more likely to establish connections, whereas efficiency-related variables are not significant. Thirdly, the digital economy sector assumes a dual role. In terms of resilience, its influence has evolved from being dominated by digital industrialization (2005—2012) to being led by industrial digitization (2012—2020). In terms of evolutionary mechanisms, the two sub-sectors exhibit asymmetric effects on link formation, primarily manifested in the optimization and substitution of existing linkages. Thus, a novel analytical framework and empirical evidence are provided for understanding the resilience characteristics and evolutionary logic of industrial networks in the digital economy era. Moreover, theoretical insights and decision-making references are offered for balancing efficiency and security and promoting the deep integration of the digital economy and the real economy within the new development paradigm.

The competitive patterns among firms in the digital age have undergone significant changes. How digital firms can compete with leading firms and achieve incremental catch-up becomes an important issue that needs to be addressed. From the perspective of substitutive innovation, the impact of digital search on the incremental catch-up performance of digital firms was analyzed, and the mediating role of substitutive innovation behavior and the moderating role of inter-organizational dependence were investigated. Based on a sample of digital enterprises that provide digital technologies, products and services, the empirical test was conducted using multiple regression analysis. The results show that digital search is a supportive condition for digital firms to achieve incremental catch-up performance, and both prospective and reactive search dimensions positively influence the incremental catch-up performance of these firms. Leapfrogging substitution behavior and coherent substitution behavior mediate the relationship between digital search and incremental catch-up performance. Sensitivity dependence has no significant moderating effect between substitutive innovation behavior and incremental catch-up performance. Vulnerability dependence negatively moderates the effect of leapfrogging substitution behavior on incremental catch-up performance, while the moderating effect on coherent substitution behavior and incremental catch-up performance is not significant. The conclusions reveal the process mechanism of incremental catch-up of latecomer digital firms, which provides theoretical and practical implications for world-class firms seeking to achieve breakthroughs.

Against the backdrop of the profound reconfiguration of global value chains and the binding constraints of the "dual carbon" goals, examining how the opening of digital services trade can empower the greening of supply chains is of considerable theoretical and practical significance. As a key frontier of China's new round of high-standard opening-up, digital services trade liberalization warrants systematic empirical scrutiny regarding its capacity to facilitate green supply chain transformation at the firm level. Drawing on data from Chinese listed companies from 2014 to 2023, the OECD digital services trade restrictiveness index with input-output tables were integrated to construct a multidimensional index of digital services trade openness that captures both inter-industry linkages and the depth of liberalization. It further measures the level of supply chain greening across three dimensions—environmental, operational, and economic performance—and empirically tests the relationship between the two. The results show that digital services trade openness significantly enhances firms' supply chain greening, a conclusion that remains robust after addressing endogeneity concerns and conducting a battery of robustness checks. Mechanism analysis indicates that digital services trade openness reshapes the green ecosystem mainly through three channels: strengthening digital governance, optimizing the allocation of supply chain resources, and promoting international expansion. Heterogeneity analysis reveals that the green-empowerment effect is more pronounced among growth-stage firms, firms with stronger transformation willingness, and firms whose executives possess overseas backgrounds. It also exhibits a positive dependence on industry technological attributes, as well as regional service-sector development and digital infrastructure. Further analysis finds that digital services trade openness strengthens supply chain resilience and security through the greening pathway, thereby providing micro-level evidence and policy implications for jointly advancing the "green" and "secure" goals of supply chains.