Based on a systematic analysis of the role and mechanism of artificial intelligence in the high-quality development of the equipment manufacturing industry, this article, using panel data from 30 provinces in China from 2013 to 2022, empirically examines the impact of artificial intelligence on the high-quality development of the equipment manufacturing industry and its internal mechanism, and analyzes the regional heterogeneity and digital infrastructure level heterogeneity of this impact. The study finds: (1) Artificial intelligence has a significant positive promoting effect on the high-quality development of the equipment manufacturing industry, and this research conclusion has passed the endogeneity analysis and robustness test; (2) Artificial intelligence can promote the high-quality development of the equipment manufacturing industry by improving the level of human capital and the efficiency of resource allocation; (3) The impact of artificial intelligence on the high-quality development of the equipment manufacturing industry has significant regional heterogeneity and digital infrastructure level heterogeneity. The eastern and central regions have a significant promoting effect on the high-quality development of the equipment manufacturing industry through artificial intelligence, while the western regions have no significant impact, and the northeastern regions have a significant negative effect. When the level of digital infrastructure is high, artificial intelligence can significantly promote the high-quality development of the manufacturing industry, but when the digital infrastructure is low, there is no effect.

Traditional supply chain finance has played an important role in alleviating financing constraints faced by micro, small and medium enterprises (MSMEs). However, it still suffers from excessive reliance on the credit endorsement of core enterprises, limited risk penetration, and weak authenticity verification. As a new generation of generative artificial intelligence technologies, financial large models are being rapidly embedded into financial scenarios and have demonstrated substantial potential in multi-source data integration, dynamic credit evaluation, and intelligent decision optimization. Against this backdrop, this paper investigates how financial large models empower supply chain finance for SMMEs. It develops a dual-mechanism analytical framework from two dimensions: information integration and the reconstruction of the credit evaluation system, and the optimization of financing ecosystem coordination. On this basis, a tripartite evolutionary game model involving financial institutions, SMMEs, and core enterprises is constructed, and MATLAB-based numerical simulations are employed to examine the evolutionary path toward stable equilibrium in the tripartite game. The results show that, in the absence of empowerment by financial large models, the tripartite game system converges to a low-efficiency equilibrium. As the empowerment intensity increases, the system gradually exhibits a tendency to evolve toward a coordinated and mutually beneficial state among the three parties. Once the empowerment intensity exceeds a certain threshold, the system ultimately reaches a high-efficiency evolutionary stable equilibrium. Further analysis indicates that empowerment intensity, credit evaluation accuracy, and coordination efficiency are the key factors shaping the evolutionary direction of the tripartite game system. The findings extend the theoretical framework of digital technology-enabled supply chain finance and provide practical references for promoting the application of financial large models in supply chain finance, implementing digital finance policies, and easing financing constraints for SMMEs.

Artificial intelligence (AI), as a transformative and disruptive technology driving the new wave of technological revolution and industrial transformation, exerts a powerful "leading goose" effect. It is propelling socioeconomic development into a new era of intelligent economy with unprecedented depth and breadth. The integrated development of AI innovation chains and industrial chains ("dual chains") serves as the fundamental pathway for cultivating new quality productivity. Through comparative analysis and indicator evaluation, research reveals persistent challenges in China's AI "dual chains" integration, including insufficient industry-wide penetration and inadequate full-chain applications. From the perspective of technological integration innovation, this study examines the fusion mechanisms of AI "dual chains" based on multi-component technological characteristics, evolutionary economics, and complex systems theory. Theoretical analysis demonstrates how technological integration innovation creates node effects during integration processes, revealing correlations between innovation complexity and phenomena like the "bucket effect" and "nonlinear leap" in product innovation. Evolutionary characteristics of technological components and their combination mechanisms are employed to explain underlying causes of AI "dual chains" integration challenges. Practical implementation follows a structured pathway: "scientific foundation→technological invention→integrated innovation→product development→market expansion→industrial growth". Research indicates that overcoming integration bottlenecks requires establishing scientific foundations through component screening libraries, developing transfer and adaptation mechanisms for AI components, fostering cross-domain "transitional technologies", breaking path dependencies to innovate technical pathways and industrial restructuring, and enhancing integration efficiency through AI empowerment. In complex systems of the intelligent economy, appropriate integration models should be selected based on objectives, technological characteristics, and industrial features: for technologies and industries driven by national strategic priorities, adopt the "government-led→nationwide system implementation→systematic R&D breakthroughs→strategic technology component integration" model; for forward-looking pillar technologies and industries, implement the "dual-chain collaborative drive→core technology component integration→supporting technology component integration→product innovation" model; for mass consumption and service-oriented technologies and industries, utilize the "demand-driven→modularized technology components→low-cost technology integration" model.

Under the background of the transformation of global artificial intelligence paradigm and the high-quality development of China's economy, "artificial intelligence+" has become a strategic fulcrum for cultivating new quality productivity. This paper clarifies the internal mechanism of "artificial intelligence+" empowering new quality productivity from the theoretical level, and explains the systematic empowerment logic of "artificial intelligence+" empowering new quality productivity from the four dimensions of production factor reconstruction, innovation system innovation, industrial ecology reconstruction and institutional coordination adaptation. Based on the law of technological evolution and productivity development, this paper constructs a four-level progressive transition path of "bottom-middle-high-top-top", and clarifies the core tasks and implementation priorities of each level. Aiming at the problem of policy fragmentation, an integrated policy system of top-level design coordination, factor guarantee coordination, scenario empowerment coordination and risk regulation coordination is proposed to form a cross-domain, cross-level and cross-subject policy integration and synergy mechanism to break the development bottleneck. This study provides theoretical support and practical guidance for "artificial intelligence+" to efficiently empower new productivity and promote economic quality and efficiency.

As we stand at the confluence of a new round of scientific and technological revolution and the accelerated iteration of artificial intelligence (AI)technologies, deepening and expanding the "AI+" strategy and forging new forms of the smart economy is a major task of our times. It is of great significance to systematically explore the internal mechanisms and practical pathways through which AI empowers industrial upgrading, governance innovation, and green development. This column features six articles that complement each other and form a coherent research framework. They examine the internal logic and implementation pathways of AI empowering a modernized industrial system, with a focus on resolving structural industrial contradictions and promoting the transformation of the industrial system from scale expansion to growth driven by quality and efficiency. They explore the acceleration of the integration of AI with manufacturing to reshape production models and economic patterns, driving industrial transformation and reshaping the global economic landscape. They analyze the mechanisms of AI-driven service sector upgrading, proposing development strategies from the perspectives of business model innovation and value chain enhancement to facilitate the intelligent upgrading of modern services. They focus on embodied intelligence as a core track of future industries, elucidating the paradigm shift brought about by its deep integration with high-end manufacturing, and constructing a coordinated development framework integrating technology, industry, and policy. They investigate the application of AI in the collaborative governance of deep-sea resource exploitation and protection, advancing a shift toward data-driven and intelligent coordination in marine governance. They also study the intelligent transformation of the sky-land-sea global monitoring system from the perspective of green development, proposing technical solutions and operational mechanisms for comprehensive intelligent monitoring. Collectively, these articles provide academic insights and policy references for advancing the "AI+" strategy, building a modernized industrial system, and cultivating new quality productivity, thereby offering significant practical value for promoting the high-quality development of China's smart economy.

The comprehensive advancement of artificial intelligence as a strategic technology for leading innovation and improving its regulatory framework is of significant importance. This paper utilizes machine learning methods to generate an artificial intelligence lexicon based on data from Chinese A-share listed companies from 2015 to 2024. It conducts text analysis on annual reports to measure artificial intelligence technology application levels and empirically examines the effects of artificial intelligence application and privacy regulation on corporate innovation. The findings indicate that artificial intelligence technology positively impacts corporate innovation mainly through three mechanisms: promoting technological transformation, enhancing management efficiency, and optimizing labor structure. However, higher spending on privacy regulation reduces the positive impact of artificial intelligence on innovation output. Heterogeneity analysis shows that stronger privacy regulations enhance the positive effect of artificial intelligence application on innovation in certain industries. Additionally, the benefits of artificial intelligence are more pronounced in labor-intensive and technology-intensive sectors, with a stronger impact observed in labor-intensive firms. State-owned enterprises also experience a greater positive effect on innovation output from artificial intelligence application, with the negative moderating effect of privacy regulation applying only to state-owned firms. This paper provides new empirical evidence on the relationship between artificial intelligence application, privacy regulation, and corporate innovation.

Amidst the profound shifts in the global economic landscape and challenges such as weak risk resilience, enhan-cing the resilience of "specialized, refined, unique, and innovative" (SRUI) enterprises has become a pressing strategic impera-tive. Using data from China's listed SRUI enterprises from 2011 to 2024 as the sample, this study employs the policy of National New Generation Artificial Intelligence Innovation and Development Pilot Zones(referred to as AI pilot zone)as a quasi-natural experiment. It adopts a multi-period DID approach to explore the impact effect of the AI pilot zone policies on the resilience of SRUI enterprises. The findings indicate that AI pilot zone policies can empower the resilience of SRUI enterprises, and the conclusion that holds up after a series of robustness tests. The mechanism tests reveal that AI pilot zone policies enhances the resilience of SRUI enterprises primarily through two pathways: optimizing resource allocation and improving technological absorption capacity. The heterogeneity analysis reveals that AI pilot zone policies' positive effects are more pronounced for the non-state-owned enterprises, the manufacturing firms, the companies in growth and mature stages, the enterprises in eastern regions, and the areas with high digital inclusive finance indices.This study provides valuable insights for enhancing the resilience of SRUI enterprises and ensuring their long-term high-quality development.

The National AI Innovation Application Pilot Zones, as a crucial institutional arrangement to promote the deep integration of artificial intelligence with the real economy, provide policy support for firm innovation activities. Using a sample of Chinese A-share listed companies from 2015 to 2023, this study employs a double machine learning model to empirically examine impact of construction of the pilot zones on firm' integration of technological and industrial innovation. The study finds that the construction of the pilot zones significantly promotes this integration, and this promoting effect is more pronounced in firms with a strong cooperative culture, facing intense market competition, and located in the eastern region. Mechanism tests indicate that the construction of the pilot zones primarily fosters the integration by enhancing data factor utilization, optimizing human capital structure, and alleviating financing constraints. Moreover, the study finds that asset specificity plays a negative moderating role, significantly weakening the promoting effect of the construction of the pilot zones on firms' integration of technological and industrial innovation. This study not only provides empirical evidence for understanding the intrinsic mechanisms through which AI policy empowers firm' integration but also offers policy insights for optimizing the construction of pilot zones and promoting the development of national scie-nce and technology industries.

Amid the global energy transition and the rapid advancement of the new energy industry, artificial intelligence (AI)serves as a critical tool for overcoming the information silos and path dependence characterizing traditional energy supply chains, thereby fostering the growth of new energy enterprises. Utilizing data from Chinese listed new energy enterprises spanning 2007 to 2024, this paper employs large language models to measure AI levels from an industrial chain perspective and investigates the impact and underlying mechanisms of AI on the growth of new energy enterprises. The empirical results indicate that AI significantly drives the growth of new energy enterprises by promoting the digitization and diversification of supply chain configurations. Furthermore, heterogeneity analysis reveals that this promotional effect is more pronounced among non-state-owned enterprises, firms in the growth stage, and those with lower financing constraints. This study not only offers a theoretical basis for the intelligent transformation of supply chain management in new energy enterprises but also provides managerial insights for the high-quality sy-nergistic development of AI and the new energy industry.

The impact of China's Artificial Intelligence Innovation and Development Pilot Zone policy on the investment efficiency of micro-enterprises is the key to assessing the effectiveness of AI in empowering the real economy. Based on a sample of A-share listed companies from 2007 to 2024, this paper uses a multi-period double-difference method based on a quasi-natural experiment of the batch establishment of the National New Generation of Artificial Intelligence Innovation and Development Pilot Zones (AI Pilot Zones Establishment)to explore the impact of the AI Pilot Zones on the investment efficiency of enterprises. The study finds that the AI pilot zones are able to improve the investment efficiency of enterprises. The study finds that the AI pilot zones can inhibit the inefficient investment of enterprises. Mechanism analysis shows that AI pilot zones can inhibit enterprise inefficient investment mainly through the "governance effect" and "information effect"-reducing agency costs, mitigating credit mismatch, and reducing the level of perceived economic policy uncertainty; Heterogeneity test reveals that the inhibitory effect is more significant in the samples of enterprises with a high proportion of technicians, high level of data element utilization, non-state-owned enterprises and underinvestment enterprises. The above study provides empirical evidence for enterprises to optimise resource allocation and policy insights on how to further enhance the policy dividends of the establishment of AI pilot zones.

Scientifically assessing whether and how the large language models industry can drive the development of new quality productivity is crucial for harnessing the new technological revolution to foster high-quality economic development. Using provincial panel data from China spanning 2014~2023, this study empirically examines the driving efficacy and underlying mechanisms of the large language models industry on new quality productivity. The findings reveal that the large language models industry significantly promotes the development of new quality productivity, primarily through three pathways: the optimization of capital allocation, the transformation of industrial achievements, and the stimulation of consumer demand. Further analysis indicates that, temporally, the driving efficacy began to emerge after the introduction of the Transformer architecture in 2017. Spatially, its impact on new quality productivity is more pronounced in the central, western and northeastern regions compared to the eastern region. This study not only provides macro-empirical support for a deeper understanding of the economic empowering role of the large language models industry but also offers practical policy implications for formulating region-specific development strategies aimed at cultivating new quality productivity and achieving high-quality development.

Taking 30 provinces in China from 2014 to 2024 as the research object, this paper empirically analyzes the impact of AI on the development of low altitude economic modernization industrial system.The benchmark regression test results indicate that artificial intelligence promotes the development of a modernized industrial system in the low-altitude economy.Heterogenei-ty test results show that, compared to the western region, the promoting effect of artificial intelligence on the development of a modernized industrial system in the low-altitude economy is more pronounced in the eastern and central regions.Mechanism test results reveal that artificial intelligence can drive the development of a modernized industrial system in the low-altitude economy through breakthrough innovation.Threshold test results demonstrate that artificial intelligence has a nonlinear impact on the development of a modernized industrial system in the low-altitude economy. Accordingly, suggestions are put forward to strengthen the investment in basic resources of artificial intelligence, create a highland of breakthrough innovation and development, and establish a digital ecosystem, in order to provide beneficial suggestions for the development of China's low altitude economic modernization industrial system.

Based on microdata from 706 listed new energy companies in China from 2010 to 2024, this paper empirically examines the impact mechanism of artificial intelligence technology on enterprises' new quality productivity.The findings are as follows: First, the application of artificial intelligence significantly enhances the new quality productivity of new energy enterprises.Second, mechanism analysis reveals that AI primarily improves new quality productivity indirectly through two pathways: optimizing the labor structure of enterprises and promoting their digital transformation.Third, heterogeneity analysis shows that the promoting effect of AI is significant in enterprises located in the eastern and central regions but not in the western region, reflecting that regional disparities in infrastructure, technological accumulation, and talent reserves influence the effectiveness of AI technology.This study provides empirical evidence for understanding the micro-mechanisms through which AI drives the high-quality development of the new energy industry and offers decision-making references for regionally differentiated policies promoting the integration of AI and new energy.

In the context of the vigorous rise of technological revolution and industrial transformation, artificial intelligence is increasingly becoming a strategic force driving the fiscal sector to achieve high-quality development. This paper uses panel data of 279 prefecture-level cities in China from 2007 to 2024 as a sample, and employs the two-way fixed effect model and the mediating effect model to empirically test the impact of artificial intelligence on the quality of fiscal revenue, its mechanism and heterogeneity characteristics. The research findings show that artificial intelligence can significantly improve the quality of fiscal revenue, and this conclusion still holds after undergoing endogeneity tests and a series of robustness tests. The mechanism analysis indicates that artificial intelligence enhances the quality of fiscal revenue through digital infrastructure effects, industrial upgrading effects and institutional adaptation effects, with the mediating effect proportions being 7.861%, 10.492% and 9.979% respectively. The heterogeneity analysis reveals that the enhancing effect of artificial intelligence on the quality of fiscal revenue is more prominent in coastal cities, economically developed cities and cities with high fiscal self-sufficiency rates.