Spatial Dubin Model Research Articles

THE IMPACT OF DIGITAL ECONOMY DEVELOPMENT ON CARBON EMISSION INTENSITY REDUCTION: EVIDENCE FROM CHINA

Digital economy development is important for promoting industrial upgrades and high-quality economic growth. As excessive carbon emissions are a threat to sustainable economic growth, whether digital economy development contributes to reducing carbon emission intensity has gained attention. This study employs the fixed effects model and spatial Dubin model to examine the effect of digital economy development in 275 cities in China on the reduction of carbon emissions intensity. The results show effectiveness in reducing carbon intensity with a spatial spillover effect to neighboring areas. Urban economic primacy plays a positive modulating role in carbon reduction. Policy implications are discussed.

Exploring associations between built environment and crash risk of children in school commuting

Understanding how built environment are associated with crash risk (CR) in school commuting is essential to improving travel safety through land use and transportation policies. Scholars often assume that this relationship is consistent across space, but this may lead to inconsistent estimates. To address this issue, using data in Shenzhen, China, the data covers traffic accident data of children taken from police incident reports and supplemented with local land use, transportation network and specific school information. The measurement model of crash scale was conducted to represent crash severity, and the CR was further quantified. The study applies three models, spatial dubin model (SDM), geographically weighted regression (GWR), and mixed GWR (MGWR), to explore spatio-temporal heterogeneity relationships between built environment attributes and CR of children in school commuting. The findings reveal that the crash scale can better represent crash severity of school commuting than a single indicator. Policy interventions should be targeted at specific spatial scales, school types, and time windows to effectively improve travel safety. However, there are some common findings. It is recommended to use a scale of 200 m to explain the relationship between the variables. The MGWR model outperforms the other two models. To reduce CR, it is important to consider lower road network density, a reasonable layout of educational facilities, fewer bus routes, and more on-street parking spaces. Our findings can help to enrich the understanding of associations between land use and CR of children, as well as offer local planning and operating guidance for creating child-friendly environment.

Analyzing the Impact of the Labor Market on Company Location Selection Using Spatial Econometric Models

Abstract This study employed spatial econometric models to analyze the company registration site selection in various counties and cities of Taiwan from 2002 to 2021 and examined its correlation with the labor market. The results revealed that over the past two decades, the number of company registrations in Taiwan has consistently risen, with a growth rate of 60.61%. Metropolitan areas such as Taipei City, New Taipei City, Taichung City, Kaohsiung City, and Taoyuan City took the lead in terms of registration numbers. Furthermore, the study found that local labor market factors and spatial spillover effects significantly influence company location decisions. Specifically, using the spatial Durbin model, it was discovered that an increase in unemployment rates negatively impacts company registrations in neighboring counties and cities; growth in employment numbers has a positive overall effect on company registrations; a rise in the proportion of industrial workers may be detrimental to company startups; a higher proportion of employees with tertiary education positively affects company registrations; an increase in the proportion of employed individuals aged 45 to 64 negatively impacts company registrations. The research findings offer valuable insights for policymakers, assisting in the creation of a more conducive entrepreneurial environment. JEL classification numbers: C31, J21. Keywords: Spatial autocorrelation, Spatial Dubin model, Spillover effects, Labor market, Number of companies.

How does digital finance affect sustainable economic growth? Evidence from China.

Digital finance is an innovative financial model of great significance for sustainable economic growth. By constructing indicators of sustainable economic growth, we explore the impact of digital finance on sustainable economic growth using the fixed effect model, mediating effect model, threshold regression model, and dynamic spatial Dubin model. The study finds that digital finance can drive sustainable economic growth, and the robustness and endogenous treatment results strongly verify this. Digital finance promotes sustainable growth mainly through technological innovation. In addition, with technological innovation and the development of renewable energy, there is a significant nonlinear relationship between digital finance and sustainable economic growth. Finally, the spatial spillover effect results show that digital finance's impact on sustainable economic growth has a positive effect, whether it is a direct effect or an indirect effect. This article provides possible ideas for digital finance to promote sustainable economic growth.

Spatial Statistical Analysis and Risk Factor Identification of COVID-19 in China

Objectives: In this paper, we discuss the spatial aggregation and evolution of COVID-19 in China and identify the risk factors affecting the spread of the disease. The aim is to provide insights that can be used to implement timely and effective interventions in the face of similar infectious diseases in the future and to ensure the safety of people around the world. Methods: We used spatial statistics and measurement methods to analyze the spatial aggregation and evolution of COVID-19 in China. We carried out spatial visualization mapping and spatial statistical analysis on the data of the epidemic. Various risk factors of COVID-19 spread at the provincial level in China were comprehensively discussed by combining geographic detector and spatial Dubin model. Results: The analysis revealed the spatial aggregation and evolution patterns of COVID-19 in China and the risk factors affecting the spread of the disease, including population density, transportation network, and climate factors. The geographic detector and spatial Dubin model were effective in identifying the risk factors, and the results provide valuable insights for implementing timely and effective interventions. Conclusions: We emphasize the importance of timely and effective interventions in the face of infectious diseases such as COVID-19. Our results can raise awareness of prevention and control and respond to potential outbreaks of similar infectious diseases in the future. The study provides a deep understanding of COVID-19 and its spatial patterns, and the insights gained can safeguard both lives and property worldwide.

How does renewable energy consumption affect carbon emission intensity? Temporal-spatial impact analysis in China

Whether renewable energy consumption is beneficial to reducing CO2 emission intensity and carbon emissions in the context of the goal of “carbon peak and carbon neutrality” in China. To prove this conjecture, considering the spatial spillover effect of carbon dioxide, this paper adopts the spatial Dubin model for empirical analysis based on the panel data of China's 30 provinces over the period 2002–2019. This study explores the CO2 intensity mitigation effect of China's renewable energy consumption and further investigates the mediation role of technological innovation, industrial structure upgrading and optimization, and energy efficiency in the nexus between renewable energy consumption and carbon intensity. The main findings present that: (1) China's carbon emission intensity is characterized by significant spatial correlation; (2) Renewable energy consumption can significantly reduce carbon intensity and per capita carbon emissions; (3) The mediation effect of renewable energy consumption in reducing carbon emission intensity is to promote technological innovation and industrial structure upgrading, as well as improve energy efficiency. Based on the conclusions, this paper offers several policy implications for governments to reduce CO2 emission intensity.

Spatial impact of digital finance on carbon productivity

Low carbon productivity has been identified as a key direction for China’s future development. As an important driving force for economic growth, the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved. Based on the carbon productivity measured by panel data from 201 cities for the period 2011–2020, this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. First, the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region, and both productivity and digital finance are characterized by high (low)–high (low) dotted spatial agglomeration. Second, digital finance can significantly improve carbon productivity via two transmission channels: the human capital and marketization effects. At the same time, digital finance exerts a spatial spillover effect on carbon productivity, and rising local digital finance levels will increase carbon productivity in neighboring areas. Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant. Third, fixed-asset investment has a positive nonlinear moderating effect on digital finance, thus improving carbon productivity. When the per capita investment in fixed assets does not exceed 682.73 yuan, digital finance exerts only a limit pulling effect on carbon productivity; when it is higher than this value, the pulling effect is intensified.

Spatial effects of market integration on the industrial agglomeration of agricultural products: evidence from China.

The spatial effects of agricultural market integration on industrial agglomeration are an important field of regional economic. This paper collected the data of agricultural market integration and industrial agglomeration in 31 provinces in China from 2010 to 2019, analyzed the spatial effects of the two by constructing a dynamic spatial Dubin model, and explored its long-term and short-term effects of the spatial effects. The results show the following: (1) the primary terms of agricultural market integration were negative and the secondary terms were positive. The impact of agricultural market integration on local industrial agglomeration had a "U-shaped" characteristic. Whether in the short-term or long-term, there was a significant direct effect of "suppression to promotion." (2) The agricultural market integration had a spatial spillover effect on industrial agglomeration in the neighboring areas. This effect had an "inverted U-shaped" characteristic. Whether in the short-term or long-term, there was a prominent spatial spillover effect of "promotion to suppression." (3) For direct effects, the short-term direct effects of agricultural market integration on industrial agglomeration were - 0.0452 and 0.0077, and the long-term direct effects were - 0.2430 and 0.0419. For spatial spillover effects, the short-term spatial spillover effects were 0.0983 and - 0.0179, and the long-term spatial spillover effects were 0.4554 and - 0.0827. The long-term effects were greater than the short-term effects. This paper provides empirical evidence for the effects of agricultural market integration on industrial agglomeration in different regions, and exploring the development of agricultural agglomeration in the long-term.

Vocational Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model

Abstract Based on the spatial Dubin model, this paper theoretically discusses the influence of collaborative innovation on regional economic growth. The paper studies the industrial structure of enterprises by using the spatial Dubin method established by different regions and social and economic factors. It is found that collaborative innovation has a spillover effect on domestic industrial development both internally and externally. The regional migration of creative elements within the region mainly causes space overflow. And it has the characteristics of regional decline. This conclusion has specific guiding significance to the mechanism of enterprise collaborative innovation. This mode plays a positive role in promoting benign exchanges between regions and realizing industrial development and upgrading.

Collaborative innovation evolution of the logistics and manufacturing industry in China.

The logistics industry and the manufacturing industry are mutually productive factors and service objects, which determines that the two must develop cooperatively. In the increasingly fierce market competition, open collaborative innovation is more conducive to improving the level of linkage between the logistics industry and the manufacturing industry, and promoting industrial development. Based on the patent data of 284 prefecture-level cities in China from 2006 to 2020, this paper uses Gis spatial analysis, spatial Dubin model and other methods to analyze the collaborative innovation between the logistics industry and manufacturing industry. The results lead to several conclusions. (i) The overall collaborative innovation level is not high, and in terms of the evolution cycle, collaborative innovation has experienced three stages: embryo, rapid development and stable development. (ii) The spatial agglomeration characteristics of the collaborative innovation between the two industries are more and more obvious, and the Yangtze River Delta urban agglomeration and the middle reaches of the Yangtze River urban agglomeration play an important role in the collaborative innovation between the two industries. (iii) The hot spots of collaborative innovation between the two industries are concentrated in the eastern and northern coastal areas, while the cold spots are concentrated in the south of the northwest and southwest areas in the late stage of the study. (iiii) The positive influencing factors of local collaborative innovation between the two industries include economic development, scientific and technological level, government behavior, and employment, while the negative influencing factors include information technology level, and logistics infrastructure. Economic development has a negative spatial spillover effect on surrounding areas, while the spatial spillover effect of scientific and technological level is significantly positive. This article aims to explore the current situation and influencing factors of collaborative innovation between the two industries, with a view to proposing countermeasures and suggestions for improving the level of collaborative innovation between the two industries, and also providing new ideas for research on cross industry collaborative innovation.

Study on the spatial spillover effect and path mechanism of green finance development on China's energy structure transformation

Green finance is essential for supporting the development of green, low-carbon projects and preventing environmental and climate financial risks. Investigating and studying whether and how it can aid in implementing energy transformation is worthwhile. This paper uses 30 provinces from 2004 to 2020 as research samples, the entropy method to construct a green finance measurement system, and the weighted multidimensional vector angle index method to construct an energy low-carbon transformation index based on the actual situation of green finance in China and the existing theoretical research basis. We systematically analyzed each province's spatiotemporal evolution characteristics of green finance and energy structure transformation by constructing a fixed effect model, a GMM model, an intermediary effect model, and a spatial Dubin model. We empirically tested the effect direction, internal mechanism, spatial spillover, and regional heterogeneity of green finance development (GFD) on energy structure transformation (EST) to investigate the low-carbon transformation of energy structures. The findings indicate the following:(1) Green finance plays a positive role in promoting the transformation and development of China's energy structure. (2) The influence of green finance development on the transformation of China's energy structure has significant spatial spillover, exhibiting “local and neighborhood” reciprocity. Through the spatial spillover effect, green finance can encourage the low-carbon transformation of local energy structures and increase the level of low-carbon transformation in neighboring areas. (3) The improvement of the level of green technology innovation and the intensity of environmental regulations are essential channels for green finance to support the low-carbon transformation of the energy structure, and regional heterogeneity will alter this transmission mechanism. The findings of this study provide empirical evidence for the vital role of green finance in achieving the double dividend of economic growth and low-carbon emission reduction. This study provides a new perspective for constructing a green finance system and advancing the dual-carbon objective and a resource for formulating effective regional energy transformation policies.

Yangtze River Delta in China space-time effect research on influencing factors for ecological civilization construction

In the strategic context of “vigorously promoting the construction of ecological civilization and following the Chinese modernization road,” it is of theoretical and practical significance to investigate the influencing factors and spatial and temporal effects of the level of ecological civilization construction (ECC) in view of the lack of scientific understanding of the spatial and temporal evolution of ECC. The ECC level of the Yangtze River Delta (YRD) territory was assessed from 2010 to 2020 based on four different factors: economy, culture, society, and environment. The SpatialDubin model, in conjunction with the Hurst index, was used to examine the spatial impact of the ECC level. The results indicate that the YRD’s ECC level exhibits high spatial agglomeration features with rising agglomeration intensity and significant spatial autocorrelation. The degree of ECC in the YRD region has a substantial regional spillover effect. Cities nearby are expected to grow by 0.507% for every 1% increase in the local ECC level. The YRD region showed a high Hurst index of the ECC level; the sustainability sequence accounted for 80.49% of this index, and the Hurst index of the cities in the core portion of the region was generally higher than that of the marginal cities. The two facets of time and space, as well as the spatial spillover and lag impact of ecological civilization formation, should be considered in the future. To support the high-level ECC growth in the entire YRD region, it is vital to increase communication and collaboration between cities in the YRD territory.

Research on the dynamic evolution and influence factors of industrial energy efficiency in China Yangtze River Economic Belt

Improving the Yangtze River Economic Belt's industrial energy efficiency is not only an important measure to alleviate China's energy shortages but also a drive to promote green economic development. The Super-EBM model, Malmquist productivity index, exploratory spatial data analysis, and Spatial Dubin model are used in this article to investigate the spatial-temporal dynamic development characteristics and influencing factors of industrial energy efficiency in 108 cities of the Yangtze River Economic Belt from 2011 to 2020. The findings demonstrate that the industrial energy efficiency of the Yangtze River Economic Belt and its three urban agglomerations went through three stages, including the “oscillation period,” “stability period,” and “enhancement period,” and decreases from east to west in the spatial dimension. The Yangtze River Delta urban agglomeration has the highest industrial energy efficiency, followed by the Middle-reach Yangtze River urban agglomeration, and the Chengdu-Chongqing urban agglomeration is the lowest. Further, this article identifies seven influencing factors including government intervention, industrial structure, degree of openness, R&D investment, urbanization, economic development, and environmental regulation. This article provides suggestions for industrial energy efficiency improvement.

Examining the Relationship between Transportation Infrastructure, Urbanization Level and Rural-Urban Income Gap in China

The development of transportation infrastructure plays a pivotal role in the regional economy from multiple dimensions. The aim of this paper is to examine the relationship between transportation infrastructure development and income inequality in urban and rural areas of China. The study utilizes panel data from 30 provinces, spanning the years 2010 to 2020, and employs the spatial Dubin model to measure and test the impact of transportation infrastructure on the urban-rural income gap. Furthermore, an intermediary effect test method is used to investigate the potential mediating effect of urbanization in this relationship. The results indicate that transportation infrastructure has a significantly negative direct, indirect, and total effect on the urban-rural income gap, with the indirect effect being greater than the direct effect. This suggests that transportation infrastructure can effectively reduce income disparities, with a noticeable spatial spillover effect. The level of urbanization plays a significant intermediary effect on the effect of transportation infrastructure on the urban-rural income gap, highlighting the role of transportation infrastructure in improving urbanization and narrowing income disparities. These findings underscore the importance of enhancing both the level of urbanization and cooperation between neighbouring regions in order to maximize the benefits of transportation infrastructure development for reducing income disparities and promoting regional balance in China.

Spatio-Temporal Differentiations and Influence Factors in China’s Grain Supply Chain Resilience

Based on Chinese provincial panel data (2011–2020), the CRITIC-EWM, kernel density estimation, Dagum Gini coefficient and a spatial Dubin model were used for analyzing endogenous and exogenous factors to enhance grain supply chain resilience. The results illustrated the following: (1) Ensuring the resilience of the grain supply chain’s ability to digest, recover and learn was fundamental to internal system robustness. (2) Overall, China’s grain supply chain resilience increased year over year, but the growth rate was low and showed an attenuation trend of east-middle-west. (3) There was a significant spatial positive correlation among provinces and grain supply chain resilience, however, the large gap and slow development led to the formation of polarization with high-high and low-low clusters. (4) The exogenous drivers were all significant and had a significant positive spillover effect on the grain supply chain resilience. (5) Subregional regressions reflected the heterogeneity of the influence factors, which highlighted the implementation of targeted strategies as the key to achieving synergy.

A View of Industrial Agglomeration, Air Pollution and Economic Sustainability from Spatial Econometric Analysis of 273 Cities in China

Industrial agglomeration in a region changes the economic structure, strategic layout and resource status of a city, and has an important impact on sustainable economic development. The relationship between industrial agglomeration, air pollution and economic sustainability is a key issue concerning the high-quality development of national economy. China is a developing country that once experienced severe air pollution. Now, the Chinese government is aiming to achieve the goal of sustainable and high-quality economic development in China. In this paper, a spatial Dubin model was developed to study the relationship between industry, environment and the economy. The statistical analysis used the air pollutant data of 273 prefecture-level cities in China from 2015 to 2018. The results showed that: (1) there was a positive U-shaped nonlinear relationship between industrial agglomeration and sustainable economic development, and there was a spatial spillover effect. (2) There was a positive U-shaped nonlinear relationship between air pollution and sustainable economic development, and there was a spatial spillover effect between them. (3) The effect of industrial agglomeration on sustainable economic development was influenced by air pollution, an intermediary variable. The existence of air pollution weakens the promoting effect of industrial agglomeration on sustainable economic development.

Does Environmental Regulation Have an Employment Dividend? Evidence from China

Environmental regulations not only benefit environmental improvement but may also have a potential employment dividend, which is important for sustainable economic development. Based on the provincial panel data of China from 1997 to 2019, the spatial Dubin model is used to analyze the spatial spillover effects of environmental regulation on employment. From the findings, there is a significant spatial agglomeration characteristic in the employment scale. The environmental regulation positively influences the local employment scale, while there is a negative spatial effect on employment in neighboring regions. With regards to the regional heterogeneity analysis, environmental regulation has a spatial negative correlation with employment in coastal areas and less polluted areas. Additionally, in inland areas and less polluted areas, environmental regulation has a significant positive impact on the employment scale. Meanwhile, market-based environmental regulation has the potential to expand the employment scale in local and neighboring areas, while command-and-control environmental regulation impacts negatively on employment in neighboring areas. This study found that environmental regulation has an employment dividend. These findings reveal the spatial dependence between environmental regulation and employment, which will help policy makers consider the environmental and employment effects of environmental regulation more comprehensively. Therefore, the government should formulate targeted policies under regional differences to promote high-quality employment and construct a multiple governance environmental regulatory system.

How Digital Transformation Affects Urban Resilience: Empirical Evidence from the Yangtze River Delta Region

The current regional development crisis and opportunities coexist. On the one hand, the economic environment is complex and volatile, with more and more crisis shocks testing the resilience of urban development, while on the other hand, the rapid development of science and technology such as the digital economy has affected all areas of the economy, life, and governance of cities, bringing opportunities for urban development. The use of digital transformation to enhance urban resilience is therefore an obvious and important topic. Based on panel data of 27 cities in the Yangtze River Delta from 2011 to 2020, this study empirically analyses the impact of digital transformation on urban resilience by constructing a fixed effects model, a mediated effects model and a spatial Du bin model. The study finds that: (1) In terms of time, the urban resilience and digital transformation capacity of the Yangtze River Delta region are both on the rise; From a spatial point of view, the urban resilience of the Yangtze River Delta region basically shows a spatial distribution pattern of “high in the central cities, and low in the peripheral cities”, while the digital transformation capacity basically shows a pattern of “high in the east-central region, and low in the west”. (2) Digital transformation has a significant positive impact on improving urban resilience; (3) Digital transformation enhances urban resilience through three main paths: technological innovation capacity, new economic sector development momentum, and innovation and entrepreneurship development vitality; (4) Digital transformation has a spatial spillover effect on the urban resilience of neighboring regions.

The spillover effect of green finance development on rural revitalization: an empirical analysis based on China's provincial panel data.

With the overall victory of poverty alleviation in China, the focus of rural work has been transformed into rural revitalization. Therefore, based on the panel data of 30 provinces and cities in China spanning 2011 to 2019, this research used the entropy-TOPSIS method to calculate the weights of each index of the two rural revitalization and green finance systems. This research also constructs the spatial Dubin model to empirically analyze the direct effects and spatial spillover effects of green finance development on the level of rural revitalization. Additionally, this research calculates the weight of each indicator of rural revitalization and green finance using entropy-weighted TOPSIS. This research reveals that the current state of green finance is not conducive to increasing local rural revitalization and does not significantly affect all provinces. Further, the number of human resources can improve the local level of rural revitalization, not the entire province. These dynamics benefit the growth of local rural revitalization in the surrounding areas if employment and technology levels are developed domestically. Moreover, this research reveals that education level and air quality have a spatial crowding effect on rural revitalization. Thus, when developing rural revitalization and development policies, it is vital to prioritize the high-quality development of finance to be closely monitored by local governments at the respective levels. Furthermore, the stakeholders must pay critical attention to the connection between supply and demand and between financial institutions and agricultural enterprises in the provinces. Again, the policymakers must also increase policy preference, deepen regional economic cooperation, and improve the supply of essential rural elements to play a more significant role in green finance and support rural revitalization.

Can environmental information disclosure attract FDI? Evidence from PITI project

Based on the balanced panel data of 260 prefecture-level cities in China from 2005 to 2016, and the quasi-natural experiment of the Pollution Information Transparency Index (PITI) project implemented in 110 prefecture-level cities in 2009, this paper uses the spatial difference-in-differences (SDID) model and the spatial Dubin model (SDM) model to investigate the FDI attraction effect of environmental information disclosure (EID). The results show that EID will significantly promote the inflow of FDI in cities, and this effect will be stronger with the increasing disclosure extent. EID has a siphon effect on FDI, that is, a city that has a better EID will attract more FDI than its neighbors, further intensifying the competition effect of FDI among cities with similar geographical distance, levels of economic development, and Internet penetration. The attraction effect of EID on FDI is mainly present in polluting cities and non-resource-based cities, while the impact on clean and resource-based cities is not significant.