Dubin Model Research Articles

Spatio-temporal differentiation characteristics of coal mine safety levels in China based on spatial spillover effects

In order to solve the problem of "potpourri" of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

Spatial spillover effects of the digital economy on the green total factor productivity of China’s manufacturing industry

The rapid expansion of the digital economy has garnered significant attention because of its potential to drive high-quality advancement in traditional sectors, particularly manufacturing. This study examines the spatial dynamics and potential spatial disparities between the digital economy and green total factor productivity (GTFP) in China’s manufacturing sector. We utilize a novel set of indicators to assess the advancement of the digital economy during Chinese provinces and apply spatial econometric models to investigate its spatial influence on GTFP. The main research content and conclusions of this study are as follows. (1) By employing a novel index system to quantify the digital economy’s advancement level within the manufacturing sector across various provinces in China, and utilizing it as the primary explanatory variable, the index system demonstrates significant efficacy in empirical analysis and is both scientifically robust and methodologically sound. (2) Using the spatial Dubin model, this study analyzes the spatial effects of the digital economy on the GTFP of the manufacturing sector and finds that it has a dominant positive spatial spillover effect on the GTFP of the manufacturing industry nationwide. (3) The results show that the effects of digital economy advancement on GTFP in the manufacturing industry have a positive spatial spillover effect in the eastern and central area, whereas it is negative in the western area. This study extends the applicability of the new economic geography and imperfect competition theories to the digital economy era, thereby contributing to these academic fields. Moreover, it introduces a novel analytical framework for assessing the digital economy’s influence on manufacturing advancement. The findings provide valuable insights and policy recommendations for fostering the development of the digital economy advancement across diverse regions in China.

Research on the Spatial Effect of Igital Economy Development Enabling Green Technology Innovation in Yangtze River Delta region

At the time of the transformation of old and new driving forces for economic growth, green technology innovation enabled by the digital economy has gradually become a powerful driving force for scientific and technological innovation and the improvement of the development level of green economy in the Yangtze River Delta region. This paper takes 41 cities in the Yangtze River Delta region as the research object, and empirically analyzes the spatial correlation degree and the spillover effect of green technology innovation in the Yangtze River Delta region from 2016 to 2020 by constructing the spatial Dubin model. The results show that: (1) Digital economy has a strong positive impact on green technology innovation and development, and has an empowering effect on regional green technology innovation and development. (2) The development of green technology innovation in the Yangtze River Delta showed a significant positive spatial autocorrelation, and the spatial autocorrelation decreased with the increase of years, and the trend of unbalanced development of green technology innovation increased. (3) The level of digital economy, the level of opening up to the outside world, the rate of urbanization and the level of scientific and technological development have a spatial spillover effect on regional green technology innovation and development. Based on the above conclusions, the paper puts forward some countermeasures and suggestions to promote the innovation and development of green technology enabled by digital economy in Yangtze River Delta region.

From here to where: assessing the infrastructure financialization in urban China

PurposeInfrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.Design/methodology/approachTo assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.FindingsThe results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.Originality/valueThis study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

Can digital transformation affect coal utilization efficiency in China? Evidence from spatial econometric analyses

The swift development of digital technology in China, coupled with the escalating significance of coal resource utilization in the industrial sector, necessitates an investigation into the role of digital transformation in fostering the sustainable development of coal resources. We utilize the Spatial Dubin Model (SDM) to analyze data from 30 Chinese provinces between 2011 and 2019, exploring the relationship between digital transformation and coal utilization efficiency. Additionally, the research investigates spatial spillover effects and the moderating effect mechanism at play. Key findings reveal that: Firstly, a noteworthy positive correlation exists between digital transformation and coal utilization efficiency. Secondly, digital transformation yields significant positive impacts on proximate cities. Lastly, analysis of moderating effects indicates that digital development policies can enhance the positive association between digital transformation and coal utilization efficiency. These findings furnish novel insights and avenues for regions seeking to propel sustainable development in coal resource utilization.

Green digital finance and technology diffusion

Technology diffusion is essential for sustainable development. However, traditional financing faces structural problems in supporting technological innovation and diffusion. Fortunately, greening and digitalisation have become important directions for the financial development. It’s well worth examining whether green digital finance would promote the diffusion of technology among regions. Therefore, this study explores the causal relationship between green digital finance and technology diffusion using the panel data of 35,532 ‘citing city-cited city’ pair observations from 2002 to 2015. Considering different spatial spillover effects of administrative boundaries and geographic distances, it applies and extends the classic spatial Dubin model with a dual-weighted boundary and distance. The main findings of this study are as follows. First, green digital finance shows significant and considerable spatial stimulus effects, particularly in terms of spatial diffusion across cities. A slight cumulative phenomenon is revealed for different technology diffusion durations. Green digital credit, investment, and support have better stimulating effects on technology diffusion. Second, digital economy and market integration are effective mechanism pathways during this stimulating process of green digital finance on technology diffusion. Digitalisation innovation has a better mechanism effect than others. Third, considering the direction of technology diffusion, the diffusion effect from the east-region cities was better than that from the central-west-region cities.

Spatial Effects of Service Industry’s Heterogeneous Agglomeration on Industrial Structure Optimization: Evidence from China

Elucidating the impacts of service industry’s agglomeration on the optimization of industrial structures holds paramount significance in advancing urban economic growth and fostering the coordinated and sustainable development of city economies. This study leverages panel data encompassing 251 prefecture-level cities spanning from 2003 to 2019, employing a spatial Dubin model to scrutinize the influence of distinct types of service industry agglomeration on industrial structure optimization. The results show that specialized agglomeration within the service sector significantly inhibits the rationalization of industrial structures and their underlying fundamentals. Conversely, heightened levels of agglomeration in diversified service industries facilitate the rationalization of industrial structure, predominantly driven by regional spatial spillover effects. Further analysis reveals heterogeneity in service industry agglomeration across cities of varying sizes concerning industrial structure optimization, notably accentuating underutilized spatial spillover effects in smaller cities. In light of these insights, this paper advocates for cities to capitalize on the agglomeration and spillover effects between the service industry and other sectors, strategically selecting optimal service industry agglomeration modes to propel industrial structure optimization.

Environmental regulation, land use efficiency and industrial structure upgrading: Test analysis based on spatial durbin model and threshold effect

Environmental regulation promotes industrial structure change and regional economic transformation through land use adjustment, which gets a new way to explore the path of reforming traditional industrialization and urbanization. Based on the panel data of 128 prefecture-level cities in China 's Yangtze River Economic Belt from 2000 to 2020, this paper uses the spatial Dubin model to analyze the impact of environmental regulation and land use efficiency on the upgrading of industrial structure, and sets the panel threshold model to examine the impact of environmental regulation on the upgrading of industrial structure by affecting land use efficiency. The results show that formal environmental regulation has a significant positive spatial effect on the rationalization and upgrading of industrial structure, which are 0.1734 and 0.2854 respectively. Informal environmental regulation has a negative spillover effect on neighboring provinces but not significant. Heterogeneous environmental regulation has obvious “double threshold effect” on industrial upgrading by affecting land use efficiency. When the threshold of environmental regulation intensity is 0.0315–0.0886, environmental regulation still inhibits land use efficiency and industrial structure upgrading. When the threshold value is greater than 0.0886, environmental regulation has a positive impact on land use efficiency but not significant. With the intensity of environmental regulation from weak to strong, it will produce a double threshold effect of “strong inhibition-weak inhibition-interaction promotion” on the upgrading of manufacturing structure through the adjustment of land use efficiency.

The spatial impact of digital economy on carbon emissions reduction: evidence from 215 cities in China

Introduction: Within the global framework of carbon emissions constraints, the digital economy has become a new strategy for cities to achieve sustainable development. Scholarly literature exploring the spatial spillover and spatial mechanisms of the digital economy on carbon emissions is notably scarce.Methods: To estimate the spatial impact of digital economy on carbon emissions, this paper conducted spatial analysis with the spatial Dubin model and panel data of 215 cities in China from 2011 to 2019.Results: The results show that there is a growing regional agglomeration of the digital economy, whereas the spatial evolution of carbon emissions displays low liquidity and high stability. Second, the digital economy directly reduces urban carbon emissions, and this conclusion is supported through a series of robustness tests. However, there exist negative spatial spillover effects of digital economy on carbon emissions reduction in neighboring cities. Third, mechanism analysis reveals that the digital economy mainly affects urban carbon emissions through two paths: industrial structure upgrading and green technology innovation. Moreover, the influence of digital economy exhibits heterogeneity, with a more pronounced effect observed in the central cities and in large and medium-sized cities, as well as in cities with a high agglomeration of the new energy industry.Discussion: Our paper not only presents new documentary evidence for understanding the relationship between digitalization and decarbonization, but also provides specific references for policy making to accelerate low-carbon urban development.

Can the allocation of primary health care system resources affect efficiency? A spatial Dubin model study in China

BackgroundThe primary health care (PHC) system plays an important role in China’s health care system, but there are challenges such as irrational allocation of health resources and inefficient operation, which need to be improved. The purpose of this study was to explore the impact of resource allocation on the efficiency of the PHC system in China.MethodsThe data in 31 provinces were collected from the China Statistical Yearbook 2017–2021 and the China Health Statistical Yearbook 2017–2021. The comprehensive health resource density index (CHRDI) was constructed based on the entropy method and the health resource density index (HRDI), which was used to analyze the allocation of primary health resources in each province. The adjusted efficiency of the PHC system in each province was calculated by the bootstrap data envelopment analysis (DEA). Finally, the spatial Dubin model was used to explore the effect of the CHRDI on efficiency.ResultsFrom 2016 to 2020, the allocation of primary health resources in 31 provinces showed an increasing trend, and the average efficiency after correction showed a decreasing state year by year. The spatial direct effect and spatial spillover effect coefficients of CHRDI were 0.820 and 1.471, which positively affect the efficiency. Per capita Gross Domestic Product (GDP), urbanization rate, and the proportion of the elderly were the factors affecting the efficiency of the PHC system.ConclusionsThe allocation of primary health resources in all provinces in China has improved each year, but there are still great differences, and efficiency must be further improved. Pay attention to the spatial spillover effect of the level of resource allocation and formulate differentiated measures for different regions. Attention should also be paid to the impact of population aging and economic development on the utilization of primary health resources by increasing health needs and choices.

Research on industrial structure adjustment and spillover effect in resource-based regions in the post-pandemic era.

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China's resource-based regions' industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China's resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.

How does economic agglomeration affect carbon emissions at the county level in Liaoning China?

China's regional economic development has become a regional agglomeration model. The implementation of urbanization strategies promotes spatial concentration and catalyzes economic agglomeration. Due to the heterogeneity of economic development at various spatial scales, economic agglomeration has different environmental externalities. Peak carbon and carbon neutrality make it vital to explore these externalities in China's urbanization process, especially at smaller scales. This study uses a long time series of macro data for 42 counties in Liaoning Province and constructs a dynamic spatial panel Dubin model to assess the impact of economic agglomeration on carbon emissions, showing that agglomeration and emissions exhibit an “inverted N-shaped” curve at the county level. There is a threshold of emission increases and reductions for economic agglomeration. When economic agglomeration exceeds a threshold, it shows a significant emission reduction effect. After further decomposition of spatial effects, economic agglomeration affects carbon emissions through two channels: the direct and the indirect effect with urbanization as a mediator. There are also spillover effects of economic agglomeration on carbon intensity, meaning that economic agglomeration impacts carbon emissions in the region and surrounding areas. The results provide a reference for development guidelines and emission reduction policies at the county level, emphasizing the need to balance the economy and the environment in county regional development.

How Double-Carbon Policies Affect Green Technology Innovation Capability of Enterprise: Empirical Analysis Based on Spatial Dubin Model

How Double-Carbon Policies Affect Green Technology Innovation Capability of Enterprise: Empirical Analysis Based on Spatial Dubin Model

How public education investment and advanced human capital structure affect regional innovation: A spatial econometric analysis from the perspective of innovation value chain

This research delves into the impact of public education investment and advanced human capital structure on the innovation outputs at various processes of China's regional innovation value chains. Utilizing the spatial Dubin model, the study analyzes panel data from 30 provinces spanning from 2002 to 2020, distinguishing between the Research and Development (R&D) and Commercialization sub-processes in regional innovation. It reveals that public education investment significantly boosts R&D output directly and also positively influences commercialization output through spillover effects. This trend remains consistent over time. The study also uncovers regional variations in the effects of public education investment on innovation outputs between China's eastern regions and its central-western counterparts. This study does not find any evidence that public education investment leads to local advanced human capital structure. In terms of direct impacts, there appears to be a substitution effect between public education investment and the advanced human capital structure on local R&D outputs, while a more advanced human capital structure tends to negatively correlate with local commercialization outputs. Notably, the study indicates that the higher the level of advanced human capital structure, the more pronounced the negative impact of public education investment on local commercialization outputs. In light of the above findings, we believe that governments should increase public investment in education, but also recognize that investment alone is not enough. It is necessary to transform the education development model and establish a high-quality innovation system. Differentiated strategies for different regions should be implemented, and an appropriate investment compensation mechanism should be established to solve the problem of uneven investment returns. We also do not recommend blindly pursuing the advanced human capital structure. Only when investment in education is in line with the human capital structure will it produce the best results.

Spatial spillover effects of urban innovation on productivity growth: A case study of 108 cities in the Yangtze River Economic Belt.

The spatial spillover of urban innovation has increasingly become an important factor affecting urban total factor productivity (TFP) growth. Based on the panel data of 108 cities in the Yangtze River Economic Belt from 2004 to 2020, this paper illustrates the spatial-temporal evolution trend of urban innovation and manufacturing total factor productivity and uses the spatial Dubin model to study the direct and indirect effects of urban innovation on manufacturing total factor productivity. The main conclusions are as follows:(1)Spatial correlation and heterogeneity between urban innovation and manufacturing total factor productivity prove to be significant in the Yangtze River Economic Belt;(2)Urban innovation has a significant positive impact on manufacturing total factor productivity and has a positive spillover effect on surrounding cities; (3)The effect of urban innovation on manufacturing total factor productivity varies with time and region;(4) Mechanism analysis shows that talent agglomeration significantly promotes the positive impact of urban innovation on manufacturing total factor productivity, while the spillover effect is insignificant.

Fiscal policy, green finance, and low carbon transformation nexus: a novel study unleashing the synergistic effects of carbon reduction and pollution in China.

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development.

Analysis of the spatial effect of clean energy development on green economic growth: evidence from China.

Clean energy development has played a pivotal role in economic transformation. Based on the panel data of 30 provinces in China from 2006 to 2021, the spatial Dubin model was used to empirically investigate the impact of clean energy development on green economic growth. Furthermore, this research selected industrial structure optimization as the mediating variable to analyze the mediating effect between clean energy development and green economic growth. The results are as follows. Firstly, there is a positive spatial correlation for green economic development, in which the level of green economic development in a region can be influenced by neighboring regions. Secondly, there is a significant positive relationship between clean energy development and green economic growth, in which the clean energy development has a positive impact on green economic growth. Thirdly, the mediating effect analysis demonstrates that the clean energy development can influence green economic growth through industrial structure optimization. Based on the findings in this paper, several suggestions are proposed. China should formulate a unified national strategy for green economy by coordinating and balancing regional differences, develop the clean energy industry to promote green economic growth, and pay attention to the intermediary effect of industrial structure to promote green economic growth.

The Impact of OFDI Reverse Technology Spillover on Regional Innovation Capabilities——A Spatial Analysis Based on Provincial Panel Data

This paper analyzes the impact of outward foreign direct investment (OFDI) reverse technology spillover on regional innovation capabilities in China. The study utilizes provincial panel data from 2004–2020 and employs the spatial Dubin model to analyze and test the spatial spillover effects of OFDI reverse technology spillover, while also decomposing the direct and indirect effects. The findings reveal that regional innovation capacity itself has a spatial spillover effect, and reverse technology spillover has a positive direct and spatial effect on the region's innovation capability. Based on these results, the paper offers recommendations for China's future development of regional innovation capability and foreign investment.

Heterogeneity Analysis of Industrial Structure Upgrading on Eco-Environmental Quality from a Spatial Perspective: Evidence from 11 Coastal Provinces in China

Upgrading the industrial structure and improving the quality of the ecological environment are important strategic steps to realize the modernization of China. Based on the panel data of 11 provinces (municipalities) in China’s coastal areas from 2010 to 2019, this paper uses the spatial Dubin model and the threshold effect model to study the impact of industrial structure upgrading on eco-environmental quality. The results show that the influence of industrial structure upgrading on ecological environment quality has a positive “U”-shaped distribution. Based on the spatial econometric model, it is found that the rationalization of industrial structure and the optimization of industrial structure have spatial spillover effects on the ecological environment quality, and the influence of the rationalization of industrial structure and the optimization of industrial structure on the ecological environment quality of the surrounding area is positive “U”-shaped and inverted “U”-shaped, respectively. Based on the threshold model, it is found that industrial structure rationalization has a small effect on the ecological environment’s quality when the degree of scientific and technological innovation is low. When scientific and technological innovation reaches a certain threshold, industrial structure rationalization has a significant effect on the quality of the ecological environment. In addition, from a regional perspective, the influence of industrial structure rationalization in the East China Sea and the South China Sea and industrial optimization in the Bohai-Yellow Seas on the eco-environmental quality of the surrounding areas has a positive “U”-shaped distribution, while the influence of the optimization of industrial structure in the South China Sea on the eco-environmental quality of the surrounding areas has an inverted “U”-shaped curve on the left side.

How does Information and Communication Technology (ICT) industry agglomeration affect carbon emission efficiency? Evidence from China.

The Information and Communication Technology (ICT) industry takes on critical significance in promoting economic development and reducing carbon emissions. From the agglomeration perspective, how the ICT industry agglomeration affects carbon emission efficiency (CEE) in 30 provinces in China from 2006 to 2020 is innovatively investigated. We measured CEE using a dynamic directional distance function-based DEA model. Then, based on the research hypotheses, the specific impact and transmission mechanism of ICT industrial agglomeration on CEE are revealed using a spatial Dubin model and a threshold panel model. The results show that: (1) the ICT industry agglomeration exerts a remarkable inverted "U-shaped" effect on CEE. This non-linear effect is significant in the eastern and central regions, but not in the western region. (2) ICT industry agglomeration can affect CEE in neighboring regions. The spatial spillover effect shows an inverted "U-shaped" in the central region, positive in the western region, and insignificant in the eastern region. (3) when green technology innovation exceeds the threshold value (4.948), ICT industry agglomeration positively affects CEE, and when energy structure exceeds the threshold value (0.389), their marginal effects are significantly negative. The threshold effect also shows regional heterogeneity. This research proposes policy recommendations focusing on accelerating the ICT industry transformation, leveraging the spillover and technological advantages of agglomeration, and enhancing regional cooperation.