As the new round of scientific and technological revolution advances in tandem with the green and low-carbon transition, how to leverage industrial intelligence policies to simultaneously improve economic performance and environmental performance has become a critical issue for the long-term high-quality sustainable development of enterprises. Based on data from Chinese A-share listed companies from 2013 to 2024, and treating the National New Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment, a multi-period difference-in-differences method was employed to examine the impact of artificial intelligence policies on corporate sustainable development performance. The findings indicate that artificial intelligence policies significantly enhance corporate sustainable development performance. Mechanism tests show that artificial intelligence policies promote corporate sustainable development performance through two channels: stimulating corporate green innovation and optimizing capital allocation efficiency. Human capital positively moderates the enabling effect of artificial intelligence policies on corporate sustainable development performance. Heterogeneity analyses reveal that the enhancing effect of artificial intelligence policies on corporate sustainable development performance is more pronounced in high-tech industries, non-heavily polluting industries, and regions with low environmental regulatory intensity. Further analyses indicate that artificial intelligence policies generate spillover effects, which contribute to the improvement of sustainable development performance among other firms within the same industry and the same city. These conclusions not only elucidate the micro-mechanisms through which artificial intelligence policies enhance corporate sustainable development performance, but also provide policy implications for optimizing the strategic layout of artificial intelligence and promoting high-quality development.

The low rate of technology transfer and the disconnect between industry, academia, and research supply and demand are structural bottlenecks currently hindering the in-depth advancement of China's innovation-driven development strategy. How to unclog technology transfer channels and activate collaborative innovation among enterprises through institutional innovation in technology market intermediaries is a core issue facing both theoretical research and policy practice. The establishment of the National Technology Transfer Center is an important institutional arrangement for the government to build a professional technology market intermediary system and address the disconnect between supply and demand of scientific and technological achievements. A-share listed companies from 2010 to 2022 were employed as the research sample. Based on a quasi-natural experiment of the establishment of the National Technology Transfer Center, the difference-in-differences method was adopted to identify the impact and mechanism of technology market intermediaries on enterprise collaborative innovation. It is found that the establishment of the National Technology Transfer Center significantly promotes enterprise collaborative innovation, a conclusion that remains valid after a series of robustness tests. Mechanism tests reveal that the National Technology Transfer Center effectively functions as a technology market intermediary, exerting a positive impact on enterprise collaborative innovation through two channels: promoting knowledge spillovers and driving resource integration. Further analysis indicates that the aforementioned facilitation effects are more pronounced in regions with more developed technology markets, in enterprises with stronger basic R&D capabilities, and in regions with greater technology resource mobility, revealing a three-pronged collaborative innovation promotion logic of "transferring out-receiving-smooth circulation". These findings provide incremental evidence for local governments to further improve the technology market intermediary system and promote the transformation of scientific and technological achievements, and bear important practical implications for optimizing the efficiency of innovation resource allocation and activating the vitality of collaborative innovation among enterprises.

Against the backdrop of rising uncertainties in the international situation and increasingly severe environmental protection pressure, the formation of global green trade barriers is accelerating, and carbon information disclosure is regarded as crucial for enterprises to compete in the international trade market. As an important practical carrier of China's institutional opening-up, the construction of pilot free trade zones is found to profoundly reshape the carbon information disclosure behaviors of enterprises in pilot areas through policy impetus and market pull, and to help Chinese enterprises smoothly integrate into the international market. From the perspective of institutional opening-up, the linkage effect between pilot free trade zone construction and green governance was explored. Taking Shanghai and Shenzhen A-share listed companies from 2010 to 2023 as samples, a difference-in-differences model was constructed to evaluate the impact of pilot free trade zone construction on the quality of corporate carbon information disclosure. The research finds that the establishment of free trade zones significantly improves the carbon information disclosure quality of local enterprises. Mechanism analysis indicates that free trade zone construction drives enterprises to release high-quality carbon information through three paths: expanding export scale, stimulating the growth of green investment and deepening managers' environmental cognition. Heterogeneity analysis shows that the policy effect is particularly prominent in cities with high export dependence, export-oriented industries and enterprises with a large proportion of export trade. Further extended analysis verifies that free trade zone construction achieves an overall improvement in corporate carbon information disclosure quality covering both information content and disclosure detail. Micro-level empirical evidence is provided for understanding the transformation of corporate green governance driven by institutional opening-up, and references are offered for the optimized design of pilot free trade zones.

In the current context where global artificial intelligence technology is rapidly evolving while governance challenges abound, the DSG framework for coordinating the development of artificial intelligence and security governance offers a new theoretical perspective and practical framework for enterprises to enhance their innovation performance in the "artificial intelligence +" era. Using the listed companies in the Shanghai and Shenzhen stock markets from 2014 to 2023 as the research subjects, the mechanism was explored by which coordinating the development of artificial intelligence and security governance affects the innovation performance of enterprises based on the DSG framework. It is found that the impact effect of artificial intelligence under the DSG framework is manifested as the more enterprises attach importance to the coordinated development of artificial intelligence security and governance, the more conducive it is to significantly promoting the improvement of enterprise innovation performance. The influence mechanism is manifested in that artificial intelligence development has a significant positive impact on enterprise innovation performance through resource structuring, resource capability enhancement, and resource utilization. Heterogeneity analysis shows that non-private enterprises, large-scale enterprises, and high-tech industry enterprises pay more attention to such coordination. A more significant promoting effect on innovation development is demonstrated by these enterprises. Theoretical support is provided for enterprises to implement "artificial intelligence +" through coordinating artificial intelligence development and security governance. Practical insights are offered for coordinating artificial intelligence, security, and governance in the innovation and development process of enterprises. Chinese practices and solutions are contributed to address global artificial intelligence governance challenges and to promote intelligence to be beneficial.

A value-security data governance scenario matrix was constructed from the perspective of human-computer interaction theory. A four-stage data governance sandbox mechanism, including modeling (M), experiment (E), diagnosis (D), and interpretation (I), was further proposed for the high-value-high-risk data governance scenario. The following findings are obtained. Firstly, a new theoretical perspective and analytical approach are provided for data governance in the digital economy by human-computer interaction theory. Data governance is not regarded as a linear process in which security compliance precedes data utilization or data utilization precedes security compliance. A dynamic balance between data utilization and security compliance is required. Secondly, a value-security data governance scenario matrix can be established based on the two dimensions of value density and security risk. The key challenge of data governance is identified in the high-value-high-risk scenario, where substantial value can be created through data utilization, while significant losses may occur if data are not effectively protected or are illegally exploited. Thirdly, the MEDI data governance sandbox mechanism enables rule solidification, problem tracing, and rule regeneration through human-computer interaction. The sandbox is therefore transformed from a technical isolation tool into a dynamic data governance system characterized by verifiability, traceability, and regenerability. An embeddable and reusable data governance mechanism framework is provided for the implementation of Article 13 of the Data Security Law, which requires data security to be promoted through data utilization and data utilization to be safeguarded through data security.

A solid foundation for all people to share the developmental achievements of the digital era is established by computing infrastructure, which serves as a critical support for the solid advancement of common prosperity. Panel data of 254 prefecture-level and above cities in China from 2012 to 2024 were adopted for research. Evaluation index systems for computing infrastructure and common prosperity were constructed, and the entropy weight method is applied for quantitative measurement. The impact effect and mechanism of computing infrastructure construction on common prosperity were systematically analyzed. Empirical results demonstrate that common prosperity is significantly and positively promoted by the construction of computing infrastructure. Mediating effect tests verify that computing infrastructure construction facilitates common prosperity through three effective channels, including industrial structure upgrading, employment structure optimization, and consumption expansion and quality improvement. Heterogeneity tests confirm that the empowering effect of computing infrastructure construction on common prosperity presents obvious urban differentiation characteristics. The effect is stronger in cities with high administrative levels than in ordinary cities and stronger in cities with high-level intellectual property protection than in cities with low-level intellectual property protection. The influence of computing infrastructure construction on common prosperity also shows a hierarchical characteristic of the common prosperity dimension > sharing dimension > sustainability dimension. Threshold effect tests indicate that the promotion effect of computing infrastructure construction on common prosperity is constrained by a double threshold of talent digital literacy and a single threshold of market-oriented allocation of data elements. Targeted suggestions are proposed to accelerate the construction of an inclusive mechanism for computing power services and smooth the empowerment path of digital infrastructure. Relevant research conclusions provide effective references for promoting substantive progress in common prosperity.

Breakthroughs in artificial intelligence technologies have elevated the smart economy to an advanced form of the digital economy and a core engine for fostering new quality productive forces, drawing extensive academic attention. Existing research achievements from four dimensions: theoretical connotation, measurement methods, effect mechanisms, and governance challenges were systemqtically reviewed and commented on. In terms of connotation definition, academic understanding has evolved from mere technical application to the transformation of economic forms, forming a theoretical framework built on the tripartite collaboration of data, algorithms, and computing power as well as human-machine symbiosis. New concepts, including Token economy and embodied intelligence, have also emerged. In respect of measurement methods, the construction of comprehensive indicator systems has become the mainstream approach, which reveals the regional heterogeneity and convergence characteristics of the smart economy. As for effect mechanisms, existing studies have verified that the smart economy plays a vital role in boosting total factor productivity, advancing industrial upgrading, promoting the integration of the real economy and the digital economy, expanding consumption scenarios, restructuring employment patterns, and facilitating green development and regional coordination. In the field of governance, scholars have analyzed multiple challenges ranging from technological dependence to algorithmic risks and have put forward countermeasures such as enhancing institutional flexibility and building an agile governance framework. Finally, it points out the deficiencies of current research in theoretical construction, measurement techniques, and empirical depth. It also looks ahead to future research directions, including establishing an independent knowledge system for the smart economy with Chinese characteristics, conducting in-depth empirical research on micro mechanisms and macro effects, and strengthening systematic studies on governance risks and social impacts. It holds important theoretical value for clarifying the theoretical context of the smart economy, grasping its evolutionary laws. It also provides academic reference and policy insights for fostering new forms of the smart economy and cultivating new quality productive forces during the 15th Five-Year Plan period.

Collaborative green transformation across industrial chains has become an important driver for the achievement of the dual-carbon goals. Existing research has mainly focused on the horizontal diffusion of green innovation within industries. Insufficient attention has been paid to the vertical transmission mechanism of green innovation along supply chains. The different roles of constraint and spillover mechanisms in this process have also not been fully distinguished. From the perspective of vertical trading relationships, Chinese A-share listed firms from 2013 to 2024 were selected as the sample. The transmission effects of green innovation in supply chains and their boundary conditions were empirically examined. It is found that an increase in the green innovation level of focal firms significantly promotes green innovation among upstream suppliers. No significant effect is found on green innovation among downstream customers. Further analysis shows that the backward transmission effect is significantly strengthened by stronger vertical market power of focal firms. Mechanism analysis indicates that upstream suppliers' green innovation is mainly promoted through increased green investment by suppliers. Heterogeneity analysis shows that both substantive green innovation and strategic green innovation among suppliers are significantly promoted by focal firms' green innovation. The backward transmission effect is also found to be more pronounced when suppliers have higher levels of digitalization, stronger dependence on focal firms, and shorter geographic distance from focal firms, and when focal firms exhibit better ESG performance. The internal logic of the vertical transmission of green innovation along supply chains is thus revealed. Empirical evidence is also provided for the coordinated green transformation of industrial chains.

While existing studies examine how structural holes or closure networks independently affect organizational innovation performance, this body of work remains constrained by static and singular perspectives. The temporal dynamics of network structure transitions in R&D collaborations are examined. It is argued that organizations strategically transition between structural hole and closure networks across different phases of the innovation process, thereby forming an ambidextrous network transition pattern. Drawing on longitudinal network data from 784 U.S. biopharmaceutical firms and patent collaboration sequences spanning 15 years from 1998 onward, optimal matching analysis is employed to identify network transition patterns. The findings indicate that firms exhibiting a higher degree of transition between structural hole and closure networks achieve greater exploratory innovation but at the expense of exploitative innovation. Furthermore, the moderating roles of two partner network characteristics-tie stability and network status-are examined. By adopting a dynamic configurational perspective on network evolution, this research extends the literature on interorganizational networks and innovation while offering practical implications for managing collaborative innovation.

During the 15th Five-Year Plan period, the expansion of service consumption is regarded as an important support for high-quality development. Domestic services are a key part of this process. However, longer working hours tighten household time constraints and raise the non-monetary costs of service use. Urban households may therefore fail to increase domestic service consumption in line with income growth. Data from the China Family Panel Studies (CFPS) were used to construct a "time-effect" analytical framework. The effect of working time on domestic service consumption was examined. Firstly, longer working hours significantly reduce domestic service consumption. The result remains robust after income, assets and other factors are controlled. Time allocation therefore constitutes an important constraint independent of income. Secondly, the negative effect is explained from the perspective of service-governance costs. Longer working hours do more than reduce disposable time. They also raise the governance costs involved in purchasing household services. Cognitive burdens and trust costs are increased. Household role norms are also reinforced. The conversion of household service demand into market consumption is consequently impeded. Third, digital literacy and institutional safeguards can partly reduce information asymmetry and perceived risk. However, the structural barriers created by rigid time constraints cannot be fully removed. The results indicate that insufficient service consumption cannot be attributed solely to weak demand. Time constraints have become a more hidden and persistent boundary of consumption. Greater working-time flexibility should be promoted. Household divisions of labour should be reshaped. Service supply should be improved. Institutional safeguards should also be strengthened. It reveals the underlying mechanism by which extended working hours hinder the transformation of household service demand into effective consumption, thereby providing empirical evidence to support the high-quality development of the service sector.