As a strategic technology driving the new wave of scientific and technological revolution and industrial transformation, artificial intelligence(AI) plays a significant role in enhancing the total factor productivity(TFP) of enterprises. Based on the theories of regional innovation systems and endogenous growth, this study uses panel data from companies listed on the Shanghai and Shenzhen A-shares between 2016 and 2024 to examine the impact of AI innovation development pilot zone policies on improving enterprise TFP. The results demonstrate that this policy significantly boosts corporate TFP, and these results remain robust after a series of robustness tests. Heterogeneity analysis reveals that the policy has a stronger effect in the central and western regions and for non-high-tech industries. Further analysis indicates that the policy influences TFP through two channels: deepening the application of AI and improving resource allocation efficiency. The policy not only enhances enterprise TFP but also strengthens profita-bility. This study provides empirical evidence for the role of the AI Innovation Pilot Zone policy in fostering corporate TFP growth and offers important policy implications for improving enterprise productivity.

Developing new quality productivity is an inherent requirement and an important focus for promoting high-quality development. Characterized by technological intensity, the development of new quality productivity requires adequate financial support. Based on panel data from 30 provincial-level administrative regions in China from 2012 to 2023, this paper employs a range of econometric methods, including ordinary least squares (OLS) regression, instrumental variable-two-stage least squares (Ⅳ-2SLS), and lagged regression, to validate the impact of digital finance on new quality productivity. Building upon these results, threshold effect models and spatial Durbin models are applied to systematically explore the non-linear and spatial effects of digital finance. The findings reveal that: (1) digital finance significantly promotes the enhancement of new quality productivity; (2) the impact of digital finance exhibits a significant threshold effect related to the scale of social financing, with its promoting effect further strengthening as the scale expands, showing a nonlinear pattern; (3) the development of new quality productivity across provinces exhibits a certain degree of spatial interdependence, whereby improvements in local new quality productivity generate positive spillover effects on neighboring regions; (4) the improvement of digital finance levels in adjacent regions can indirectly inhibit the deve-lopment of local new quality productivity through a "siphon effect". This paper innovatively reveals the threshold and spatial effects of digital finance on new quality productivity, providing a novel perspective for understanding inter-provincial economic integration. Furthermore, it offers theoretical and practical insights for advancing the construction of a "strong financial nation" and fostering new quality productivity.

Under the wise leadership of the Party Central Committee with Comrade Xi Jinping as its core, the development of AI industry, and the R & D and application of large language models in China have been in the first echelon in the world.In August 2025, the State Council's Opinions on the In-depth Implementation of the Artificial Intelligence Plus Action drew a grand blueprint for further promoting the extensive and deep integration of artificial intelligence with various sectors of the economy and society.The vigorous development of intelligent economy is inseparable from the rapid progress of intelligent technology, critically, it also requires enterprises to renew their organizations and adjust strategies to grasp new opportunities and tap into new potential.However, the intelligent economy with unlimited potential also contains greater technological and social risks, such as data security, unemployment of vulnerable workers and so on.Therefore, the society should actively foresee all kinds of risks and formulate countermeasures to promote the stable and healthy development of AI economy.

As a reverse regulatory mechanism in international capital flows, foreign capital withdrawal has emerged as a criti-cal issue affecting supply chain stability within the framework of national economic circulation.Utilizing supply chain data from Shanghai and Shenzhen A-share listed companies spanning from 2010 to 2023, the research reveals that the retreat of foreign capital notably diminishes supply chain resilience.Mechanism analysis identifies dual transmission pathways: the capital chain exacerbates corporate financing constraints, while the innovation chain diminishes bidirectional technology spillovers across horizontal and vertical dimensions.Heterogeneity analysis further indicates that this weakening effect is more pronounced in non-state-owned enterprises, asset-intensive enterprises, enterprises with high virtual agglomeration, and those with significant environmental sensitivity.Additionally, foreign capital withdrawal exhibits multi-level spillover effects within the industry, while the establishment of free trade zones exhibits dual positive impacts on optimizing capital structure and bolstering supply chain stability.This paper offers crucial policy insights for attracting and effectively leveraging foreign capital, as well as enhancing the quality and efficiency of China's supply chain.

Under the background of the new round of global scientific and technological revolution and the accelerated evolution of industrial transformation, "artificial intelligence+", as a practical paradigm of integration and innovation, is promoting systematic changes in various fields of society by means of technological empowerment.This paper takes China's provincial policies of"artificial intelligence+"as the research object, analyzes 38 policies of"artificial intelligence+"in China with the help of word frequency analysis and semantic network analysis, and builds a PMC index evaluation model of China's provincial policies of "artificial intelligence+"on this basis.After evaluating the selected 16 provincial policies, it is found that the average PMC index score of the 16 provincial policies is 7.38, of which Zhejiang Province has the highest policy score(8.83)and Shaanxi Province has the lowest policy score(5.62).It is found that the overall score of"artificial intelligence+"policies at the provincial level in China is good and there are no bad policies, but there are significant differences between the policies in the eastern, central and western regions.In addition, most policies still have much room for improvement in terms of policy timeliness, empowerment direction and policy tools.In this regard, we can improve it from the aspects of enriching policy subjects, expanding application direction, perfecting policy tools and optimizing policy timeliness.

As a core factor of production in the digital age, data has emerged as a crucial driving force in promoting and empowering the innovative development of the real economy.Grounded in resource scheduling theory, the study selects panel data from China's A-share market for"specialized, refined, distinctive, and innovative"enterprises during the period of 2011 to 2024 as the research sample.This paper empirically investigates the impact and underlying mechanisms through which data elements influe-nce enterprise innovation resilience.The findings indicate that data elements significantly enhance the innovation resilience of"specialized, refined, distinctive, and innovative"enterprises, primarily via four channels: increasing R&D investment, alleviating financing constraints, reducing principal-agent costs, and optimizing human capital structure.Moreover, data elements exhibit a more pronounced positive effect on the innovation resilience of non-high-tech enterprises, large-scale enterprises, and labor-intensive industries.This research offers a theoretical foundation and practical insights for"specialized, refined, distinctive, and innovative"enterprises to foster their innovation resilience by leveraging data elements within the context of digital transformation.

Digital green transformation is the core goal and important focus of improving new quality productivity, and its coo-rdinated development and deep integration provide continuous momentum for economic growth. This paper systematically analyzes the conceptual mechanism of digital green collaborative transformation, creatively distinguishes the transformation and development of the two into four categories: the green transformation of the digital industry itself, the digital transformation of the new energy industry in the traditional industry, the digital green transformation of the traditional energy industry and the digital and green collaborative transformation of the traditional industry in general. Enrich the theoretical connotation of digital green collaborative transformation. The study finds that digital greening focuses on manufacturing, construction, information and communication, transportation and energy industries, promoting the transformation and upgrading of the manufacturing industry, promoting carbon reduction and efficiency of green buildings, accelerating the high-quality development of the information and communication industry and the transportation industry, and the "digital intelligence and dual carbon" industrial ecology has been initially formed. China's digital and green development promotes low-carbon transformation in key areas with breakthroughs in digital technology applications, but it also faces challenges such as unclear synergy mechanism paths, incomplete institutional guarantees, unbalanced synergy degrees, and insufficient support capabilities of various factors. To promote "dual coordination", we should focus on optimizing the allocation of resources in key industries, standardizing low-carbon standards and data use systems, promoting the deepening and deve-lopment of digital transformation in the whole society, and forming new quality productivity aggregation elements in all fields.

Venture capital is characterized by high risk, high returns, and a focus on innovative sectors.These attributes are closely aligned with the essence of new quality productivity, making the relationship between the two worthy of exploration.This paper empirically examines the impact and mechanisms of venture capital on enterprises' new quality productivity using data on venture capital activities from 2011 to 2023 and listed companies on the SME board, growth enterprise market, and STAR Market.The findings reveal that venture capital significantly enhances enterprises' new quality productivity.The mechanism analysis indicates that venture capital promotes the improvement of enterprises' new quality productivity by promoting breakthrough innovation in enterprises, alleviating enterprises' financing constraints and enhancing their risk-taking capabilities.Further analysis indicates that the positive effect of venture capital on new quality productivity is more pronounced for enterprises facing greater financing constraints.Additionally, non-state-owned enterprises, high-tech firms, technology-intensive industries, and enterprises in eastern regions exhibit stronger effects of venture capital in boosting new quality productivity.The conclusions of this study provide empirical evidence and policy references for China's strategic decisions to leverage venture capital in empowering technological innovation and advancing new quality productivity.

Based on the resource-based theory and information asymmetry theory, this paper uses Shanghai and Shenzhen A-share listed companies from 2012 to 2023 as samples to verify the relationship between digital transformation, green innovation, and high-quality enterprise development using a bidirectional fixed-effects model. The research results show that digital transformation has a significant positive impact on the high-quality development of enterprises, and digital transformation indirectly exerts a positive impact on high-quality enterprise development through green innovation. Both corporate social responsibility and enterprise risk-taking levels positively moderate the effect of digital transformation on the high-quality development of enterprises.

With China's economic development entering a new stage of improving quality and efficiency, cultivating and developing new quality productivity has become the key task of high-quality economic development at present.Patience capital is a practical requirement to guide the integration of innovative resources and factors and promote the formation of new quality productivity.Therefore, this paper focuses on how patient capital can empower the formation of new quality productivity, and analyzes its synergy in the transformation of innovation achievements, industrial structure optimization and talent gathering.Through empirical analysis, it is found that patient capital has a significant role in promoting the formation of new quality productivity, and patient capital can promote the formation of new quality productivity in areas with low economic development level, and its influence is mainly realized through three aspects: improving innovation level, rationalizing industrial structure and promoting talent gathering.Therefore, we should give full play to the guiding role of patient capital in technological innovation and structural transformation, promote the more stable and fair flow of capital elements, continuously release the potential of innovation, and form new quality productivity.

Private enterprises play a key role in cultivating new quality productivity and promoting high-quality economic development. This paper analyzes the practical difficulties and causes that private enterprises face in promoting new quality productivity, explores the mechanism through which reverse mixed-ownership reform broadens managers' strategic vision, explains its theoretical path for enhancing new quality productivity, and tests it using A-share private listed companies from 2010 to 2024 as a sample. The results indicate that: (1)reverse mixed-ownership reform can promote the development of new quality productivity in private enterprises by alleviating managerial short-termism; (2)economic policy uncertainty significantly weakens the positive effect of reverse mixed-ownership reform on new quality productivity; (3)the aforementioned effects are more pronounced in strategic emerging industries and technology-intensive sectors. From a managerial cognition perspective, this paper enriches research on the economic consequences of reverse mixed-ownership reform and provides theoretical support and practical guidance for private enterprises to cultivate new quality productivity through optimizing their equity structure.

Utilizing panel data from Chinese cities spanning 2011 to 2023, this article employs a multi-time point progressive difference-in-differences model to systematically investigate the impact of the national green data center construction pilot policy on the development of the new energy industry. The research findings are as follows: (1) The national green data center construction pilot policy significantly promotes the development of the new energy industry; (2) Mechanism analysis reveals that the establishment of national green data centers fosters the development of the new energy industry through green innovation cooperation and enhanced computing power support; (3) Heterogeneity analysis indicates that the construction of national green data centers has a more pronounced positive effect on the new energy industry within the Internet sector, central cities, and non-resource-based cities. This article addresses the critical question of whether the deployment of green computing power can effectively facilitate the advancement of the new energy industry, thereby providing empirical evidence to support the promotion of green computing power initiatives and the attainment of high-quality development in the new energy sector.

As a new core factor of production and strategic resource for enterprises, data assets are reshaping corporate value creation models and capital structures.This paper examines A-share listed companies in China from 2010 to 2023, analyzing the impact of corporate data capitalization on the cost of debt financing by quantifying the extent of enterprise data assetization.The study finds that data capitalization significantly reduces the cost of debt financing.Specifically, data assetization reduces debt financing costs not only through a defensive mechanism of lowering financial risk but also via a proactive mechanism of enhancing financial flexibility.The research conclusion not only provides empirical support for clarifying the reduction path and functional boundary of debt financing costs by data assetization, but also offers theoretical support and policy guidance for further developing specific data assets of enterprises.

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.

Based on the pilot policies of green financial reform and innovation pilot zones, this study uses data from listed companies between 2010 and 2023 and applies a difference-in-differences (DID)model to assess the impact and mechanism of the pilot policies on corporate carbon performance.Additionally, a multiple mediation model is used to clarify the theoretical boundaries of the Porter Hypothesis after the effect of the pilot policies.The study finds that companies in the pilot zones experience a significant improvement in carbon performance, with cost pressure and green innovation playing parallel mediating roles in the process through which the pilot policies impact corporate carbon performance.Moreover, the cost pressure brought by the pilot policies not only has a parallel mediating effect on corporate carbon performance but may also incentivize green innovation, playing a chain mediation role.Due to the difficulty of offsetting the"innovation compensation effect"with"compliance costs", the"Porter Hypothesis"in the pilot policies remains in the"compliance cost"phase.The heterogeneity analysis reveals that the pilot policies have a more significant impact on corporate carbon performance in regions with high levels of financial development and in heavily polluting enterprises.Additionally, companies with executives who have a strong green awareness are less affected by the green pilot policies.

The digital transformation of enterprises can help reduce market transaction costs, improve the level of specialization, and improve their Total Factor Productivity (TFP). An empirical analysis based on the data of A-share manufacturing listed companies in Shanghai and Shenzhen from 2010 to 2023 shows that: (1)The digital transformation of manufacturing enterprises can improve their TFP, which is still true after a series of robustness tests; (2)The specialization has a significant mediation effect and a double threshold effect, and when the level of specialization crosses the high threshold, the TFP improvement effect of digital transformation is greater, but this effect is not obvious when it is below the low threshold. (3)There is a significant spatial spillover effect of digital transformation on TFP, and the specialization has a significant spatial mediating effect. (4)Large enterprises, high-tech enterprises, enterprises in eastern China and enterprises in the National Big Data Comprehensive Pilot Zone have a greater increase in TFP from digital transformation. In order to enhance the TFP improvement effect of the digital transformation of the manufacturing industry, it is necessary to strengthen the policy support system and capacity building for the digital transformation of the manufacturing industry, and implement differentiated digital transformation strategies.

Enterprise digital transformation is an important link to implement the digital economy strategy and promote the integrated development of the digital economy and the real economy. This paper focuses on the transmission mode of government digital attention-enterprise digital transformation, and empirically tests the influence and mechanism of government digital attention on enterprise digital transformation. Based on the data of China's Shanghai and Shenzhen A-share listed companies from 2007 to 2023, the study found that the government's digital attention significantly promoted the digital transformation of enterprises, and the above effect was more obvious when state-owned enterprises, small-scale enterprises, and regional digital infrastructure were better. The effect of government digital attention on enterprise digital transformation mainly plays a role through two transmission paths of government and enterprise. On the one hand, government digital attention improves digital subsidies; on the other hand, it plays a role in the peer effect among enterprises, that is, the imitation learning behavior that promotes enterprise digital transformation. The research conclusions are of great significance for enriching relevant researches on enterprise digital transformation and exploring how to give full play to the effectiveness of policies to solve the dilemma of enterprise digital transformation.

Will the development and wide application of artificial intelligence bring a significant productivity dividend, or will it trigger a new Solow productivity paradox and Baumol's disease?This paper employs a systematic literature review method to conduct a comprehensive analysis of research on the productivity effects of artificial intelligence.The findings of this study are as follows: (1)There is no definitive conclusion regarding the relationship between those two.Studies that argue for no significant effects often consider the multidimensional adaptability of AI technology and the lag in productivity effects, while those that advocate for significant promoting effects tend to focus on AI's substitution for traditional labor and skill spillovers.(2)The mechanisms through which productivity effects influence labor and positions at high- and low-skill levels differ.(3)The impacts of AI on productivity exhibits heterogeneity due to factors such as firm size, ownership type, and the technological stage firms are at.(4)As an auxiliary tool, generative AI has already demonstrated significant promoting roles in certain industries, though there are differing views on its impacts on high-and low-skilled labor.Based on these findings, this paper suggests that future research should pay more attention to the impacts of specific AI technological segments on productivity, with particular emphasis on mechanisms and pathways, as well as the productivity effects of generative AI.

Energy structure transformation is vital for fostering harmony between humanity and nature and for promoting high- quality economic growth.This study evaluates the impact of the new energy demonstration city pilot policy on urban Green Total Factor Productivity (GTFP)using panel data from 279 Chinese prefecture-level cities from 2006 to 2024.A super-efficiency SBM-GML model is employed to measure GTFP, while a difference-in-differences (DID)approach with propensity score matching (PSM) identifies policy effects.Results show that the pilot policy significantly enhances urban GTFP.Mechanism analysis reveals that the effect operates mainly through substantive green technological innovation, with industrial structure optimization playing a positive moderating role.Heterogeneity analysis suggests stronger policy effects in central and western regions, non-resource-based cities, and cities with stricter environmental regulations.Based on these findings, the paper recommends: establishing an integra-ted policy framework covering institutional design, market incentives, and behavioral guidance; promoting green innovation and in- dustrial upgrading; and adopting region-specific, resource-based coordinated development strategies.

Corporate circular supply chain management has received increasing attention due to its high efficiency in recycling waste resources.As one of the critical stakeholders, foreign shareholders have a significant influence on the decision of corporate circular supply chain management.Based on stakeholder theory, this paper explores the impact of foreign shareholding on corporate circular supply chain management.Taking Chinese A-share listed companies from 2012 to 2023 as the research sample, the empirical test is conducted by using a panel data two-way fixed-effects model.The paper indicates that foreign shareholding has an inverted U-shaped effect on corporate circular supply chain management.The paper also indicates that in enterprises with higher supply chain concentration, the inverted U-shaped relationship of foreign shareholding on the corporate circular supply chain ma-nagement is flatter or even produces the phenomenon of shape reversal due to the supply chain rigidity effect.In enterprises with hig- her board of directors' shareholding, the inverted U-shaped relationship of foreign shareholding on circular supply chain management is steeper due to the shareholder synergy effect.This paper not only expands the application field of stakeholder theory and enriches the antecedent research on circular supply chain management but also provides a theoretical basis for the host government to formulate reasonable foreign investment introduction policies and corporate governance decisions of enterprises.

The widespread application of artificial intelligence has had a profound impact on the economy and society. This paper analyzes the theoretical mechanisms through which artificial intelligence(AI) promotes high-quality development of enterprises and conducts empirical tests based on panel data of listed companies on the Shanghai and Shenzhen stock exchanges. Empirical results show that artificial intelligence significantly promotes the high-quality development of enterprises, and remains significant after robustness tests and causal identification considering instrumental variables. Mechanism tests show that artificial intelligence can promote the high-quality development of enterprises through ways such as promoting technological innovation, reducing costs, enhancing the level of human capital, and promoting common prosperity within enterprises. However, it has an inhibitory effect on the technological diversification of enterprises. Moreover, the impact of AI on high-quality enterprise development varies under different circumstances, including different firm sizes, different labor costs, and different ownership structures. The research conclusion provides a feasible path for the high-quality development of enterprises and also offers a basis for the government to promote the deep integration of artificial intelligence and the real economy.

With the advancement of the digital economy, big data technology has emerged as a critical component of national development competitiveness.This study examines Shanghai and Shenzhen A-share listed companies in China from 2012 to 2023.Grounded in Resource Dependency Theory and Innovation Ecosystem Theory, we construct a theoretical framework analyzing how the national big data comprehensive pilot zone policy influences corporate open innovation.Using a difference-in-differences (DID)approach, we empirically identify the net policy effect on enterprise open innovation.The study found that the establishment of national big data comprehensive pilot zones significantly promotes corporate open innovation, with enhanced effects observed in state-owned enterprises, eastern-region enterprises, and labor-and technology-intensive enterprises; transmission mechanisms analysis indicates that the policy facilitates open innovation through three channels: driving corporate digital transformation, alleviating financing constraints, and reducing information asymmetry.Therefore, this article proposes a strategic path for deepening the develo-pment of big data and promoting collaborative open innovation in enterprises.

Based on the quasi-natural experiment of the"Low Carbon City Pilot"policy, this study identifies the effect of low-carbon city construction on the development of new quality productivity by using the multistage dynamic difference-in-differe-nces model with a sample of 263 prefectural-level and above cities in China, and further analyzes its intrinsic mechanism and spatial effect.The results show that: (1)low-carbon city construction can significantly promote the development of new quality productivity, and the reliability of this conclusion is verified by a series of robustness tests.(2)There is heterogeneity in the effects of low-carbon city construction on the development of new quality productivity in terms of city location, resource endowment and carbon emission intensity, in which low-carbon city construction can significantly promote the development of new quality productivity in cities in the east and west, non-resource cities and high-carbon emission cities.(3)Low-carbon city construction can promote the development of new quality productivity through three paths: promoting green technological innovation, enhancing the level of industrial agglomeration, and improving the development level of green finance, and fiscal decentralization has a significant positive moderating effect in promoting the development of new quality productivity in low-carbon city construction.(4)Low-carbon city construction has obvious spatial spillover effects on the development of new quality productivity, and it has significant promotion effects on the development of new quality productivity in pilot cities and neighboring cities.The conclusions of this study provide guidance for strengthening the construction of low-carbon cities and broadening the path of urban new quality productivity development.

Accurate prediction of carbon prices can provide quantitative support and reference basis for climate policy formulation, rational decision-making of investors, and maintaining the stable operation of the carbon market.This article proposes a novel hybrid machine learning Mod-EMD-BiLSTM prediction model that combines data augmentation, empirical mode decomposition, and bidirectional long short-term memory techniques.Specifically, the original carbon price series is first subjected to empirical mode decomposition to obtain a series of relatively stable and low noise intrinsic mode components(IMF).Secondly, introduce data augmentation techniques to enhance data recombination and randomly generate half of the IMF combinations.Furthermore, based on the two parallel mechanisms of prevention and prediction in this model, the IMF composite components are further preprocessed and model training is carried out.Finally, the output values of the two frameworks are integrated through the fully connected layer of the BiLSTM neural network to obtain the final carbon price prediction results.On the basis of establishing a prediction model, empirical research is conducted by crawling the daily closing prices of carbon trading in Hubei's carbon trading market from 2014 to 2024.The results show that the model established in this article exhibits the best direction prediction accuracy compared to the other 16 benchmark models, reflecting the superior prediction performance and good practicality of the model.

Government green guidance funds serve as policy-oriented financial instruments designed to channel capital into green development, and play an important role in advancing the comprehensive green transformation of the economy and society.Based on a sample of China's A-share listed firms from 2010 to 2024, this study employs a multi-period difference-in-differences (DID)model to empirically examine the impact and underlying mechanisms of government green guidance funds on corporate ESG performance.The findings indicate that government green guidance funds significantly enhance corporate ESG performance.This effect is primarily achieved by alleviating financing constraints, strengthening external governance and reducing agency costs.Further analysis shows that the positive effect of government green guidance funds on corporate ESG performance is more pronounced in large firms and in firms located in regions with a lower degree of marketization.The funds exert a significant positive effect on all three dimensions—environmental, social, and governance performance.Additionally, the impact of government green guidance funds on corporate ESG performance exhibits a negative spillover effect among peer firms within the same industry, but no such spillover effect is observed among firms within the same region.This study provides important empirical evidence for improving China's green financial system and promoting corporate green and low-carbon transformation.

Public data opening is a necessary condition for the high-quality development of the digital economy and an important driving force for improving energy utilization efficiency and achieving green and low-carbon economic development.This paper uses the launch of public data open platforms in Chinese cities as a quasi-natural experiment and empirically analyzes city-level panel data from 280 cities from 2006 to 2023.A multi-period difference-in-differences (DID)model is employed to examine whether public data opening can improve urban energy utilization efficiency.The study finds that public data opening significantly enhances urban energy utilization efficiency, and this conclusion remains robust after a series of robustness and endogeneity tests.Mechanism analysis shows that public data opening can improve energy utilization efficiency by promoting urban green technology innovation and optimizing the energy consumption structure.Heterogeneity analysis indicates that the policy has a more pronounced effect on improving energy utilization efficiency in non-resource-based cities, cities with high levels of digital economy development, and cities with strong innovation and entrepreneurship capabilities.The findings have important implications for relevant departments in formulating public data policies, promoting green technology innovation, and optimizing energy consumption structures to enhance urban energy efficiency.

By developing an analytical framework that distinguishes between geographic proximity and economic proximity, this study provides a theoretical explanation and empirical examination of the local and spillover effects of artificial intelligence on regional productivity, based on panel data from prefecture-level cities in China between 2014 and 2024.The findings show that AI directly boosts productivity in the local region and generates positive spatial spillovers through geographic proximity, yet it produces a polarization effect through economic linkages.In light of this, during the 15th Five-Year Plan period, policy efforts should focus on building differentiated digital infrastructure that strengthens geographic synergies, implementing targeted empowerment strategies that balance efficiency and fairness, and developing a collaborative governance framework suited to the networked characteristics of modern economic development.These measures are intended to guide the spatial distribution of AI dividends toward more inclusive and widespread benefits, helping shape a regional economic landscape defined by complementary strengths and high-quality development.

Against the backdrop of deepening reforms and accelerating the cultivation of new quality productivity, enhancing the resilience of industrial and supply chains has become a critical pathway to achieving high-quality economic development.This study employs balanced panel data from 285 prefecture-level cities in China from 2009 to 2023 to empirically examine the spatial spillover effects of new quality productivity on regional industrial and supply chain resilience, as well as the underlying mechanisms.The findings reveal that improvements in new quality productivity significantly enhance resilience and exhibit notable spatial spillovers, primarily through the alleviation of regional labor and capital misallocation.Heterogeneity analysis further indicates that non-resource-based, non-transport hub, and small to medium-sized cities benefit more prominently.These results offer important policy implications for place-based reform strategies, optimized resource allocation, and the cultivation of new growth drivers, thereby contributing to regional balanced development and stronger industrial and supply chain resilience.

Digital new quality productivity can assist in the transformation of industrial production methods, activate the potential of factors, and promote industrial integration, which is of great significance for the development of green total factor productivity. This article is based on panel data from 30 provinces (regions, municipalities) in China from 2012 to 2023. Using visuali-zation methods, it explores the spatiotemporal evolution characteristics of the development level of digital new quality productivity in China, and empirically examines the impact and mechanism of digital new quality productivity on regional green total factor productivity. Research has found that digital new quality productivity can significantly promote the development of green total factor productivity, with the role of digital workers being the most significant. In addition, the promotion effect of digital new quality productivity on green total factor productivity is more significant in the eastern region, regions with high levels of green total factor productivity development, and regions with high policy intensity. Mechanism analysis shows that optimizing industrial structure and enhancing green technology innovation are effective paths for promoting the development of green total factor productivity through digital new quality productivity. Therefore, this paper suggests that efforts should be made to cultivate digital new quality productivity, actively promote industrial structure upgrading and enterprise green technology innovation, combine regional characteristics, adapt to local conditions, and implement precise policies.

The core connotation of new quality productivity are "new" and "quality". The characterization of "new" can be systematically constructed through three dimensions: production factor innovation, technological revolutionary breakthroughs, and profound industrial transformation. The leap in "quality" can be effectively captured using the hyperbolic entropy weight TOPSIS method, which emphasizes its progressive and nonlinear characteristics. Methods such as the Dagum Gini coefficient, Kernel density estimation, Moran's index, and Markov chains are used to deeply analyze the sources of regional disparities, dynamic evolution trends, and spatial spillover effects of new-quality productivity. The study reveals that between 2012 and 2023, China's new quality productivity level showed an overall upward trend, but regional development was notably uneven, with the leap in productivity further widening regional disparities and internal differences. The analysis of spatiotemporal evolution highlights a dynamic coexi-stence of "differentiated improvement" and "internal polarization" across regions, with spatial externalities demonstrating a complex mechanism of "low lock-in, high promotion, and long-distance enhancement" during productivity transitions.

From the chained incubation perspective, this study utilizes panel data from China's 31 provinces and municipalities from 2015 to 2024 and employs Double Machine Learning (DML)to examine how large model technology impacts AI industry innovation.Results show significant positive total effects, with 31.12% transmitted through"technology→ecosystem→innovation"pathways.Blocking any link reduces the indirect effect to zero, confirming chained incubation necessity.The ecosystem acts as the core intermediary, exhibiting strong technological drive and weak ecological transformation characteristics, with the conversion efficiency from ecosystem to innovation being the key bottleneck.Further analysis indicates that large model technology and the industrial ecosystem exhibit non-linear synergistic effects, with the marginal effect of technology on innovation increasing as the ecosystem matures.In regions with low ecosystem maturity, technology has already become the core engine driving innovation indepen-dently.Regional heterogeneity is significant: in the eastern region, the chain-based transmission pathway is intact, with indirect effects accounting for 29.61%;in the central region, transmission fails due to the absence of an ecosystem intermediary; the wes-tern region exhibits negative effects due to technical-ecological mismatches; and the northeastern region lacks established collaborative mechanisms.Based on these findings, the study proposes recommendations to address the bottleneck in ecological-to-innovation conversion, including establishing a linked infrastructure of computing power, incubation, and open-source collaboration, implementing tiered regional policies, and mitigating risks of ecological monopolization, to enhance chain incubation mechanisms and drive the innovative development of the AI industry.

In the context of deep integration between the digital and real economies, whether data assetization can help enhance supply chain resilience has become an urgent question to address. Based on data from A-share listed companies from 2007 to 2024, this study empirically examines the impact of corporate data assetization on supply chain resilience. The findings indicate that: (1) Corporate data assetization significantly improves supply chain resilience, and the results remain robust after a series of endogeneity treatments and robustness tests. (2) Data assetization primarily enhances supply chain resilience by increasing corporate information transparency, reducing supply chain coordination costs, and improving product competitiveness. Furthermore, information transparency and coordination costs play a chained mediating role in this process. (3) The empowering effect of digitalization is more pronounced in technology-intensive enterprises, competitive industries, and in the eastern and central regions of China. This study provides evidence and insights from the perspective of data assetization on how to enhance supply chain resilience under the Digital China strategy.

Based on the panel data of Chinese cities from 2010 to 2024, this paper regards the establishment of the National Big Data Comprehensive Pilot Zone(hereinafter referred to as the Big Data Comprehensive Pilot Zone)as a quasi-natural experiment and uses the difference-in-differences model to evaluate the impact of big data policies on digital-real integration.The results indicate that the Big Data Comprehensive Pilot Zone has significantly improved the level of digital-real integration, and this conclusion remains robust after a series of robustness tests.Mechanism tests show that the Pilot Zone mainly facilitates the development of digital-real integration by driving digital technology innovation and promoting the agglomeration of digital industries.In addition, the promoting effect of big data policies on digital-real integration is heterogeneous across the types of Pilot Zones, the geographical locations of cities, and urban resource endowments.The research conclusions help deepen the understanding of the role of big data policies represented by the establishment of Big Data Comprehensive Pilot Zones in promoting high-quality economic development, and also provide theoretical support and policy references for different regions to formulate digital-real integration development strategies based on local conditions.

This paper constructs an evaluation index system for the modernization of low-altitude economy development, covering industrial development, regional development, and social and environmental factors. Using an improved entropy-weight TOPSIS method, the low-altitude economy modernization development level of 30 provinces in China is measured. The results show that: (1) there are significant regional differences in the modernization development level of low-altitude economy in China, and these differences mainly stem from the industrial chain scale, market demand, and social impact. (2) There is a clear spatial agglomeration effect, with stable development in the eastern regions, and larger disparities in the central, western, and northeastern regions, reflecting regional development imbalance. (3) The modernization level of low-altitude economy exhibits strong path dependence, with higher-level regions showing greater stability, while lower-level regions rely on the radiation effect of higher-level regions, demonstrating strong upward potential.

China attaches great importance to hard science and technology innovation, taking it as the core driving force of national development, improving the international discourse power through innovation-driven strategy and major science and technology projects, solving the problem of "bottleneck" of key technologies and promoting high-quality economic development. Based on the data of 1037 specialized and new enterprises, this paper constructs a theoretical model of "hard science and technology innovation-dual legitimacy-high-quality development" to explore the regulatory role of digital transformation. It is found that hard technology innovation directly promotes the high-quality development of enterprises, and at the same time has an indirect effect by improving adaptation legitimacy and strategic legitimacy, and digital transformation can strengthen this positive relationship. The research provides theoretical support for the government to optimize innovation policies and enterprises to formulate hard science and technology development strategies, and helps specialized and special new enterprises to achieve qualitative leaps through hard scie-nce and technology innovation.

Market demand serves as a powerful internal driver for fostering new quality productivity. Therefore, it is crucial to unlock the potential of the robust domestic market to enhance both the scale and quality of production, effectively nurturing and advancing new quality productivity. Using provincial panel data from 30 provinces between 2012 and 2023, this paper measures the level of new quality productivity across these regions and empirically examines the role and mechanism of market potential in cultivating it. The findings indicate that enhancing market potential significantly boosts the development of new quality productivity by expanding the quantity and upgrading the quality of production. The heterogeneity analysis results indicate that in the eastern regions, densely populated areas, regions with high degree of marketization, and areas along the "Belt and Road", the promotional effect of enhanced market potential on the development of new quality productivity is more pronounced. Based on these insights, this paper offers several policy recommendations, including deeply tapping into market potential, stimulating equipment renewal and investment demand through production expansion, and driving innovation and technological empowerment through quality upgrades in production.

In response to the challenges posed by digital service trade to traditional international trade theory, this paper break through the analytical paradigm of technology-element segmentation, construct a theoretical framework of"technology-institutional synergy", innovatively integrate the width of the technology niche and the complementarity of technology combinations, and design a technology specialization index to analyze the new Leontief mystery.Empirical tests based on WTO cross-border panel data from 2008 to 2024 find: there is a significant negative relationship between technology specialization and digital service trade, confirming the existence of the new mystery; intellectual property protection shows heterogeneity in regulating the technology path, with the technology-institutional synergy evolution forming a quaternary form, namely, balanced synergy-type countries achieve dyna- mic adaptation of technology diversity-institutional flexibility, technology-led types face path locking risks, institution-led types encounter rule deadlocks, and inefficient synergy types show systemic failure characteristics.The study indicates that the essence of the competition in digital service trade is the synergy efficiency competition of technological capability and institutional adaptation, and solving the new mystery requires establishing a governance framework of"technology-institutional fit".

As a core link in modern industrial economic management, industrial risk prediction plays an irreplaceable role in ensuring coordinated economic development, optimizing industrial structure and scientifically formulating industrial development poli- cies.This paper proposes an innovative solution-Mixed-Frequency Temporal Fusion Dual Attention Network (MF-TF-DAN).What is particularly critical is that the MF-TF-DAN model innovatively introduces a dual attention mechanism, which starts from the two dimensions of time and features to deeply mine and evaluate the importance of the information processed by GRU and CNN. This paper conducted comprehensive and in-depth experimental verification on the industrial risk data set, including model compari- son experiments and ablation experiments with different prediction step sizes.Experimental results show that the MF-TF-DAN model performs significantly better than other comparison models in the mixing data prediction task.This result not only proves the scientific and effectiveness of the model design, but also brings new breakthroughs in the field of industrial risk prediction.This model provides industry managers with unprecedented accurate risk warning capabilities, allowing managers to have a deeper insight into market changes, identify and evaluate potential risks in advance, and thereby formulate more scientific and reasonable corporate strategies and market response strategies.

The intelligent economy is a new economic form based on intelligent technology.Through the deep integration of artificial intelligence and various industrial fields, the intelligent economy has played an engine role in high-quality economic develo-pment.With artificial intelligence technology as the core driving force, the intelligent economy has formed a mechanism of action from five dimensions: technology drive, factor reconstruction, industrial transformation, value creation, and system adaptation, through reconstructing production factors, changing labor methods, optimizing industrial structure, and adapting institutional systems.Therefore, it is necessary to formulate a clear-oriented and well-established policy system based on the underlying logic of artificial intelligence and the efficiency of the intelligent economy.