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.

Using industrial panel data from China covering 2010~2024, this study empirically investigates the impact of public data openness on corporate green transformation, with attention to regional heterogeneity and underlying mechanisms.The results show that: (1)Public data openness significantly promotes corporate green transformation, with effects increasing over time.And robustness tests employing diverse methodologies were conducted.(2)The green-enabling effect of public data openness exhibits multidimensional heterogeneity. At the regional level, there is a significant"digital divide"in its impact, meaning that the policy effect in the eastern region is significantly better than that in the central and western regions. At the data attribute level, the driving effect of high-quality public data is more pronounced, highlighting the policy value of data quality optimization. At the enterprise characteristic level, the policy effect is significant in low-pollution enterprises but relatively limited in high-pollution enterprises.(3)Mechanism analyses indicate that public data openness indirectly facilitates corporate green transformation by enhancing information transparency, accelerating digital transformation, and improving aggregate productivity.This study offers timely and actionable policy insights for governments aiming to design more effective public data disclosure strategies.

Under the new development pattern, government data governance is an important measure to realize the transformation of government governance from regulatory to service-oriented with the help of data elements.Whether it can release the value creation effect, stimulate high-tech entrepreneurship and promote high-quality development is a major issue of common concern in both practical and theoretical circles.Based on the quasi-natural experiment set up by the Big Data Administration, a multi-stage different-difference model is constructed to test the impact of government data governance on high-tech entrepreneurship.The study found that the establishment of a big Data authority can significantly promote high-tech entrepreneurship by reducing the perception of policy uncertainty for entrepreneurs and improving the digital development environment.Further analysis shows that data gover-nance can reduce the entry threshold of high-tech entrepreneurship, and in the regions with high human capital, data governance has a greater role in promoting high-tech entrepreneurship.In areas where public data is not open, the establishment of big data bureaus is more conducive to promoting high-tech entrepreneurship, indicating that government data governance can optimize the business environment by improving policy transparency.This study not only provides empirical support for in-depth understanding of the theoretical significance and practical value of data, but also has important policy implications for further improving data and promoting high-quality economic development.

Digital capital has increasingly become a key productive factor for assessing firm competitiveness and promoting sustainable development, while the expansion of high-speed rail(HSR)networks provides important external support for its accumulation.Using panel data on A-share listed firms from 2007 to 2024, this study applies a multi-period difference-in-differences(DID)approach to examine the effects of HSR network development on firms' digital capital and the underlying mechanisms.The empirical results indicate that improved urban HSR accessibility significantly promotes digital capital accumulation, and this finding remains robust across a series of robustness checks.Mechanism analyses identify two main channels through which HSR networks enhance digital capital: a talent agglomeration effect, whereby HSR facilitates the attraction and retention of technical and R&D personnel, and an environmental effect, whereby HSR improves energy consumption efficiency and environmental performance.Moreover, these two channels exhibit a complementary relationship, jointly reinforcing firms' digital capital accumulation.Heterogeneity analyses further reveal that the positive effects of HSR networks are more pronounced for firms located in larger cities, ope-rating in high-technology industries, and exhibiting stronger digital leadership.

"Empowering Entities with Data"drives industrial innovation and transformation and boosts the development of the real economy.Based on a research sample of Chinese A-share listed enterprises from 2010 to 2024, this paper empirically exa-mines the impact of data assets on enterprises' real business activities.The results show that data assets can significantly promote enterprises' real business activities, which is achieved through the channels of enhancing innovation awareness, improving production and operational efficiency, and boosting supply chain synergy effects.Moreover, this impact is more pronounced against the backdrop of low resource allocation efficiency, inferior business environment, and weak market competition intensity.In addition, the synergistic effect between government support and supervision and data assets not only facilitates the development of enterprises' real businesses, but also significantly enhances enterprises' sustainable development capability and corporate value.This study provides empirical evidence for enterprises to improve their data asset systems, promote the development of the real economy, and advance industrial structure upgrading.

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.

Developing a state-owned asset supervision system aligned with new productive capacities is vital for deepening SOE reform and stimulating endogenous growth. Using the 2019 launch of the online supervision system for state-owned assets as a quasi-natural experiment, this study analyzes data from A-share listed SOEs during 2011~2024 and employs a difference-in-differences (DID)approach to assess the impact of regulatory cloud adoption on new productive capacities. Results show that regulatory cloud adoption significantly enhances SOEs' new productive capacities, with findings robust to multiple checks. Mechanism analysis indicates that the effect operates mainly through supervisory governance and digital enablement. Heterogeneity analysis further reveals stronger effects for commercially competitive SOEs, digitally advanced firms, and SOEs in less-developed digital economy regions. The study provides micro-level evidence on how digitalized regulation fosters new productive capacities and offers policy implications for optimizing state-owned asset supervision and promoting high-quality SOE development.

We examines the impact of tariff increase on Chinese firms' innovation investment and its path of function based on the data of A-share listed companies from 2010 to 2024, taking the tariff increase of the United States on China in 2018 as an exogenous shock. We find that increased tariff imposed by the United States on China significantly push Chinese firms to increase their investment in innovation through four channels: government subsidy, tax break, market competition and bank credit. The contribution of this research is to systematically reveal the push mechanism of tariff increase on firms' innovation, firstly analyses its transmission logic from government subsidy, tax, market competition and bank credit, as well as theoretical references for policy design to cope with international trade conflict.

Using panel data from Chinese cities over the period 2010~2024, this study treats the National Big Data Comprehensive Pilot Zone policy as a quasi-natural experiment and employs a Double Machine Learning (DML)framework to empirically examine the impact of digital policy on urban green technological innovation. The results show that the implementation of the Natio-nal Big Data Comprehensive Pilot Zones significantly enhances cities' green innovation performance, and this finding remains robust after a series of robustness checks, including instrumental variable estimation and staggered difference-in-differences analyses. Heterogeneity analysis indicates that the policy exerts a stronger innovation-enhancing effect in resource-based cities and highly polluted cities, while the policy impact exhibits substantial consistency across cities in different geographical regions. Mechanism analysis further reveals that the policy promotes urban green technological innovation by improving resource utilization efficiency, strengthening technological spillover effects, and reinforcing environmental regulation. Overall, this study provides new theoretical insights and empirical evidence on how institutional design in the digital era can drive green technological innovation.

Impact is one of crucial indicators to measure the value of innovation output by individuals. However, existing studies have rarely distinguished technological impact and social impact, let alone to explore how the embeddedness of dual networks, collaboration networks and knowledge networks, influences different dimensions of innovation impact. To narrow these gaps, this study draws on resource orchestration theory and embeddedness theory to investigate how structural holes and centrality of dual networks affect technological impact and social impact. Based on the patents and primary data of 30 largest listed firms within the Chinese communication industry from 1986 to 2024, we conduct the analysis as the focal-inventor-year unit. Empirical results indicate that: (1)structural holes of collaboration networks are positively related to both technological impact and social impact. (2)Structural holes of knowledge networks are negatively associated with technological impact, while contributes to social impact. (3)Centrality of collaboration networks facilitates both technological impact and social impact. (4)Centrality of knowledge networks impedes both technological impact and social impact. Our findings systematically unveil the formation mechanisms of different dimensions of innovation impact from the perspective of dual network embeddedness. Doing so not only enriches the literature on innovation impact, but also provide targeted suggestions for managers to allocate network resources and improve innovation impact.

Using data from China's A-share listed companies and their top five customers from 2007 to 2024, this study investigates the spillover effects of customers' climate risks on the credit risk of their suppliers, revealing the transmission mechanisms and key influencing factors. Empirical results indicate that customers' climate risks significantly increase their suppliers' credit risks by negatively impacting their operational efficiency and financing capabilities. These spillover effects are more pronounced when suppliers have high specificity, a high customer concentration, and when economic policy uncertainty is high. Further analysis suggests that in response to climate risk spillovers from customers, firms may increase cash holdings and engage in earnings management. The research findings provide theoretical foundations and practical guidance for mitigating the cross-firm transmission of climate risks and enhancing the overall resilience and sustainability of the economic system.

Against the backdrop of deepening"dual carbon"goals, rising carbon risks are impacting corporate information environments.Given that ESG ratings are highly information-dependent composite assessments, it is significant to examine whether the degree of ESG rating divergence is influenced by carbon risk.This study empirically examines the impact of carbon risks on ESG rating divergence, based on data from A-share listed companies from 2018 to 2022.It investigates the causal pathways of carbon risks on ESG rating divergence from an information environment perspective.Findings indicate that rising carbon risk significantly widens ESG rating divergence, with managerial power intensity amplifying this effect while external audit regulation mitigates it.Mechanism tests reveal that carbon risk amplifies ESG rating divergence through two pathways: by increasing corporate ESG information manipulation and by heightening rating agencies' reliance on private information.Heterogeneity analysis indicates that these effects are more pronounced in firms operating under weaker environmental regulations, lower information transparency, poorer regional institutional environments, and higher industry ESG sensitivity.This study provides empirical evidence on how carbon risks influence ESG rating divergence from an information environment perspective, offering insights for optimizing information environments and reducing rating discrepancies.

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.

The algorithmic governance of platforms has given rise to new forms of gig work.This study constructs a three-dimensional analytical framework— "endogenous resources-resource depletion-resource compensation ", based on the self-regulation resource model.Using fuzzy-set qualitative comparative analysis (fsQCA), we surveyed the algorithmic resistance behavior of 320 platform gig workers.Results indicate: high algorithm resistance is triggered by three categories and five pathways—compensation deficiency-high-pressure polarization, endogenous scarcity-resource depletion, and trust crisis-compensation failure—with core mechanisms rooted in perceived organizational support deficits and the synergistic effect of multiple stressors that breach the resource equilibrium threshold.Low algorithmic resistance emerges through three pathways: endogenous resilience-pressure alleviation, endogenous resilience-compensation sufficiency, and pressure alleviation-compensation sufficiency.These pathways rely on interactions among reserves of psychological resources, systemic compensation mechanisms, and alleviated stressors.This study breaks from traditional linear analysis to reveal the multiple concurrent causal mechanisms of algorithmic resistance, providing a new configurational analysis framework for platform gig worker behavior research.

Flexible and proactive domestic supply chain adjustments are of critical importance for Chinese firms in responding to trade policy uncertainty.Using text-based measures constructed from annual reports of Chinese A-share listed firms on the Shanghai and Shenzhen stock exchanges over the period 2005~2024, this study examines the impact of firm-level perceptions of trade policy uncertainty on supply chain adjustments.The results indicate that firms' perceptions of trade policy uncertainty exert a statistically significant effect on supply chain adjustments.Specifically, firms respond to heightened trade policy uncertainty by actively expanding overseas markets, reducing upstream supplier concentration, and increasing intermediate input usage, thereby adjusting their supply chains along multiple dimensions.Heterogeneity analyses reveal that the effects of trade policy uncertainty on supply chain adjustments vary systematically across firms with different sizes, levels of foreign ownership, and ownership types.Mechanism analyses further suggest that trade policy uncertainty affects firms' supply chain adjustments primarily by increasing supply-demand coordination costs, operational costs, and financing costs.Overall, this study provides empirical evidence that enhances our understanding of how firms adjust their supply chains in response to trade policy uncertainty and offers insights into the construction of more resilient and secure supply chain relationships.

In the context of the profound restructuring of global value chains and the increasing frequency of"black swan"events, building a more resilient industrial system has become a strategic priority for balancing development and security.However, there is still a lack of systematic empirical research on whether lighthouse-style transformation can enhance the resilience of manufacturing firms.Using data from China's A-share manufacturing listed companies between 2014 and 2024, this study applies a multi- time-point difference-in-differences (DID)model to systematically examine the dynamic impacts and pathways through which lighthouse transformation influences the resilience of the manufacturing sector.The results indicate that lighthouse-style transformation significantly enhances the resilience of manufacturing enterprises, with these findings remain valid after a battery of robustness checks.Mechanism analysis shows that lighthouse transformation strengthens resilience through improvements in innovation capabilities and network centrality.Further analysis reveals that the effects of digital transformation on manufacturing resilience vary based on factors such as industry-specific characteristics, regional context, and the level of digital infrastructure.Additionally, lighthouse transformation plays a crucial role in enhancing the synergies between digital transformation and manufacturing resilience.This study contributes to the theoretical understanding of lighthouse transformation and the resilience of the manufacturing industry, providing empirical evidence to support firms in formulating intelligent upgrading strategies and assisting policymakers in optimizing industrial policies.It also offers practical insights for fostering the high-quality development of the manufacturing sector.