Search Results (23)

In the era of the digital economy, enhancing enterprise resilience has become a strategic imperative for sustainable manufacturing development. However, the micro-level mechanisms through which data element policies, specifically China’s National Big Data Comprehensive Pilot Zone, empower enterprise resilience remain insufficiently explored. To address this gap, this study leverages the policy rollout as a quasi-natural experiment and employs a multi-period difference-in-differences approach to analyze panel data of listed manufacturing firms. The results reveal that enterprises within pilot zones exhibit a 2.3% average increase in resilience compared to non-pilot counterparts. This effect is significantly amplified by enterprise digital transformation and regional innovation-entrepreneurship vitality. Mechanism analysis further identifies that the policy enhances resilience primarily by reducing supply chain coordination costs and improving relationship stability, with additional positive spillovers observed in adjacent cities. These findings highlight the disruptive potential of big data in reshaping corporate resilience paradigms and provide empirical support for scaling data-driven industrial policies to foster high-quality economic development. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

The synergistic development of digitalization and green transition has become a key driver for promoting China’s high-quality economic development. To elucidate the impact and mechanism of digital–green policy synergy on urban carbon emissions, this paper utilizes the intersection of the “National Big Data Comprehensive Pilot Zones” (BDPZ) and “Low-Carbon City Pilot” (LCCP) programs as a quasi-natural experiment. Based on panel data from 300 prefecture-level cities in China from 2005 to 2023, a multi-period DID model is constructed for empirical research. The empirical results indicate the following: (1) The synergy between digital and green policies significantly curbs urban carbon emissions, and this conclusion remains robust after parallel trend tests and a series of robustness checks. (2) Compared with single digital or green policies, the digital–green synergy exhibits a significantly superior carbon reduction effect. (3) Mechanism analysis reveals that digital–green synergy promotes low-carbon transition primarily through three pathways: driving green technology innovation, promoting the agglomeration of scientific and technological talent, and optimizing the allocation efficiency of capital factors. (4) Heterogeneity analysis reveals stronger emission reduction effects in non-resource-based, eastern, and developed cities, highlighting how structural rigidities and the digital divide constrain the policy’s effectiveness. We suggest strengthening policy integration and adopting differentiated strategies to break path dependence and achieve “Dual Carbon” goals. Full article

Green total factor productivity (GTFP), as an important indicator considering both economic development and environmental protection, has prompted countries around the world to actively explore ways to improve it in the context of the global transition to a green economy. The Low-Carbon City Policy (LCCP) implemented by the Chinese government, along with the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ), which serve as key carriers of green regulation and digital innovation, respectively, play an important role in improving green total factor productivity (GTFP) and achieving high-quality economic development. This study aims to deeply explore whether there is a collaborative enabling effect of the Low-Carbon City Policy (LCCP) and the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ) on green total factor productivity (GTFP) and to reveal the internal mechanism by which they improve GTFP through green technological innovation and industrial agglomeration. Specifically, based on the panel data of 269 prefecture-level cities in China from 2006 to 2022, a “dual-pilot” policy is constructed through LCCP and NBDCPZ, and a multi-period difference-in-differences model (DID) is used to evaluate the collaborative effect of the “dual-pilot” policy on GTFP. The results show that the “dual-pilot” policy has a significant collaborative effect on green total factor productivity (GTFP), and its enabling effect is more obvious than that of the “single-pilot” policy. These conclusions still hold after a series of endogeneity and robustness tests. Mechanism analysis shows that the “dual-pilot” policy can also improve green total factor productivity (GTFP) through green technological innovation and industrial agglomeration. Heterogeneity analysis reveals that the collaborative enabling effect of the “dual-pilot” policy is influenced by geographical location and population density. Specifically, the “dual-pilot” policy significantly promotes green total factor productivity (GTFP) in coastal cities and those with high population density. These research results provide a scientific basis for formulating green development policies in China and other countries, as well as a direction for subsequent research on the collaborative enabling effect of multiple policies. Full article

This paper takes the quasi-natural experiment from the National Big Data Comprehensive Pilot Zone (NBDCPZ) in China as an example to examine the impact of market-oriented allocation of data elements on enhancing enterprises’ New Quality Productive Forces (NQPF). Based on panel data from China’s A-share listed enterprises on the Shanghai and Shenzhen stock exchanges between 2011 and 2022, this study employs a robust policy evaluation method, the multi-way fixed effects staggered difference-in-differences (MWFE Staggered DID) method, to analyze the impact of the NBDCPZ on NQPF comprehensively. The key findings are threefold: First, the NBDCPZ significantly boosts enterprises’ NQPF within their jurisdictions. Second, the NBDCPZ enhances NQPF by accelerating enterprise digital transformation, and the digital talent can amplify the promotional effect of the NBDCPZ on enterprise digital transformation. Third, the NQPF-enhancing effects are more pronounced for privately owned enterprises (POEs), foreign-invested enterprises (FIEs), and smaller enterprises, whereas they exhibit an inhibitory impact on state-owned enterprises (SOEs) and large enterprises. Fourth, the promotional effect of the NBDCPZ on enterprises’ NQPF varies across different industries. Furthermore, regional (city-level) digital infrastructure and financial development levels amplify the NQPF-enhancing effects of the NBDCPZ. Full article

Modernization in developing countries such as China has long been unsustainable. As a result, China has set the goal of achieving sustainable modernization characterized by harmony between humanity and nature. Against this backdrop, in this study, we apply spatial difference-in-differences (SDID) and double machine learning (DML) models using panel data from 30 provincial-level regions in China from 2009 to 2021. We examine the impacts of the National Big Data Comprehensive Pilot Zone policy and sci-technology financial ecology on the Chinese Path to Sustainable Modernization. The results show that big data pilot zones significantly enhance modernization and generate positive spatial spillover effects through demonstration and diffusion. Sci-technology financial ecology improves sustainable modernization and amplifies the role played by pilot zones. Heterogeneity tests reveal stronger effects in eastern provinces and in areas implementing urban–rural integration or green finance reforms. The results of the mechanism analysis show that big data innovation promotes modernization by strengthening sci-technology financial ecology, raising government attention, fostering inclusive intelligence development, enhancing green innovation efficiency, and upgrading industrial structures. Full article

The escalating frequency of natural disasters and political conflicts has heightened focus on industrial supply chain resilience and security, making corporate supply chain resilience enhancement a critical global concern. Data, as a novel production factor, presents an effective pathway to fortify supply chain resilience. This paper investigates data factor marketisation by constructing a theoretical framework linking it with manufacturing enterprise supply chain resilience. Using China’s Big Data Comprehensive Experimental Zone establishment as a quasi-natural experiment, we analyzed data from Chinese A-share listed manufacturing firms spanning 2003–2023 to empirically validate our theoretical analysis. Our findings reveal that data factor marketisation significantly enhances manufacturing enterprise supply chain resilience, as confirmed using rigorous robustness checks. Mechanism analysis demonstrates that data factor marketisation improves resilience by reducing information asymmetries, boosting management efficiency, mitigating supply chain reliance, and enhancing supply chain financing. Heterogeneity analysis indicates stronger positive impacts in non-state-owned enterprises, smaller firms, companies with advanced data capabilities, non-digital-intensive businesses, enterprises with substantial supply chain funding needs, and those in regions with strong rule of law. Further analysis shows that improved employment, financing, innovation, and communication environments amplify the positive relationship between data factor marketisation and supply chain resilience. This study provides crucial insights for policy makers seeking to leverage data marketisation for industrial resilience enhancement and offers strategic guidance for enterprises navigating an increasingly uncertain global supply chain environment. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

The positive impact of data elements on enterprise operation has been confirmed by many scholars, but few studies have paid attention to the effect of data elements on corporate green transformation, especially in the context of global climate change. In this study, we employ panel data from Chinese listing firms to identify the casual impact of data elements on corporate green transformation, using the staggered difference-in-differences method. We show that: (a) Data elements exert a significant positive influence on corporate green transformation. This finding holds up in a series of robustness checks; (b) The promoting effect of data elements on green transformation is mediated by alleviating financing constraints and elevating executive green attention; (c) Green governance resilience and green management innovation can strengthen the positive relationship between data elements and green transformation; and (d) The promoting effect is more pronounced in enterprises with larger boards of directors, those located in the eastern regions, and those characterized by higher carbon emission intensities. Overall, we not only provide empirical evidence of optimizing regional data-factor allocation and promoting green technological innovation but also offer theoretical guidance for refining the pathways of corporate green transformation. Full article

In the context of global climate change and the deepening of ecological civilization construction, forestry, as an ecological security barrier and green economic engine, faces many challenges to the enhancement of its ecological total factor productivity in the traditional development model. As a new type of production factor, the data factor provides a new path to crack the bottleneck of forestry eco-efficiency improvement. Based on China’s provincial annual panel data from 2014 to 2022, this study systematically examines the impact and mechanism of data factors on forestry ecological total factor productivity by using the SBM-GML model and dual machine learning model. It was found that data factors have a significant contribution to forestry ecological total factor productivity, a conclusion that passes a series of robustness tests and endogeneity tests. The analysis of the mechanism shows that the data factor enhances the total factor productivity of forestry ecology mainly through three paths: promoting the progress of forestry technology and promoting the rationalization and advanced structure of the forestry industry. Further analysis showed that the promotional effect of data elements is more obvious in regions with a high level of green finance development, high intensity of environmental regulation, and strong financial autonomy. It is recommended to systematically promote the in-depth application of data elements in forestry, build a data element-driven innovation system for the whole chain of forestry, and implement regionally differentiated data element-enabling strategies. Full article

Against the backdrop of the structural transition in China’s economic landscape, the implementation of digital economy policies—particularly through the Broadband China Demonstration Cities initiatives—has significantly enhanced urban ecological resilience. Based on panel data from 280 prefecture-level cities in China over the period 2013–2022, this study employs the national big data comprehensive pilot zone as a quasi-natural experiment and utilizes the dual machine learning method to examine how pilot zone construction influences urban ecological resilience. This analysis provides theoretical support for fostering green urban development. The results are summarized as follows. (1) The construction of national big data comprehensive pilot zones significantly enhances urban ecological resilience. The conclusion is robust to various tests, including the removal of outliers, changes in sample splitting ratios, and alterations in machine learning algorithms. (2) The construction of national big data comprehensive pilot zones indirectly improves urban ecological resilience through pathways of green innovation and energy efficiency. (3) This study assesses the heterogeneity of policy effects based on the generalized random forest (GRF) model to identify the sources of heterogeneity in policy effects, and conducts a comprehensive heterogeneity analysis from the three dimensions of resource endowments, geographical location characteristics, and the attributes of environmental protection zones. These findings enrich the analysis of the consequences of national big data comprehensive pilot zone policies and offer a theoretical basis and policy reference for how constructing big data pilot zones can better serve urban ecological development. Full article

Digitization plays a vital role in fostering economic and social development. This study empirically investigates the impact of digitization on urban industrial structures, technological innovation, public service levels, and social–ecological resilience. Various approaches, including the two-tier stochastic, spatial econometric, and panel threshold models, have been employed to analyze panel data from 287 cities from 2008 to 2023. These data are examined through a quasi-natural experiment analyzing the evolution of urban social–ecological resilience following China’s promotion of the national comprehensive pilot zone for big data. The findings are as follows. (1) The positive effects of digitization on urban social and ecological resilience substantially outweigh the negative effects, with an overall increasing trend in the positive net effect, albeit with significant regional differences. (2) Digitalization exhibits a significant spatial spillover effect, enhancing local social–ecological resilience while inhibiting improvements in neighboring cities. (3) Technological innovation and public service levels positively affect social–ecological resilience, whereas industrial structure upgrading has a negative indirect effect. Both industrial structure upgrading and public service levels demonstrate nonlinear effects under the threshold constraints of the intermediary mechanism. (4) In terms of policy mechanisms, regional differences in the urban industrial structure, innovation capacity, and public service levels must be considered. This approach is essential for promoting the organic integration of digitization across regions, mitigating the polarization effect, and enhancing the diffusion effect. Full article

Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data. Full article

In the midst of rigorous market rivalry, enhancing a company’s competitiveness and operational efficiency in an era of rapid IT advancement is a pressing concern for business leaders. The National Big Data Comprehensive Zone (BDCZ) pilot scheme, instituted by the Chinese government, systematically addresses seven core objectives, encompassing data resource management, sharing and disclosure, data center consolidation, application of data resources, and the circulation of data elements. This policy initiative aims to bolster the establishment of information infrastructure through big data applications, facilitate the influx and movement of talent, and propel corporate sustainable growth. Utilizing a quasi-natural experiment approach, we assess the pilot policy’s influence on the digital-intelligent transformation (DIT) of manufacturing companies from a green innovation ecosystem perspective, employing datasets from 2010 to 2022, and methodologies such as Difference-in-Differences (DID), Synthetic Differences-in-Differences (SDID), and Propensity Score Matching-DID (PSM-DID). The findings indicate that the BDCZ initiative significantly fosters DIT in manufacturing companies. The policy’s establishment confers benefits, including access to increased government support and innovation capital, thereby enhancing the sustainability of green innovation efforts. It also strengthens corporate collaboration, engendering synergistic benefits that improve regional economic progression and establish a conducive environment for digital development, ultimately enhancing the regional innovation ecosystem. The pilot policy’s impact varies across entities, with more profound effects observed in developed financial markets compared to underdeveloped ones. Additionally, non-state-owned companies exhibit a greater response to BDCZ policy interventions than their state-owned counterparts. Moreover, manufacturing bussiness with a higher proportion of executive shareholding are more substantially influenced by the BDCZ. This article fills the research gap by using the quasi-natural experiment of BDCZ to test the impact on DIT of companies and provides inspiration for local governments to mobilize the enthusiasm of manufacturing companies for DIT. Full article

The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development. Full article