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.

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.

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.

The digital economy is a key driving force for the development of new quality productivity. This study constructs an evaluation index system based on two dimensions: "technological progress" and "factor upgrading". It empirically measures the development level of new quality productivity in 30 provinces(regions, municipalities)of China from 2012 to 2023, and explores the impact and mechanism of the digital economy on the development of new quality productivity. The results show that the level of new quality productivity in China has continuously improved, but significant regional disparities exist. The digital economy significantly promotes the development of new quality productivity, with the conclusion holding even after addressing endogeneity and conducting robustness checks. Additionally, the promoting effect follows a geographic gradient: "East > West > Northeast > Central". The digital economy drives the development of new quality productivity through structural optimization, technological innovation, and resource allocation effects. Furthermore, it exhibits a spatial spillover effect, promoting development within a region while exerting a suppressive effect on neighboring regions.

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.

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.

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.

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.

Based on the panel data of China's Shanghai and Shenzhen A-share listed companies in 2014~2023, this paper constructs a chain multiple mediating effect model to empirically test the heterogeneous impact of financial subsidies and tax incentives on the new quality productivity of enterprises and its action mechanism.The research shows that financial subsidies and tax incentives can significantly promote the development of new quality productivity of enterprises, and the conclusion is still established after the robustness test and endogenous analysis.Mechanism analysis shows that financing constraints and risk assumption not only play an independent intermediary role, but also play a chain intermediary role between financial subsidies, tax incentives and the new quality productivity of enterprises.The heterogeneity analysis shows that the fiscal subsidy and tax preferential policies have a more significant marginal promotion effect on the new quality productivity of non-state-owned enterprises and enterprises located in central cities.

ESG, which conforms to the new concept of green and sustainable development, has been paid more and more attention by the government, investment institutions and enterprise managers. How to improve enterprise ESG performance and internalize the externalities of enterprise ESG practice, the National Digital Economy Innovation and Development Pilot Zone gives new ideas. In order to avoid the default bias, dimensional curse and strong model assumption of the traditional progressive DID model, and fully consider the nonlinear relationship between variables, this paper uses the data of listed enterprises from 2006 to 2023 and uses a dual machine learning model to evaluate the impact of the establishment of "National Digital Economy Innovation and Deve-lopment Pilot Zone" on the ESG performance of enterprises and its mechanism. The research results show that the establishment of the "National Digital Economy Innovation and Development Pilot Zone" has significantly improved the ESG performance of enterprises, and has a significant promotion effect on the E performance, S performance and G performance of enterprises, respectively. The conclusion is still valid after resetting the machine learning model, replacing the explained variables and regression methods of instrumental variables. The establishment of the "National Digital Economy Innovation and Development Pilot Zone" mainly improves the ESG performance of enterprises by promoting technological upgrading and digital transformation of enterprises, and the improvement effect is more significant in the high degree of new quality productivity development and state-owned enterprises. This paper further puts forward policy suggestions such as vigorously promoting the construction of the national digital economy innovation and development pilot zone, and promoting the ESG performance of enterprises by relying on the development of new technologies such as 5G, big data and cloud computing.

In an era marked by unprecedented global transformation, enhancing enterprise resilience amidst environmental uncertainties has become a critical concern for policymakers, particularly the Central Committee of the Communist Party of China. This study employs data from Chinese listed companies spanning 2010 to 2023. This research investigates the impact of the technology convergence of real and digital economy on enterprise resilience and its underlying mechanisms. The findings indicate that: (1) The technology convergence of real and digital economy significantly enhances enterprise resilience through the upgrading of labor skills and wages, dismantling industry barriers, and decreasing customer concentration. (2) Environmental uncertainty is found to positively influence the relationship between the technology convergence of real and digital economy and enterprise resilience. (3) The positive effects of the technology convergence of real and digital economy on enterprise resilience are more pronounced in non-dual-role enterprises, enterprises with low financing constraints, and profitable enterprises. This paper offers a theoretical foundation and policy insights for facilitating the integration of real technology and enhancing the resilience 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.

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.

Based on the industrial robot data of IFR database and the manufacturing value chain data of UIBE-GVC from 2008 to 2019, this paper empirically tests the impact of artificial intelligence on the resilience of manufacturing value chain and the moderating effect of intellectual property protection.The results show that artificial intelligence significantly improves the value chain resilience of manufacturing industry, which is more significant in the samples of economically developed countries, downstream of value chain division and capital-intensive industries."Promoting technological innovation"and"saving trade costs"are important channels for artificial intelligence to influence the resilience of the manufacturing value chain.The impact of artificial intelligence on the resilience of manufacturing value chain is regulated by intellectual property protection.After crossing the threshold of intellectual property protection, the promotion effect of artificial intelligence on intellectual property protection is significantly enhanced.This paper explores the channels and boundary conditions of artificial intelligence affecting the value chain resilience of manufacturing industry, and provides some empirical enlightenment for fully releasing the enabling role of artificial intelligence to ensure the value chain resilience and safety.

Open government data is a crucial measure in advancing the Digital China strategy and the construction of a digital government. Based on panel data of 284 cities in China, taking the launch of open government data platforms as a quasi natural experiment, this paper builds a difference-in-differences model to evaluate the impact of open government data on urban innovation. The research finds that, first, open government data has a significant effect on the level of urban innovation. Second, mechanism analysis suggests that open government data promote urban innovation through three aspects: attracting scientific and technology ta-lents to enrich the supply of innovation resources, inspiring entrepreneurial vitality to expand the scope of innovation subjects, and optimizing business environment to improve innovation environment. Third, heterogeneity analysis shows that the digital policy environment, digital financial environment, and digital infrastructure strengthen the role of open government data in promoting urban innovation. Fourth, expansion analysis shows that open data quality and platform construction quality have a positive impact on urban innovation. Based on this, the government should actively build open government data platform, pay attention to the heterogeneous impact of open government data on different regions, in order to better unleash the dividends of open sharing of data elements.

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.

As a new high ground for reform and opening up in the new era, the establishment of comprehensive pilot zones for cross-border e-commerce provides a favorable environment and conditions for the development of new quality productivity.Based on the data of 278 prefecture-level cities from 2010 to 2022, this paper explores the impact of the pilot policy of comprehensive cross-border e-commerce pilot zones on the level of new-quality productivity forces using a multi-period DID model.The results of the study show that the construction of comprehensive cross-border e-commerce pilot zones can significantly enhance the level of new quality productivity, and the effect has a certain degree of continuity.The results of the mechanism test show that the construction of the comprehensive cross-border e-commerce pilot zone drives the new-quality productivity forces level mainly through three channels, namely strengthening regional innovation accumulation, optimizing resource allocation, and improving the level of industrial agglomeration.Heterogeneity analyses show that the policy's promotional effect on the level of new quality productivity is more pronounced in the sample of cities within the five major urban agglomerations, with a higher degree of market integration and a favorable business environment.

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.

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.

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.

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.

In the era of digital economy, virtual agglomeration based on the network has become a new form of industrial spatial organization.Its interaction and coupling with traditional geographical spatial agglomeration are deepening increasingly, which plays an important role in expanding the agglomeration effect of industries and enhancing regional innovation capabilities.Based on the perspective of knowledge flow, this paper conducts an empirical test using the panel data of 30 provinces, regions and municipalities in China from 2008 to 2022.The results show that the interactive coupling of virtual agglomeration and geographic agglomeration can significantly improve regional innovation ability, and knowledge flow plays a part of mediating role.Heterogeneity analysis shows that the interactive coupling of virtual agglomeration and geographic agglomeration has a significant regional difference on regional innovation ability, which is significantly positive in eastern region, positive but not significant in central region, and significantly negative in western region.Therefore, it is proposed to accelerate the layout of new digital infrastructure, promote the interaction between virtual agglomeration and geographic agglomeration, promote knowledge flow by relying on industrial internet, industrial innovation alliance, flexible talent introduction and other measures, constantly release the overall efficiency of regional innovation system, and enhance regional innovation capacity.

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.

In the context of intensifying global climate change and the growing urgency of sustainable development, green finance has emerged as a crucial tool for supporting sustainability, with its policy effects garnering significant attention.This study aims to investigate how green finance policies influence enterprises' sustainable development capabilities, particularly their new quality productivity.Grounded in institutional theory and social capital theory, we examine the impact of green finance reform and innovation pilot zone policies using a difference-in-differences model.Our analysis of panel data from Chinese A-share listed companies from 2012 to 2023 reveals that these policies significantly promote the development of enterprises' new quality productivity.This effect is primarily achieved through enhancing management's environmental awareness and strengthening enterprises' centrality in industry networks.Furthermore, we find that patient capital, as a long-term oriented investment strategy, significantly reinforces the positive impact of green finance policies, environmental awareness, and network centrality on new quality productivity.These findings not only enrich the cross-disciplinary research on green finance and corporate innovation but also deepen our understanding of long-term investment's role in corporate sustainable development.The study concludes by offering practical guidance for policymakers and corporate leaders in advancing green finance innovation and promoting sustainable corporate development.

Based on financial data from non-financial companies listed on the main boards of the Shanghai and Shenzhen stock exchanges from 2011 to 2023, this paper examines the impact of science and technology finance on new quality productivity.The findings indicate that optimized tech-financial services significantly enhance productivity and total factor productivity, with results remaining robust.Mediation analysis reveals that tech finance boosts R & D investment through improved funding and reduces costs via better resource allocation, promoting low-carbon transformation.Heterogeneity tests show that these effects are more potent in eastern and central regions, strategic emerging industries, private firms and non-Big 4 audited companies, especially when accompanied by high analyst coverage, strong investor attention and generous regional subsidies.This study provides micro-level evidence supporting the role of science and technology finance in advancing technological self-reliance and fostering a virtuous cycle among technology, industry and finance.

Industrial transformation is a natural part of the new round of industrial revolution, and the arrival of the artificial intelligence era has transformed the industrial productivity innovation in the sense of "Internet of Things" into a new industrial revolution of "data+algorithm+sensor+behavior tokenization+control system". Artificial intelligence has transformed industry from the meaning of the Internet of Things to the embodied AI meaning of "AlphGo+OpenAI", putting the era of intelligent industry on the eve of Grokking. On the one hand, AI deeply accelerates industrial transformation, and on the other hand, it directly reconstructs into an intelligent industrial system, thereby directly achieving the original goal of industrial transformation. However, at present, the shortcomings of energy and the old industrial production system urgently need to be addressed through industrial transformation, otherwise the era of intelligence will be difficult to truly achieve. At the same time, there are a large number of technical and hardware difficulties that slow down the marketization of intelligent industry. Without considering costs, it is possible to first achieve "laboratory intelligent industry" and then gradually realize it in real industrial application scenarios over the long term.

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.

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.

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.

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.

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.

Digital infrastructure, as a core pillar of the development of the digital economy, is profoundly reshaping the pathways for corporate sustainable development. Based on data from listed companies on the Shanghai and Shenzhen A-share markets between 2014 and 2022, this study constructs a digital infrastructure index to evaluate its impact on corporate sustainable development performance. The results demonstrate that the construction of digital infrastructure significantly enhances corporate sustainable development performance, a conclusion that holds true under a series of robustness tests. Heterogeneity tests reveal that the benefits of digital infrastructure development are more pronounced in the eastern regions, the manufacturing industry, and large-scale enterprises. Further analysis shows that digital infrastructure improves corporate sustainable development performance through three main channels: alleviating financing constraints, enhancing innovation input and output, and improving internal control quali-ty. Additionally, the increased public attention to environmental issues further strengthens the positive effects of digital infrastructure. This study uncovers the multi-dimensional mechanisms through which digital infrastructure enhances corporate sustainable development performance, providing empirical evidence for companies to seize opportunities in the digital economy and achieve sustainability goals.

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.

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.

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.

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.

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 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.