Government investment funds leverage fiscal capital to guide and mobilize private capital toward key sectors. They have become a core component of the science and technology finance system and an important instrument for local governments to cultivate emerging industries and attract investment. The development history of Chinese government investment funds and their operational model of "fundraising, investing, managing, and exiting" are introduced. A micro-level database was constructed, covering the establishment of more than 1,900 government investment funds, over 20000 equity investment events, and more than 6,000 exit events. Several stylized facts are documented. Since 2014, when the central government began to restrict irregular preferential policies adopted by local governments, including tax incentives, fiscal subsidies, and land concessions, government investment funds have expanded rapidly as an alternative tool for promoting regional industrial development. Strong local investment preferences are exhibited by local government funds. The overall industry allocation of fund investments is dispersed, yet convergence in strategic emerging industries generates the risk of low level redundant construction. Exit channels are relatively limited and are significantly affected by the IPO policies of the China Securities Regulatory Commission. Regional disparities are widening, and central and western provinces display the characteristics of numerous establishments, difficult exits, and low returns. Several future research directions are proposed from the perspectives of sample selection, research orientation, risk assessment, and performance evaluation. A factual basis and directional insights are provided for regulating local governments' economic promotion behavior, advancing the high-quality development of government investment funds, and cultivating patient capital under a model that combines an effective market with a proactive government.

Against the backdrop of global energy structure transformation and deepening climate governance, the scale of China's energy investment in "Belt and Road" countries is continuously expanded and its structure is increasingly diversified. Given that the environmental impact of energy investment projects was characterized by long-term persistence, based on the data of fifty-four countries from 2008 to 2024, a fixed effect model and quantile regression method were built to explore the relationship between China's foreign energy investment and the green development of host countries. The results show that the countries' green development level is significantly promoted by China's energy investment, and the green promotion effect is stronger in countries with lower GTFP levels. A series of robustness and endogeneity tests was conducted. Meanwhile, the promotion of green development in countries by China's energy investment was achieved through four channels: investment structure optimization, green financial support, green technology innovation, and digital infrastructure construction. Furthermore, heterogeneity was demonstrated in sub-samples when investment form, investment location, and host country characteristics were considered. The green development effect of China's foreign investment in clean energy and greenfield merger and acquistion was found to be stronger, and the green development level of land "Silk Road" countries, Asian countries, middle-income and low-income countries, and countries with a low institutional environment was significantly enhanced. A theoretical basis and empirical support are provided by the results for actively responding to international public opinions and doubts, such as the "China pollution shift theory" and the "China environmental threat theory", and for continuously promoting the green "Belt and Road".

As a new engine to stimulate the vitality of consumption, the debut economy is the power engine to smooth domestic circulation and release the potential of domestic demand, which can quickly enhance the commercial level and attractiveness of cities and has an important impact on Chinese-style consumption modernization. Based on the panel data of 283 cities at the prefecture level and above in China from 2015 to 2024, the two-way fixed effect model and the intermediary effect model were used to examine the relationship between the debut economy, the allocation of innovative factors, and Chinese-style consumption modernization. The results show that the development of the debut economy plays a significant role in promoting the modernization of Chinese-style consumption. After adjusting the measurement method of the explained variables, excluding the sample data of provincial capital cities and municipalities directly under the central government, and controlling the policy interference in the same period, this conclusion still holds. The analysis of intermediary effect shows that the debut economy can promote the modernization of Chinese-style consumption by promoting the optimization of the allocation of innovative factors. Heterogeneity analysis shows that in large-scale cities and cities with a high marketization level, the debut economy has a stronger driving effect on Chinese-style consumption modernization, and it is obviously stronger than that of small and medium-sized cities and cities with a low marketization level. Therefore, countermeasures and suggestions were proposed to take multiple measures to promote the development of the debut economy, optimize the allocation of innovation factors, and cultivate the debut economy according to local conditions. The above conclusions can provide theoretical cornerstones and practical paths for China to promote the prosperity of the debut economy and cultivate new growth points of Chinese-style consumption modernization.

Amidst the intensifying global technological competition, the cross-regional flow of technological factors has become the core driving force for optimizing industrial structures and driving industrial transformation and upgrading. Grounded in the theoretical framework of "dual-circulation linkage" and "structural support", the synergistic enhancement mechanism governing cross-regional technological factor flows under the new dual-circulation development paradigm was elucidated. Based on multi-source patent transfer data covering 283 Chinese cities for the period from 2007 to 2023, a three-dimensional measurement system for industrial upgrading was constructed, and the interaction between international technology inflows and domestic technology outflows was empirically examined. Significant synergies are identified between these two flows. Domestic technology outflows are shown to exert a prominent structural supporting effect on industrial upgrading induced by international technology absorption, primarily by bolstering regional technological absorption and re-innovation capabilities. Empirical evidence is thereby provided regarding the path dependence risks inherent in exclusive reliance on technology introduction. Heterogeneity analysis reveals that the dividends of technology transfer are more readily harnessed in regions southeast of the "Hu Huanyong Line", where mature innovation ecosystems prevail. A double-threshold characteristic of domestic technology outflows is confirmed through further threshold tests, and the dynamic evolution process of technological factors from fragmented input to systemic integration is revealed. A gradient regional governance system and institutional opening-up strategies are proposed to mitigate insufficient value chain progression and uneven regional technological development. Theoretical support and practical pathways are thus offered for steering the deep coupling of innovation chains and industrial chains under the dual-circulation linkage framework.

Breaking labor market segmentation is recognized as a crucial aspect for building a unified national market and achieving high-quality development. An analysis of the impact of the digital economy on labor market segmentation was conducted using urban panel data from China. Firstly, the impact of the digital economy on labor market segmentation is found to exhibit an inverted U-shaped pattern, initially strengthening and subsequently weakening, which is confirmed to remain valid after endogeneity and robustness checks. Secondly, the influence of the digital economy on labor market segmentation is shown to vary by region, industrial structure, and economic foundation. The development of the digital economy in eastern and central regions, regions with a high proportion of the tertiary industry, and economically developed regions exerts a significant inverted U-shaped impact on labor market segmentation, while no significant impact is observed in western and northeastern regions, regions with a low proportion of the tertiary industry, and economically underdeveloped regions. Thirdly, human capital investment and industrial structure upgrading are identified as important mediating mechanisms through which the digital economy affects labor market segmentation. When the development level of the digital economy exceeds 0.270, labor market segmentation is weakened through the promotion of human capital investment. When the development level of the digital economy exceeds 0.337, labor market segmentation is weakened during industrial structure upgrading. Fourthly, the impact of the digital economy on labor market segmentation has a spatial spillover effect. The research conclusion provides empirical evidence for a profound understanding of the role of the digital economy in resolving the bottlenecks and obstacles of the national unified market and also offers important policy implications for accelerating the construction of a national unified market and achieving high-quality development.

The application of general artificial intelligence is gradually undergoing concept verification and scenario exploration across various industries, yet whether it can effectively boost labor productivity remains questionable. Therefore, whether the application of artificial intelligence can mitigate the New Solow Paradox has become a topic of considerable concern. Based on data from A share listed companies spanning from 2012 to 2023, the two way fixed effects model and the double machine learning model were applied to examine the mechanism by which the application of artificial intelligence mitigates the New Solow Paradox. Empirical evidence reveals that artificial intelligence applications can mitigate the New Solow Paradox. Mechanism analysis indicates that artificial intelligence applications mitigate the New Solow Paradox by optimizing labor structure, reducing organizational redundancy, and strengthening organizational capability, with incentive mechanisms playing a positive moderating role in this process. Heterogeneity analysis reveals that the application effects of artificial intelligence are more pronounced in non-high-tech industries than in high-tech industries, and significantly enhance total factor productivity in the eastern and central regions, while their efficacy is constrained in the western region. Furthermore, in terms of sectoral heterogeneity, the application of artificial intelligence significantly improves total factor productivity in manufacturing, services, and the production and supply of electricity, heat, gas, and water, while its productivity enhancing effects are constrained in agriculture, mining, and construction. Practical implications and policy recommendations are offered for enterprises to cope with the challenges of the New Solow Paradox and achieve leapfrog growth of enterprise total factor productivity driven by artificial intelligence.

Digital intelligence empowerment of ambidextrous innovation serves as a key driver for securing a leading position in technological development and accelerating the cultivation of new quality productive forces. Panel data from Chinese cities from 2010 to 2023 was used to examine the impact and mechanisms of digital intelligence on ambidextrous innovation. The findings are as follows. The role of digital intelligence in driving ambidextrous innovation is asymmetric. A linear positive effect on breakthrough innovation is exerted by it, while its impact on incremental innovation follows a U-shaped pattern, in which it is initially suppressed and then stimulated. The synergy in ambidextrous innovation is promoted by digital intelligence, but a differentiated influence on their interactive relationship is exerted, which is characterized by decreasing balance and increasing complementarity. Mechanism tests indicate that optimizing factor allocation and promoting knowledge agglomeration are key transmission pathways. Among these, the transmission effects of capital and labor factor allocation exhibit bias, while the transmission effect of knowledge agglomeration demonstrates selectivity. Moderation effect tests show that knowledge diffusion not only positively moderates the direct effects of digital intelligence on ambidextrous innovation and its synergy, but also exerts a positive moderating effect on the latter half of the transmission pathway for knowledge agglomeration. Heterogeneity analysis reveals that the driving effect of digital intelligence on breakthrough innovation is more prominent in resource-based and low-scientific-level cities. The driving effect of incremental innovation is significant only in high-scientific-level cities, whereas a significant inhibitory effect is observed in resource-based cities. The driving effect on ambidextrous innovation synergy is more prominent in resource-based and high-scientific-level cities. The theoretical understanding of digitally driven regional innovation is deepened, and a scientific basis is provided for optimizing the strategic layout of digital intelligence and unleashing innovation potential in a manner tailored to local conditions.

How digital transformation can effectively promote integrated innovation across the upstream, midstream, and downstream segments of industrial chains has become an important issue in the cultivation of new quality productive forces. Based on dynamic capability theory, Chinese A-share listed firms in Shanghai and Shenzhen from 2000 to 2023 were selected as the research sample. The impact mechanism and boundary conditions of digital transformation on corporate integrated innovation were systematically examined. The results show that the relationship between digital transformation and corporate integrated innovation is not simply linear. A significant inverted U-shaped relationship is found. This indicates that a moderate level of digital investment can significantly promote corporate integrated innovation. However, when digital transformation is promoted excessively, its marginal promoting effect gradually weakens. The conclusion remains valid after a series of robustness tests, including endogeneity treatment, replacement of core variables, and adjustment of the sample period. The moderating effect analysis further shows that economic policy uncertainty and market concentration negatively moderate this inverted U-shaped relationship. The mechanism test further indicates that digital transformation promotes integrated innovation mainly by reducing corporate R&D manipulation and improving resource flexibility. New theoretical explanations are provided for understanding the formation logic of corporate integrated innovation in the digital era. Practical references are also offered for enterprises to scientifically promote digital transformation, optimize innovation ecosystems, and enhance value co-creation capability during the 15th Five-Year Plan period.

Balancing economic development with environmental protection is central to achieving high-quality development. As a key measure for promoting industrial transformation and upgrading, the environmental governance effects of the pilot policy for innovative industrial clusters warrant in-depth examination. Based on panel data from 257 prefecture-level cities covering the period from 2010 to 2022, a multi-period difference-in-differences model was applied to systematically evaluate the impact of the innovative industrial cluster policy on urban environmental quality. The results indicate that the innovative industrial cluster policy significantly improves urban environmental quality. This conclusion remains valid after a series of robustness and endogeneity tests, including parallel trends tests, placebo tests, and PSM-DID. However, the policy effects exhibit significant heterogeneity, influenced by factors such as geographic location, resource endowments, the institutional foundation for environmental protection and the degree of marketization. Mechanism analysis reveals that the environmental optimization effects are primarily achieved through three pathways: incentivizing green technological innovation, guiding industrial restructuring, and leveraging the green transmission function of digitalization. Simultaneously, digitalization serves as a prerequisite for unlocking environmental dividends. Further analysis indicates that the environmental governance effects of this policy exhibit phased characteristics, with policy effectiveness showing a trend of diminishing marginal returns over time. From a spatial perspective, the policy pilot not only improved local environmental quality but also effectively reduced environmental pollution levels in neighboring areas, demonstrating positive spatial spillover effects. The conclusions provide empirical evidence for achieving synergistic governance between industrial development and environmental optimization under the new development paradigm, offering valuable insights for policymakers seeking to promote the development of innovative industrial clusters in a manner tailored to local conditions, unlock the potential of digital empowerment, and enhance the level of regional environmental governance.

Against the backdrop of intensifying global urgency in climate governance and the deepening advancement of China's "Dual Carbon" goals, the clarification of influencing factors of new quality productive forces on carbon efficiency is crucial for urban green transformation. A measurement system for new quality productive forces was constructed, encompassing six dimensions: data factors, new-type workers, new-type means of labor, technology research and development, new production organization, and new-type objects of labor. The non-radial directional distance function was employed to measure carbon efficiency at the prefecture-level city level, which is further decomposed into efficiency change and technological change. Furthermore, by integrating the LightGBM model with SHAP methods, the association characteristics between each element and carbon efficiency were systematically identified. The results indicate that data factors, new-type workers, and new-type means of labor serve as core driving forces for improving carbon efficiency, whereas technology research and development, new production organization, and new-type objects of labor exhibit potential influences. Regarding nonlinear relationships, most elements demonstrate threshold effects. Specifically, data factors and new-type means of labor display a U-shaped influence structure. The effects of new-type workers and new-type objects of labor show significant maturity dependence and scale threshold characteristics, while new production organization presents a phased positive impact. Mechanism decomposition reveals that efficiency change is primarily driven by the optimization of resource allocation by data factors and new production organization, whereas technological change is more supported by frontier technological innovations in new-type means of labor and new-type objects of labor. The green effect patterns of new quality productive forces are identified, providing a reference for precise urban low-carbon policy-making.