This study provides city-level causal evidence on the economic effects of data trading infrastructure. Treating the staggered establishment of data trading platforms (DTPs) across Chinese cities between 2014 and 2023 as a quasi-natural experiment, we estimate a multi-period difference-in-differences model using a balanced panel of 298 prefecture-level cities from 2000 to 2023. The baseline estimate indicates that DTP establishment increases night-time light intensity - our primary proxy for urban economic vitality - by 3.610 units, robust across all sensitivity specifications. Event-study analysis validates parallel trends, and a randomization-inference placebo test yields an empirical p-value below 0.001. A Bartik instrumental variable strategy produces a 2SLS estimate of 9.421 (p < 0.05), which we interpret as complementary given a moderately relevant first stage (F = 2.71). Mechanism tests reveal that DTPs promote innovation and digital economy employment. A dose-response analysis shows that each additional year of platform operation increases vitality by 0.876 units on average, but the marginal effect turns negative in cities with below-median internet penetration. These findings establish that DTPs can serve as effective institutional vehicles for data factor marketization, but their economic returns are contingent on complementary digital infrastructure investments.

As the digital economy advances, information infrastructure has become a core engine for driving green economic transition and optimizing sustainable land systems. However, its heterogeneous governance effects on different types of pollutants and spatial spillover mechanisms remain insufficiently explored. This study draws on the theoretical framework of the dynamic game between scale and technique effects. It utilizes the PSTR model and the SDM to systematically investigate the nonlinear and spatial synergistic impacts of information infrastructure. The analysis covers aggregate information infrastructure and its structural subdivisions, including traditional and new information infrastructure. To ensure empirical rigor, this study introduces a Bartik instrumental variable constructed via the shift share approach and thoroughly eliminates endogeneity interference through the Control Function Approach and a core variable lagging strategy. The empirical research reveals three core findings. Firstly, after crossing the initial extensive scale effect dominated by physical construction, the profound technique effect dominates long-term environmental governance. Secondly, new-type information infrastructure demonstrates a superior capacity for long-term environmental governance and land use efficiency compared to traditional telecommunications. Finally, spatial spillover analysis indicates that although PM2.5 exhibits strong cross-regional physical contagion, the current environmental dividends of information infrastructure remain highly localized due to regional administrative data silos, lacking significant cross-regional synergistic spillover effects. This study provides a solid empirical basis for formulating differentiated digital spatial governance frameworks, breaking interprovincial data factor barriers, and preventing the physical expansion trap of traditional infrastructure.

Rapid digital industry expansion is reshaping urban growth patterns and raising new research questions about how cities respond to external shocks and maintain economic stability. Using balanced panel data for Chinese prefecture-level cities from 2000 to 2023, this study examines the effect of digital industry development on urban economic resilience and further analyzes the transmission role of financial resource allocation. The results reveal that digital industry development enhances urban economic resilience, indicating that the digital industry not only promotes urban industrial upgrading but also strengthens cities’ capacity to resist shocks, absorb volatility, and restore growth. Digital industry development improves urban financial resource allocation by strengthening financial supply capacity, promoting financial agglomeration, and enhancing financial inclusion, which reinforces economic resilience. More advanced digital infrastructure increases the positive effect of digital industry development on economic resilience. The study also reveals financial resource allocation as the internal pathway through which digital industry development affects urban economic resilience and provides empirical evidence for understanding the relationships between digital industry cultivation, financial system optimization, and stable urban growth.

With growing uncertainty in global trade, improving access to domestic capital markets has become an important way to manage financial risk spillovers. This study examines how the registration system reform affects the finance sector's risk spillovers to other 10 sectors in China’s stock market using a dual machine learning model. The findings include: (1) The finance sector's risk spillovers vary over time and are heterogeneous. Risk spillovers rapidly intensify two months after the outbreak of the COVID-19 pandemic, with the average net ∆CoVaR value changing from −0.001 to −0.006. The finance sector mainly accepts risk from the public utility sector and exports risk to the other 9 sectors, with the highest spillovers going to the communication and information technology sectors, showing extreme net ΔCoVaR values around −0.02. (2) The registration system reform increases the finance sector's risk spillover effect, and this conclusion remains the same after a series of robustness tests. (3) Sector heterogeneity tests show that the reform boosts the finance sector's risk spillovers to cyclical sectors and sectors with a low proportion of strategic emerging companies but reduces risk spillovers to midstream and supportive sectors. Finally, some suggestions and implications are proposed.

This study investigates the impact of high-speed rail opening on the quality development of Chinese listed firms from 2010 to 2022 by using a difference-in-differences (DID) based on panel data. Through the robustness test and parallel trend test, the results of the study show that the opening of high-speed railway significantly promotes the high-quality development of firms. The opening of high-speed railway significantly improves the economic quality of enterprises by improving the quality of information disclosure of enterprises, promoting innovation of enterprises, reducing market distortions, alleviating information asymmetry, and easing financing constraints. From the heterogeneity analysis, the opening of high-speed rail has no significant effect on the quality development of technology-intensive industries, capital-intensive industries and state-owned enterprises, and highly competitive industries, but it has a significant effect on labor-intensive industries, non-state-owned enterprises, and low-competitive industries. This paper extends the study of the economic impact of high-speed rail construction to the micro-firm level, providing valuable insights into the planning and spatial distribution of high-speed rail infrastructure.

Utilizing data from 282 prefecture-level cities in China from 2005 to 2021, this study constructs an evaluation index system for high-quality economic development across the following five dimensions: innovation, coordination, green, openness, and sharing. A continuous difference-in-differences approach is employed for regression analysis to empirically examine the impact of high-speed rail on high-quality economic development, further exploring its mechanisms and spatial spillover effects. The findings reveal that (1) HSR significantly promotes high-quality economic development; (2) with the development of HSR, from 2005 to 2021, China’s high-quality economic development showed an evolutionary trend of overall improvement, with a gradual optimization of spatial patterns; (3) it facilitates high-quality economic development by enhancing capital and labor mobility, strengthening industrial chain resilience, and advancing industrial structure upgrading; (4) high-speed rail development in neighboring regions generates positive spatial spillover effects on local urban economic quality; and (5) the impact of high-speed rail on high-quality economic development exhibits significant heterogeneity across cities with different regions, tiers, scales, and resource endowments. These results confirm the positive role of high-speed rail in fostering high-quality economic development.

This study focuses on the coupling and coordination between China’s new-type urbanization (NU) and transportation carbon emission efficiency (CET), revealing its spatial and temporal evolution patterns and driving factors. In recent years, the rapid rise of the digital economy has profoundly reshaped traditional industrial structures. It has catalyzed new forms of production and consumption and opened up new pathways for carbon reduction. This makes synergies between NU and CET increasingly important for realizing a low-carbon transition. In addition, digital infrastructures such as 5G networks and big data platforms promote energy efficiency and facilitate industrial upgrading. It also promotes the integration of low-carbon goals into urban governance, thus strengthening the linkages between NU and CET. The study aims to provide a scientific basis for regional synergistic development and green transformation for the goal of “dual carbon”. Based on the panel data of 30 provinces in China from 2004 to 2021, the study adopts the entropy weight method and the super-efficiency SBM model to quantify NU and CET, and then analyzes their spatial and temporal interactions and spatial spillovers by combining the coupled coordination degree model and the spatial Durbin model. The following is found: (1) NU and CET show a spatial pattern of “leading in the east and lagging in the west”, and are optimized over time, but with significant regional differences; (2) the degree of coupling coordination jumps from “basic disorder” to “basic coordination”, but has not yet reached the level of advanced coordination, with significant spatial clustering characteristics (Moran’s I index between 0.244 and 0.461); (3) labor force structure, transportation and energy intensity, industrial structure and scientific and technological innovation are the core factors driving the coupled coordination, and have significant spatial spillover effects, while government intervention and per capita income have limited roles. This paper innovatively reveals the two-way synergistic mechanism of NU and CET, breaks through the traditional unidirectional research framework, and systematically analyzes the two-way feedback effect of the two. A multidimensional NU evaluation system is constructed to overcome the limitations of the previous single economic or demographic dimension, and comprehensively portray the comprehensive effect of new urbanization. A multi-dimensional coupled coordination measurement framework is proposed to quantify the synergistic evolution law of NU and CET from the perspective of spatio-temporal dynamics and spatial correlation. The spatial spillover paths of key factors are finally quantified. The findings provide decision-making references for optimizing low-carbon policies, promoting green transformation of transportation, and taking advantage of the digital economy.

The efficacy of green credit policies is contingent less on their scale than on the quality of their implementation by local governments. Departing from conventional scale-based analyses, this study constructs a novel indicator for the intensity of local government policy responses to disentangle the independent effect of implementation quality on carbon abatement in China. Employing a panel dataset of Chinese provinces and municipalities, we utilize fixed-effects, System-GMM, and spatial econometric models. Our analysis yields three core findings. First, a proactive policy response at the provincial level significantly reduces carbon intensity, a finding that proves robust across a comprehensive suite of sensitivity analyses. Conversely, municipal-level responses are largely ineffective, pointing to a significant "implementation gap" within China's multi-level governance structure. Second, the policy's impact is spatiotemporally heterogeneous; its effectiveness emerges only after the 2012 national Green Credit Guidelines, and it generates positive spatial spillovers. Third, our moderation analysis reveals that the policy's abatement effect is significantly attenuated in regions with high coal dependency, confirming that carbon-intensive path dependence constitutes a formidable barrier. To diagnose the sources of this municipal-level inefficacy, we employ cluster analysis to classify cities into four distinct typologies, each facing unique implementation challenges. These findings underscore that enhancing green finance efficacy necessitates a fundamental shift from a "one-size-fits-all" framework toward interventions tailored to local governance capacity and economic context.

The two-part tariff (TPT) policy is implemented as an important initiative to accelerate the marketization of the pumped storage industry and promote its high-quality development. However, it is not clear exactly how the TPT policy affects the productivity of the pumped storage industry. Using the EBM-GML method and the DID model, this paper measures the total factor productivity of the pumped storage industry and explores the impact of the Two-Part Tariff (TPT) policy on its total factor productivity. Based on the samples of 16 provinces in China from 2004 to 2020, we find the following: (1) At present, the total factor productivity of China’s pumped storage industry is still at a low level. (2) TPT policy can promote the improvement of total factor productivity, which was strongly supported by the robustness test. Innovation incentive is one of the main mechanisms. (3) The impact of TPT policy on total factor productivity has obvious regional heterogeneity. By geographic location, the TPT policy has little effect on the pumped storage industry’s TFP in the eastern region, but it exerts a significant positive role in the central region. By energy affluence, TPT policy effect is stronger in provinces with low energy dependence. By environmental governance, the role of this policy is more obvious in provinces with low environmental regulation but developed green financial market. Finally, some corresponding policy implications have been put forward.

This paper explores the impact of digital economy, population, affluence, technology, and other factors on carbon emissions, with panel data for 13 cities in the Beijing-Tianjin-Hebei urban agglomeration from 2011-2019.To overcome the negative influences of multicollinearity among independent variables under acceptable bias, we extended the traditional STIRPAT model and adopted the Partial Least Squares Regression (PLSR) algorithm.Results show that the digital economy has a directly dampening effect on carbon emissions, and the effect will diminish as the digital economy develops.Besides, under different development levels, differences are significant in terms of the impact of population, affluence, technology, urbanization rate and industrial structure on carbon emissions.Academically, we applied the PLSR method to the study of the relationship between digital economy and carbon emissions for the first time, which enhanced the credibility of the research conclusions.In addition, analysis based on samples from China's Beijing-Tianjin-Hebei urban agglomeration also provides more empirical evidence for related research.Practically, we recommend such policies as developing digital cities, promoting low-carbon concepts, and accelerating industrial transformation for the Beijing-Tianjin-Hebei region to achieve the "dual-carbon" goals and high-quality economic development.

As a new type of economic format, digital economy has three major characteristics: technical, innovative, energy-saving and environmentally friendly. Acting on various sectors of the national economy, it is beneficial for improving carbon emission efficiency and is of great significance for achieving China’s two major goals of carbon peak and carbon neutrality. Firstly, theoretical analysis of the impact mechanism of digital economy on carbon emission efficiency, proposing research hypotheses on the direct effect, mediating effect, and spatial effect of digital economy on carbon emission efficiency. Secondly, based on panel data from 279 cities in China from 2011 to 2020, the econometric models are constructed to empirically analyze the direct, mediating, and spatial effects of digital economy on carbon emission efficiency. The results show that: 1) Digital economy can improve carbon emission efficiency; 2) The impact of digital economy on carbon emission efficiency has a “U”-shaped relationship, which is consistent with the "Environmental Kuznets Curve" hypothesis; 3) The impacts of digital economy on carbon emission efficiency exist in urban heterogeneity, specifically manifested as regional heterogeneity and urban scale heterogeneity; 4) Technological innovation is an important mediator for improving carbon emission efficiency in digital economy, and promoting technological innovation in digital economy can improve carbon emission efficiency; 5) Digital economy has spatial effect on carbon emission efficiency, which can improve the carbon emission efficiency of neighboring cities. Finally, based on the above results, suggestions are proposed from three aspects: promoting important industries and key areas for deep cultivation of carbon emission in digital economy, emphasizing regional balance in the development of digital economy, and strengthening regional cooperation in the development of digital economy, in order to continue to play a positive role in improving carbon emission efficiency through digital economy.

This study systematically evaluates the reliability of PM2.5 monitoring data across major urban areas, utilizing a comprehensive dataset covering 283 cities in China over a seven-year period. By using Benford’s Law, robust regression analysis, and various machine learning methods, such as Gradient Boosting Trees and Random Forests, the overall reliability of China’s PM2.5 monitoring data is high. These models effectively captured complex patterns and detected anomalies related to both natural environmental and socioeconomic factors, as well as potential data manipulation. Based on the integrated models, the proportion of anomalies in PM2.5 concentration monitoring data across 283 cities in China from 2015 to 2022 was less than 2%, which strongly indicates the overall reliability of China’s PM2.5 concentration monitoring data. Additionally, machine learning models provided a ranking of the importance of different variables affecting PM2.5 concentrations, offering a scientific basis for understanding the driving factors behind the data. The three variables that have the greatest impact on PM2.5 concentrations are population density, average temperature, and relative humidity. By comparing with other related studies, we further validated our findings. Overall, this study provides new methods and perspectives for understanding and evaluating the reliability of PM2.5 data in China, laying a solid foundation for future research.

Applying the improved social-network analysis method and the idea of rolling-window regression, this paper explores the impact of an HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over the period 2009–2019, we find that the high-speed railway network has an increasingly positive effect on the quality of urbanization, which proves the existence of a network effect. The empirical results further show that this effect is closely related to the coverage rate of the high-speed rail network. In addition, heterogeneity analysis reveals that urban agglomeration cities are the main beneficiaries. Academically, our study provides a plausible explanation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed-rail-network construction stages.

This paper explores the relationship between high-speed rail (HSR) and industrial agglomeration within urban agglomerations. The paper selects the data of the Beijing–Tianjin–Hebei Urban Agglomeration (BJHUA) and Central Plains Urban Agglomeration (CPUA) from 2002 to 2016 as the research object. The time-varying difference-in-difference (TVDID) model is innovatively applied to analyze the impact of HSR on the agglomeration of secondary and tertiary industries in urban agglomerations, and the industrial agglomeration effects of the two urban agglomerations are compared. The results show that the influence of high-speed railways on the industrial agglomeration of urban agglomerations is heterogeneous. In the BJHUA, the impact of HSR on the agglomeration of secondary and tertiary industries is not particularly significant. On the other hand, in the CPUA, HSR does not have a significant impact on the agglomeration of secondary industry. However, it does have a significant negative effect on the agglomeration of tertiary industry. In addition, further analysis reveals significant variations in the impact of HSR on the agglomeration of industries within urban agglomerations after excluding the central cities. It is important to note that the impact of HSR on regional industries can be complex and multifaceted. The findings enrich the theoretical understanding of the relationship between HSR and industrial agglomeration.

Innovation in new energy technologies is a key driver in China’s efforts to achieve its environmental goals. However, the ability of different regions to develop and utilize new energy technologies may depend on their level of economic development. Based on a two-way fixed-effects panel data model, this paper empirically analyses the industry carbon emission reduction effect of new energy technology innovation and its heterogeneous performance at different stages of economic development, using data from 30 provinces and cities in China from 2000 to 2019. The results show that new energy technology innovation generally promotes CO2 emissions in China. The specific effects are closely related to the characteristics of the industry and the stage of economic development. At the same time, the implementation of environmental regulations will inhibit this positive effect, while the adjustment of the industrial structure may promote this positive effect. This paper discovers the mechanism of heterogeneity in new energy technology innovation among different provinces with different levels of economic development. This finding helps to fully assess the carbon emission reduction capacity and potential of different provinces and facilitates the rational disaggregation and formulation of climate policy goals among regions.

PurposeThe digital economy has become a new engine for economic development, promoting the upgrading and transformation of traditional industries as well as fostering emerging industries and forms of business. Nonetheless, how does the digital economy affect innovation? The research objective is to explore the specific impact of the digital economy on innovation output.Design/methodology/approachThis paper innovatively adopts the dynamic panel data model (DPDM) to carry out an empirical study on the impact of the digital economy on innovation output, through the observation of 30 provincial-level administrative regions in China. Furthermore, the paper innovatively analyzes the impact of different dimensions of the digital economy on innovation output and the impact of the digital economy on different dimensions of innovation output.FindingsIt is found that the digital economy is conducive to boosting innovation output considering innovation continuity. Specifically, the driving impact of core industries and enterprise application of digital economy on innovation output is more prominent, but the driving impact of infrastructure and personal application on innovation output is not fully played. Meanwhile, the driving impact of the digital economy on the innovation output quality is more significant than that digital economy on the innovation output quantity.Originality/valueThis study employs a DPDM for the first time to investigate the specific impact of the digital economy on innovation output, and contributes to the existing literature on the digital economy and digital economy-driven innovation. The findings offer a comprehensive explanation for the impact of the digital economy on innovation output, which has reference value for the formulation of innovation policies driven by digital economy, thereby providing impetus for the sustained and stable development of China's economy.

On the China–Europe route, the sea used to dominate entirely continental transports, but in the last decade the railways started to gain some ground. However, it took number of years that railway volumes grew as significant, and finally coronavirus era (2020–2021) promoted much higher usage. This was the case not only on the main route Poland–China, but also on the more northern and lower-volume routes, such as Finland–China. This research uses regression analysis of the latter route to reveal factors that have an effect on rail container volumes. It is shown that Baltic Dry Index (BDI) and coronavirus related variables have influenced most container volumes on the Finland–China route. Oil price development has also played some sort of role. Interestingly, sea port handling of Finland does not play any significance in the model, nor does foreign trade between countries. Findings could be explained with the low starting ground of service, and dominance of sea transportation.