Boosting the intelligent, green, and integrated development of industries is fundamental to building a modern industrial system. Among them, promoting industrial integration serves as the industrial bedrock for advancing the construction of this system and accelerating the emergence of new quality productive forces. Producer services’ inherent integrative specialty positions them as a key direction for China ’ s industrial restructuring and upgrading. Analysis based on cross-national data reveals that the share of producer services remains relatively low in China, indicating untapped potential for driving economic growth. As a strategic general-purpose technology driving the new wave of technological revolution and industrial transformation, artificial intelligence (AI) shifts industrial development from stronger specialization to mutual integration, becoming a critical tool for accelerating industrial integration and promoting the development of producer services. This paper develops a multi-sector dynamic general equilibrium model that incorporates AI and industrial integration . The findings suggest that when AI is biased toward manufacturing and the elasticity of substitution between manufacturing and producer services is low, AI promotes the integration of manufacturing and producer services, thereby increasing the share of producer services through intensive marginal effects. Simulation results demonstrate that AI can effectively promote industrial integration and industrial restructuring and upgrading . Furthermore, raising new infrastructure investment rates and reducing labor mobility costs across service sectors can further promote industrial integration and accelerate the development of producer services. This paper presents the theoretical mechanism through which AI affects industrial integration and industrial restructuring and upgrading from a macroeconomic structure perspective and provides policy recommendations on how AI can promote industrial integration and develop producer services.

In the current “dual circulation” development paradigm, science and technology finance provides strong support for breaking the low-end lock-in of China’s manufacturing sector within the global value chain. Utilizing provincial panel data from China covering the period from 2011 to 2021, this study empirically examines the impact of science and technology finance on the upgrading of the manufacturing value chain from the perspective of technology transfer . The findings indicate that science and technology finance can promote the upgrading of the manufacturing value chain, with this promoting effect exhibiting regional and source heterogeneity. For one thing, the impact of science and technology finance on value chain upgrading is notably significant in Chinese eastern regions. For another, science and technology finance inputs from venture capital institutions, capital markets, and enterprises significantly drive the upgrading of the manufacturing value chain. In terms of the impact mechanism, technology transfer plays a partial mediating role in the process by which science and technology finance promotes the upgrading of the manufacturing value chain; however, the mediating effect of technology transfer varies based on the source of science and technology finance input. Furthermore, the promoting effect of science and technology finance on the upgrading of the manufacturing value chain also displays nonlinear characteristics, becoming more pronounced when the level of technology transfer surpasses a certain threshold.

The industrial ecosystem covers the advanced technologies, key industries and core factors in the process of developing new quality productive forces, serving as an important perspective for investigating the development of new quality productive forces. Based on the analytical framework comprising technologies, industries and factors, this study examines the internal logic of the development of new quality productive forces. Firstly, the four industrial revolutions in history demonstrate that the industrial ecosystem has promoted a series of revolutionary technology innovations. Secondly, the industrial ecosystem promotes the formation and development of emerging industries through resource sharing, knowledge spillover, and technology diffusion. Thirdly, the industrial ecosystem achieves an innovative allocation of factors of production through the dynamic feedback loop mechanisms of data-algorithm-traffic and data-network-activity. The industrial ecosystem consists of three subsystems, including the innovation ecosystem, the business ecosystem, and the platform ecosystem. The three subsystems transcend the boundaries of innovation for new quality productive forces, broaden their application scenarios, and enhance the efficiency of factor allocation, respectively. To further advance the development of new quality productive forces, it is necessary to establish a system ensuring the coordinated development of all factors. Additionally, efforts are required to integrate the dual driving forces of technology innovation and institutional innovation, and enhance the integration of four chains, including the innovation chain, the industrial chain, the capital chain, and the talent chain.

Can the geographical proximity of an industry to the “technological knowledge pool” outside its own sector effectively enhance its innovation performance? Are there differences in the effects brought about by geographical proximity based on different types of linkages? Under the framework of the knowledge production function, this paper empirically examines the innovation performance enhancement effect of differentiated technological knowledge pools formed by directional industrial spatial coagglomeration, using data from the industrial enterprise database and patent database. The findings reveal that the level of industrial innovation is positively influenced by the diverse technological knowledge pools generated through industrial spatial coagglomeration. This conclusion remains valid even after addressing potential endogeneity issues by employing the UK’s industrial coagglomeration index as an instrumental variable. In particular, knowledge spillovers serve as the primary mechanism through which industrial coagglomeration is influenced by technological knowledge pools from outside its own sector. The innovation spillover effect of active coagglomeration is significantly greater than that of passive coagglomeration, and the impact of technological knowledge pools on the scale of industrial innovation is slightly stronger than on the quality of innovation. Further research indicates that only active coagglomeration between industries with input- output linkages can significantly enhance the innovation capabilities of both industries, while industrial coagglomeration with technological linkages demonstrates a notable “parasitic effect.” The policy implications of this paper suggest that local governments should thoroughly consider the spatial dependency relationships and historical patterns of inter-industry location selection when developing regionally diversified industrial clusters. Simultaneously, they should strengthen intellectual property protection and industry regulation to achieve high-quality development of regional industries.

Accelerating the concentration of capital factor resources in the fields of scientific, technological, and industrial innovation is of significant importance for fostering the emergence and development of new quality productive forces characterized by high technology, high efficiency, and high quality. From the perspective of science and technology finance supporting the development of new quality productive forces, this paper first clarifies the conceptual connotation of patient capital and defines its characteristics and expansion of its scope. It then elucidates the operational mechanisms through which patient capital underpins and facilitates the development of new quality productive forces. Furthermore, this paper discusses the essential principles, requirements, and key points of comprehensive support for new quality productive forces through patient capital, including science and technology credit, venture capital, and stock financing. Finally, several policy recommendations are proposed to cultivate and strengthen patient capital under the current conditions.