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Explore the interaction and spatial spillover effects between industrial agglomeration, environmental regulation, and high‐quality development of agriculture and rural areas, formulate appropriate industrial and environmental policies for different regions, guide the orderly agglomeration of agricultural sectors, and adjust the intensity of environmental regulation, which will help achieve high‐quality development. This manuscript firstly deconstructs the internal logic among industrial agglomeration, environmental regulation, and high‐quality development. Secondly, the Super‐SBM model is used to calculate the high‐quality development efficiency in rural China from 2000 to 2020. Finally, the Spatial Dubin Model (SDM) is used to analyze the spatial spillover effects and regional differences of industrial agglomeration, environmental regulation, and their integration impact on high‐quality development in rural China. The results show that: ① From 2000 to 2020, the high‐quality development efficiency shows a downward trend in fluctuation, and high‐quality development needs to be improved. ② There are significant regional differences in the effects of industrial agglomeration and environmental regulation on the high‐quality development efficiency. Industrial specialization agglomeration can effectively improve the high‐quality development efficiency, while industrial diversification agglomeration has uncertain effects. ③ The integration of environmental regulation and diversification agglomeration inhibits the high‐quality development efficiency, while the integration with specialization agglomeration has a stronger effect on improving high‐quality development efficiency. It is necessary to further enhance the effect of diversification agglomeration. [ABSTRACT FROM AUTHOR]

Research on the impact of digital inclusive finance on the development of rural revitalization and its spatial spillover effects can offer valuable insights for comprehensively promoting rural revitalization and advancing common prosperity. Based on spatial panel data from 31 provinces in China from 2011 to 2021, the study uses the entropy weight method to scientifically and accurately measure the comprehensive index of rural revitalization. Additionally, the study employs the Spatial Durbin Model for empirical research, considering the issue from spatial and temporal perspectives and utilizing MATLAB software. The research findings are as follows: First, the overall development of rural revitalization and digital inclusive finance in China demonstrates significant spatial dependence, locally displaying high-high agglomeration and low-low agglomeration. Second, digital inclusive finance can significantly promote the development of rural revitalization--the higher the degree of digital inclusive finance, the greater the assistance to the development of rural revitalization. Third, both digital inclusive finance and rural revitalization development exhibit positive spatial spillover effects. Fourth, fiscal support for agriculture and the degree of openness to the outside world help promote rural revitalization; the level of economic development has a positive spatial spillover effect on the development of rural revitalization. Based on these conclusions, it is inferred that the development of rural revitalization is inseparable from strong support for digital inclusive finance and other policies. [ABSTRACT FROM AUTHOR]

Background: The 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. Methods: The 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. Results: From 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. Conclusions: The 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. [ABSTRACT FROM AUTHOR]

Based on the perspective of the innovation value chain, sci-tech innovation is divided into two stages: R&D and achievement transformation. This paper uses panel data from 25 provinces in China as the sample. We utilize a two-way fixed effect model, spatial Dubin model, and panel threshold model to discuss the impact of two-stage innovation efficiency on the value of the green brand, the spatial effect of this impact, and the threshold role of intellectual property protection in the process. The results indicate that: (1) the two stages of innovation efficiency have a positive impact on the value of green brands, and the effect of the eastern region is significantly better than that of the central and western regions. (2) The spatial spillover effect of the two stages of regional innovation efficiency on the value of green brands is evident, especially in the eastern region. (3) The innovation value chain has a pronounced spillover effect. (4) The single threshold effect of intellectual property protection is significant. When the threshold is crossed, the positive impact of the two stages of innovation efficiency on the value of green brands is significantly enhanced. (5) The influence of economic development level, openness, market size, and marketization degree on the value of green brands shows remarkable regional differences. In conclusion, this study contributes to understanding green brands' growth and provides important implications for developing independent brands in various regions of China. [ABSTRACT FROM AUTHOR]

The coupling and coordinated development of scientific-technological innovation and new urbanization have important practical value for the high-quality development of Yellow River Basin. By discussing the mechanism of interactive development of scientific-technological innovation and new urbanization, and constructing an evaluation index system, the entropy method and the coupling coordination model were used to calculate the comprehensive development index of scientific-technological innovation and new urbanization, and coupling coordination degree of 9 provinces, and the spatial evolution and connection of the coupling coordination degree of 9 provinces in Yellow River Basin from 2005 to 2019 were analyzed by means of Moran index, Dagum Gini coefficient and kernel density estimation method, and the spatial Dubin model was used to analyze the influencing factors of the coupling coordination degree. The results show that the comprehensive development index and coupling coordination degree of scientific-technological innovation and new urbanization in 9 provinces show a stable and rising trend; there is a positive spatial autocorrelation between the coupling coordination degrees of each province, but the overall difference in coupling coordination degree continues to increase; narrowing the gaps within the upstream area and between the upstream and downstream areas, is the key to improve the spatial coordination linkage mechanism; the absolute difference of coupling coordination degrees among upstream, midstream and downstream areas in Yellow River Basin is decreasing; innovation input, patent output, government support and higher education level have a positive role in promoting the coupling coordination degree of the province, and the income level of urban resident has a negative inhibition effect on the coupling coordination degree and the overall coupling coordination degree of the neighboring provinces. Improving the coupling coordination degree needs to be achieved by enhancing the ability of scientific-technological innovation, improving the level of new urbanization, strengthening regional connection, narrowing the development gap and adjusting the influencing factors. [ABSTRACT FROM AUTHOR]

• We study the direct and spatial impacts of China's human capital structure on regional GTFP. • The structure of human capital is divided into academic education and non-academic education. • The mechanisms of GTFP influenced by different human capital structure was explored. • The mechanisms include technological progress, knowledge spillover, industrial upgrading and environmental regulation. China's rapid economic growth has resulted in resource consumption and environmental degradation. Only by improving green total factor productivity (GTFP) can China's economic growth become stable and sustainable. Existing studies have demonstrated that total human capital can improve GTFP, but such studies have ignored the heterogeneous influence of human capital structure, and there is a lack research on how human capital structure affects GTFP spatially. Therefore, based on China's provincial panel data from 2000 to 2018, this paper uses the Super-SBM-ML model to evaluate GTFP. The human capital is divided into that having academic education and that having only non-academic education, and then the spatial Dubin model is used to estimate the relationship between GTFP, academic education and non-academic education human capital. The empirical results show that different levels of human capital have different effects on GTFP. Some types of human capital (such as tertiary-level educational human capital) promote local GTFP, while other types (such as primary-level educational human capital) inhibit it. This indicates that local governments should optimize the human capital structure in order to promote green development. In addition, although there are effects of human capital on the spatial spillover of GTFP, those effects are not significant for some types of human capital, indicating that local circulation channels for human capital are not smooth, so it is necessary to improve local talent policies and strengthen regional cooperation and development so that human capital can promote the rapid development of a green economy. Finally, this paper proves that educational human capital can alter the impact on GTFP of technological progress, knowledge spillover, industrial upgrading and environmental regulation. [ABSTRACT FROM AUTHOR]

How to correctly use the spatial measurement model to analyze the differences, causes, and influencing factors of regional finance, and to establish corresponding development strategies to keep the regional financial differences in an ideal state is one of the problems that the current regional financial development needs to face. Based on the spatial Dubin model (SEM), spatial Dubin error model (SDEM), generalized spatial Dubin model (GSAM), etc. in the spatial measurement method, this paper compares the actual effects of different models, and combines the machine learning algorithm to improve the model algorithm, and intelligently analyzes the spillover effects of regional financial space. After constructing the model, this paper takes area A as an example to analyze the spillover effect of regional financial space through the model constructed in this paper. In addition, this paper conducts an empirical analysis on the influencing factors of regional financial development and proposes corresponding strategies to provide support for the construction of regional financial centers. [ABSTRACT FROM AUTHOR]

High-tech industrial agglomeration is conducive to boosting technological progress, promoting industrial structure upgrading and realizing economic transformation, and certainly affects the overall industrial environmental efficiency. However, few recent studies have focused on its impact on industrial environmental efficiency from a green perspective. In the context of promoting the development of green economy, it is of great significance to clarify the links between high-tech industrial agglomeration and industrial environmental efficiency. In this research, we first analyzed the theoretical mechanism of the impact of high-tech industrial agglomeration and its spatial spillover effects on industrial environmental efficiency and then made an empirical analysis based on the panel data of 29 provinces and cities in China from 2003 to 2016. During the research, Super-DEA method, ESDA method and spatial Dubin model are used. The result shows that: (1) There is a significant spatial positive correlation between China's industrial environmental efficiency and high-tech industrial agglomeration; (2) high-tech industrial agglomeration has improved the local industrial productivity and industrial technology level through scale effects and technical effects, which has accordingly significantly enhanced the corresponding environmental efficiency; (3) through the association of regional industries, the cross-regional cooperation of enterprises and the formation of innovation networks, high-tech industrial agglomeration promotes the spillover of knowledge and technology among regions, improves the level of industrial technology in neighboring regions, and enhances the industrial environmental efficiency in neighboring regions. All these three is helpful to re-evaluate the development mode of high-tech industry agglomeration and to formulate relevant government policies. [ABSTRACT FROM AUTHOR]

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. • Public investment in education has an impact on both sub-processes of the innovation value chain, but with different impact mechanisms. • Public investment in education relies mainly on direct effects on the R&D sub-process and spillover effects on the commercialization sub-process. • There is regional heterogeneity in the impact of public investment in education on both sub-processes of the regional innovation value chain. • Public investment in education may not contribute to regional innovation value chains through advanced human capital structures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Interaction between economic agglomeration and carbon emissions was explored at the county spatial scale. • Constructing a spatial panel Dubin model by using both geographic and economic matrix. • A three-stage character of economic agglomeration was found instead of previous studies' two-stage. • Economic agglomeration and carbon emissions show an "inverted N-shaped" relationship at the county level. • Agglomeration environment externalities are heterogeneous under different industry-led conditions. 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. [ABSTRACT FROM AUTHOR]

Shortage of water resources has become an important factor that restricts economic and social development. It is of great theoretical and practical significance to examine the current situation of water resources utilization and explore the effective driving force for sustainable development of water resources. Based on the water footprint perspective, this paper calculates the water footprint intensity of 31 provinces in China from 2006 to 2015 respectively. The spatial data analysis (ESDA) is used to examine the evolution of space-time pattern. Considering the rough nature of the spatial description of the method, the spatial Dubin model is introduced to refine and with the help of the model, we analyse the influencing factors. The result shows that the spatial agglomeration effect of water footprint intensity in China is significant and with transition, however, it is mainly based on type VI, and its spatial structure has a certain path dependence. Currently, population quantity is still a major driving factor of water footprint intensity in China, while the urbanization rate and the degree of opening to the outside world play an active role in reducing the water footprint strength. There is an "inverted N" Kuznets curve in China's water footprint intensity In most provinces, the intensity of water footprint is between the first and second inflection points. Beijing, Tianjin, and Shanghai have crossed the second inflection points, and they are in the stage of the decline of footprint intensity, while some of the underdeveloped areas in the western region have not crossed the first turning point. [ABSTRACT FROM AUTHOR]

• The text used SDM and Hurst index to assess the Yangtze Delta ECC time-space effect. • The Yangtze Delta ECC shows varying spatial gather with towns mostly cluster H-H,L-L. • SDM fit spacetime dual fixed effect shows ECC has spillover,local-to-city is 1:0.507. • Hurst index predicts Yangtze Delta ECC evolution,central index exceeds peripheral. 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 Spatial Dubin 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. [ABSTRACT FROM AUTHOR]

With the development of urbanization, ecological space is increasingly disturbed and invaded by human activities. From 2000 to 2020, the value of ecosystem services in China decreased from 28.05 to 27.77 trillion yuan. This paper combines the natural and social data of 370 administrative units in China from 2000 to 2020 with the GTWR model. Our results show that in most regions of China, the coefficient value of factor GDP or POP is significantly higher than that of other factors. The regions with higher natural factors coefficient value are concentrated in the northwest and southeast. In addition, this study found that the driving factors and ESV have geographical spillover effects. The above conclusions have important practical significance for planning policies according to local conditions. • The coefficients of natural and anthropogenic factors were compared. • Natural and anthropogenic factors' coefficients dominate regions showing regional differentiation. • Excessive anthropogenic disturbance can negatively impact ecosystem services. • GTWR model was used to analyze Spatial-temporal changes of ecosystem services. • Ecosystem service value has spillover effect in geographical space. [ABSTRACT FROM AUTHOR]

In this paper, the panel data of 30 provinces in China from 2011 to 2019 are analyzed by the spatial measure model and the threshold regression model. The results show that the air quality level is positively correlated with green finance, but there is no spatial effect. The spatial effect of the three influencing factors, including the degree of openness, the level of infrastructure, and the level of education, is the crowding-out effect. At the same time, variables such as human resource level, air quality, and infrastructure construction level all have threshold effects in the relationship between green finance and economic development. The research conclusions suggested that local governments at all levels should formulate policies according to the actual situation to promote the development of provinces' intensive, intelligent, and green development, and build a regionally-linked green finance development model, thereby promoting the improvement of green finance. [ABSTRACT FROM AUTHOR]

Due to the pressure of global ecological degradation, the coordination of economic increase and ecological protection has drawn attention from policymakers and practitioners. Green economic efficiency (GEE) is a comprehensive index to measure economic, social, and environmental development. As China is the second-biggest economy in the world with high-energy consumption, it is necessary to investigate its green economy efficiency. In this paper, we innovatively adopt a super-SBM (slacks-based measure) model with undesirable outputs to calculate the GEE in 30 provinces of China between 2008 and 2017, and then comprehensively apply a spatial Dubin model (SDM) to investigated its influencing factors. The results showed that the overall GEE in China during the study period was at a low level with significant regional differences. The inter-regional GEE generally showed a gradient decreasing pattern of "East-Middle-West", which demonstrates a gradual decline from the East to the West in China. The trend of the national GEE initially dropped and then gradually stabilized over the study period. Foreign trade dependence and direct investment had significant positive effects on the GEE, while the secondary industry and urbanization level had a significant negative effect. Unlabelled Image • Studying the concept of Green Economy Efficiency (GEE) • Coupling the Super-SBM model with the Spatial Dubin Model and Global Moran's I index • Finding the GEE in a gradient decreasing pattern of "East-Middle-West" • Revealing foreign trade dependency and direct investment with positive effects on GEE • Demonstrating secondary industry and urbanization level with significant negative effect [ABSTRACT FROM AUTHOR]