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Cities are the core carriers and key positions to achieve the dual carbon goals. It is of great significance to explore whether promoting urbanization can improve carbon emission performance, which is of great significance to comprehensively promote the goal of carbon neutrality. Based on the panel data from 2006 to 2021, this paper analyzes the spatial autocorrelation of carbon emission performance per unit space and the impact mechanism of urbanization process on it. The fixed-effect model was further used to identify the influencing factors of spatial carbon emission performance. The results show that: 1) China's carbon emission performance per unit space is declining year by year. 2) There is a strong positive spatial correlation and stable path dependence on the performance of carbon emissions per unit space in each region. 3) To a certain extent, increasing the level of urbanization will reduce the carbon emission performance per unit space. 4) The urbanization process has a spatial spillover effect on the carbon emission performance per unit space of surrounding provinces, and the spatial spillover effect of industrial structure and energy consumption structure is more obvious than that of economic level, population density and urbanization rate. Based on the conclusions, this paper puts forward specific policy suggestions to reduce the carbon emission performance per unit space to help the low-carbon development of cities. [ABSTRACT FROM AUTHOR]

With the rapid developm the application of digital ent of the new generation of information and communication technologies (ICT), the state-level governance model has undergone profound changes. It is worth pondering whether this change will have an impact on the carbon peak and carbon neutrality goals. Therefore, we collected data from 51 global economies that have proposed carbon neutrality targets from 2003 to 2020 to build a series of spatial measurement models and explored the impact of e-government construction on carbon emission intensity (CEI). Then, we further considered regional heterogeneity factors, grouped the selected countries into Asian, European, and American countries, and verified the differences between e-government construction and CEI in different regions. Additionally, on the basis of the spatial mediation effect model, this paper analyzed the industrial structure which played an intermediary role in achieving target for reducing CEI through e-government construction. The main findings are as follows. (1) A higher level of e-government construction will result in lower-carbon emissions intensity. (2) Spatial spillover effect regression results showed that the construction of e-government in neighboring countries also significantly inhibits the carbon emission intensity of the host country. (3) There was significant regional heterogeneity in the impact of e-governments on CEI; in Asia, the construction of e-governments significantly reduced CEI, but no significant reductions were observed in Europe and America. (4) E-governments influenced CEI by optimizing the industrial structure. The construction of e-governments was effective in Asia and Europe, while intermediary effect test was failed to pass in the Americas. This study mainly focused on the environmental benefits brought by the construction of e-governments, and the obtained results would provide an important reference for the achievement of the dual-carbon goals in countries worldwide. [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]

IntroductionChina is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China.MethodsThis study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency.ResultsThe key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran’s Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces’ values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume.DiscussionTo enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents.

Abstract 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.

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. [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]

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. [ABSTRACT FROM AUTHOR]

The quality of a country's business environment speaks volumes about its government's capacity and competitiveness. Unfortunately, the current system only evaluates countries and cities, overlooking the business environments of individual provinces. To address this issue, this study utilizes a green and sustainable development approach to evaluate the business environments of 30 provinces/municipalities in China. By incorporating ecological and environmental protection and sustainable development indicators, a novel green business environment index is constructed and analyzed to determine its impact on macroeconomic sustainable development and micro-enterprise operation. Taking into account the business environment index established by the World Bank and other organizations, this evaluation system adds ecological and environmental indicators specific to each province/municipality in China from the year 2011 to 2020. The result is a provincial green business environment evaluation index system consisting of 5 primary indicators and 30 secondary indicators. Principal component analysis (PCA) is then applied to rank the green business environment for each province/municipality. Furthermore, the overall green business environment of the Eastern region is superior to that of the Central and Western regions, highlighting the uneven development of the business environment in China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the era of the digital economy, there is a widespread trend for property enterprise economic management to undergo digital transformation. The article examines how the digital economy propels the economic management of property enterprises, scrutinizing its risk components and the digital ecological model of management services. The evaluation index system for the digital economy and the economic management level of property enterprises has been designed, and its comprehensive score is solved using the entropy weight method. Based on the comprehensive score, a spatial Durbin model was established, and spatial autocorrelation tests and econometric regression analyses were performed. Additionally, the risk prediction model for the economic management of property enterprises was established using BP neural networks and risk evaluation indexes. The global Moran’s mean values for the economic management of property enterprises in the digital economy were 0.267 and 0.081, respectively, and both passed the 1% significance test. The development level of the digital economy significantly promotes the economic management of property enterprises at the 1% level, as indicated by the influence coefficients obtained from the neighbor weight matrix, geographic weight matrix, and economic distance weight matrix, which are 2.236, 2.132, and 2.078, respectively. The BP neural network predicts economic management risk for property enterprises with an accuracy of 91.95%. Relying on digital economy-related technology can effectively promote the improvement of the economic management level of property enterprises and can also effectively enable risk prediction in economic management.

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.

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.

In view of the insufficient and unbalanced green development of the Beijing-Tianjin-Hebei urban agglomeration (BTH), we explored the green development efficiency of the region and its influencing factors, so as to provide basis and reference for improving its green development efficiency. To be specific, the super-efficiency network slack-based model considering undesired output (SE-SBM-undesirable) and the Global-Malmquist-Luenberger index are used to analyze the green development efficiency of BTH from 2005 to 2020. In addition, we also used the Spatial Dubin Model (SDM) to analyze the influencing factors of the green development efficiency. The following results were obtained: (1) The green development efficiency of the region has increased from 0.339 to 0.733. The regional difference has narrowed by 66.43% in 15 years, but the degree of difference is still very large. (2) The static green development efficiency presents a state of "strong in the north and weak in the South," and the dynamic green development efficiency shows a characteristic of "core-middle-periphery" decreasing in turn. (3) The green development efficiency of a city is likely to be affected by more than 20% of other cities in the region. the level of urbanization, per capita GDP, government public service expenditures, and environmental governance have a positive effect on the green development of BTH, while the proportion of the secondary industry and FDI have a negative effect on the green development of BTH. Based on the above results, the key point to realize the green transformation of BTH's economic development is to control the development intensity, improve the efficiency of resource utilization, and strictly restrict the industries with high pollution, high energy consumption and low output. [ABSTRACT FROM AUTHOR]

We use the panel data of innovation and entrepreneurship for global spatial autocorrelation and model selection testing. At the same time, we applied the Spatial Dubin Model (SDM) to empirically study the knowledge spillover effects of higher education campuses on regional innovation capabilities. Experimental research found that there are significant spatial differences in the innovation efficiency of universities. Specialized agglomeration can effectively improve the innovation efficiency and scale efficiency of colleges and universities. At the same time, the diversified agglomeration has no obvious effect on the innovation efficiency of colleges and universities. The accumulation of high-tech industries can effectively promote the innovation efficiency of universities. Human capital and opening to the outside world can effectively improve the innovation efficiency of universities.

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.

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.

This article uses the entropy weight method to calculate China’s economic high-quality development index and carbon finance development index from 2000 to 2020. Additionally, it conducts empirical examinations to scrutinize impacts of carbon finance regarding quality-focused development of China’s economy through dynamic spatial models. The findings reveal that the advancement of carbon finance substantively promotes the quality-focused economy advancement through enhancing the overall factor productivity, thereby establishing a mediating effect. Concurrently, the allocation of resources in green fiscal expenditure assumes a moderating effect, subsequently amplifying the promoting effect of carbon finance regarding quality-focused economy advancement. Consequently, it is recommended that a robust financial milieu be established, energy efficiency be enhanced, scientific and technological progress be bolstered, total factor productivity be further augmented, and the expenditure on green conservation be escalated. These measures will effectively stimulate the quality-focused development of China’s economy.

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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. [ABSTRACT FROM AUTHOR]

The logistics and manufacturing industries' co-agglomeration (LMCA) and deep integration, as well as the industries' digital transformation and intelligent upgrading, are of great significance to enhance regional economic resilience (EcoResi). This paper establishes a theoretical framework for LMCA and EcoResi based on the economic development theory and the new economic geography theory, explores the spatial spillover effect of LMCA on EcoResi, and measures the levels of LMCA and EcoResi. The data set is consisted of the indicators of LMCA and GDP growth rate of 30 provinces, centrally administered municipalities, and autonomous regions in China from 2006 to 2020. Spatial econometric models were used to empirically analyze the impact of LMCA on EcoResi based on provincial panel data. The results show that the improvement in LMCA not only improves the resilience of local economy, but it also has a significant spatial spillover effect. Further regional analyses show that LMCA has significant stimulating effects and spatial spillover effects on EcoResi in the central and western regions of China. However, the same effects are not significant in the eastern region of China. This research enriches the literature by suggesting effective ways to enhance EcoResi through LMCA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Solving the contradiction between agricultural economic growth and agricultural environmental problems is a difficult problem in regional environmental governance. Based on the panel data of 31 provinces, municipalities, and autonomous regions in China from 2000 to 2019, spatial Dubin model (SDM) is used to analyze the influence of agricultural economic growth and other factors on planting non-point source pollution. Innovate from the perspective of research objects and research methods, and the research results show (1) In the past 20 years, the amount of fertilizer applied and crop straw yield increased continuously. Through the fertilizer and farmland solid waste discharge of ammonia nitrogen (NH3–N), total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD), calculation of the equal-standard discharges of planting non-point source pollution shows that China's planting non-point source pollution is serious. Among the investigated areas in 2019, the equal-standard discharges of planting non-point source pollution in Heilongjiang Province were the highest and have reached 24.35 × 1010 m3. (2) The global Moran index of 20 years in the study area shows obvious spatial aggregation and diffusion characteristics, and has a significant positive global spatial autocorrelation, indicating that planting non-point source pollution discharges of the study area have potential interdependence in space. (3) SDM time-fixed effect model showed that the equal-standard discharges of planting non-point source pollution had a significant negative spatial spillover effect, and the spatial lag coefficient was − 0.11. Among the influencing factors, agricultural economic growth, technological progress, financial support to agriculture level, consumption capacity, industrial structure, and risk perception have significant spatial spillover effects on planting non-point source pollution. The results of effect decomposition show that the positive spatial spillover effect of agricultural economic growth on adjacent areas is greater than the negative effect on the local area. Based on the analysis of significant influencing factors, the paper provides direction guidance for the formulation of planting non-point source pollution control policy. [ABSTRACT FROM AUTHOR]

Based on Panel data collected from 2011 to 2020 targeted to 50 prefecture-level cities in the Yellow River Basin, this paper adopted standard deviation ellipse and spatial Dubin model to explore the nonlinear effects and spatial spillover effects of urbanization on air pollution and ecological resilience in the Yellow River Basin. The results show that the degree of air pollution in the southeast of the Yellow River Basin is higher than that in the northwest of the Yellow River Basin, the distribution range of air pollution is shrinking, the concentration of ecological resilience is enhanced, and the ecological environment is developing for the better. There is a significant U-shaped relationship between urbanization and air pollution in the Yellow River Basin, and an inverted U-shaped relationship between urbanization and ecological resilience. For every 1% increase in urbanization, air pollution decreases by 0.0873%, ecological resilience increases by 0.4046%. For every 1% increase in the square term of urbanization, air pollution increases by 0.2271%, ecological resilience decreases by 0.1789%. The urbanization of the Yellow River Basin has a spatial spillover effect on air pollution and ecological resilience, and urbanization has a significant negative impact on the ecological environment of neighboring cities. The robustness of the above conclusions is verified by introduce an inverse distance weight matrix replacing the spatial weight matrix. [ABSTRACT FROM AUTHOR]

The forestry industry is a low-carbon green industry with great potential for development, but its current development model is facing multiple dilemmas that urgently require a shift to high-quality forestry development. Neoclassical theory and endogenous growth theory consider technology innovation as the foundation of economic growth. In order to explore the impact of forestry technology innovation (FTI) on the economic growth of forestry industry, this paper uses the entropy method, spatial Durbin model, and threshold model to explore the spatial effect of forestry technology innovation on forestry industry economy for analysis and exploration of the non-linear relationship between the two, and the panel data from 31 provinces in China from 2002 to 2020 are used as the sample for empirical study. Results show that: (1) Forestry technology innovation has a significant role in promoting the economic growth of the forestry industry and has obvious spatial spillover effects, which also promotes the economic growth of forestry in neighboring regions. (2) There is a threshold effect of forestry technology innovation on the economic growth of the forestry industry. When the forestry technology innovation exceeds its optimal interval, its effect on the economic growth of forestry industry shows diminishing marginal benefits. (3) Forestry technology innovation has industrial heterogeneity on the economic growth of the forestry industry. Therefore, managers should take advantage of the growth effect of forestry technology innovation in the forestry industry at the level of sustainability, and further make full use of the spatial effect resulting from the flow of technology to establish a system of communication and learning to form a virtuous coordination of the forestry economic environment for the high-quality development of the forestry industry. [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]

Using the panel data of 274 cities from 2011 to 2019, this article studied the impact of the digital economy on the low-carbon transformation and space overflow effect on the city's low-carbon transformation and used the panel threshold model to calculate the regulatory effect of public attention. Studies found the following: (1) The digital economy, the urban low-carbon transformation level, and public attention show regional differences, manifesting as the distribution of "point clusters" and "decreasing from east to west", and as far as the speed of change, after the year 2015, the annual increase of various indicators increased. (2) The role of digital economy development and urban low-carbon transformation levels have a "U" relationship. At present, most cities across the country have passed the point of inflection, which presents a significant promotion effect. However, in a comparison of regional coefficients, the Eastern region > Western region > Midlands. (3) The development of the digital economy has a significant "siphon effect" on the impact of urban low-carbon transformation. (4) With the increase in public attention in the region, the positive promotion of the digital economy on the urban low-carbon transformation has gradually increased. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is an urgent scientific issue to explore the spatial and temporal differentiation and impact indicators of the agricultural water footprint in major grain-producing areas. Therefore, this study tries to use the water footprint theory to implement top-down calculation of the agricultural water footprint in major grain-producing regions from 2000 to 2019 and investigate the various impacts on the agricultural water footprint under the influence of spatial-temporal effects using spatial autocorrelation and the spatial Dubin model. The results indicate that from 2000 to 2019, the overall agricultural water footprint of China showed a fluctuating downward trend in an inverted N shape and demonstrated high–high and low–low spatial aggregation characteristics. There are notable characteristics, including high spatial dependence, spatial barriers, and path locking of the agricultural water footprint in most provinces and regions of the main grain-producing areas. Policy factors, water-saving technologies, social development, economic development, and industrial structure adjustment are all significantly and negatively correlated with the increase in the agricultural water footprint, while agricultural production and natural factors have a significant positive relationship with the agricultural water footprint. The spatial spillover effect of water-saving technologies, industrial restructuring, agricultural production, and natural factors is powerful. Therefore, a rationally optimized industrial structure, strengthened regional linkage of water resources management and control, and the promotion of efficient water infrastructure technology are important ways to inhibit the agricultural water footprint. [ABSTRACT FROM AUTHOR]

In the Yangtze River Economic Belt (YREB), the green economy is an essential to sustainable economic development. In this study, we calculated a comprehensive index of environmental pollution based on the global entropy weight method and used the super slacks-based measure (SBM) model to estimate the green economic efficiency (GEE) of provinces and cities in the YREB from 2005 to 2018. Subsequently, we explored temporal and spatial evolution characteristics combined with the Theil and Moran indexes, and adopted the spatial Dubin model to analyze its influencing factors. We divided the YREB into three watersheds to facilitate the analysis. The results show that the GEE in the YREB initially decreased and then increased, and the difference among the three major watersheds was higher than that within the watershed. We found a positive spatial autocorrelation in the development level of the green economy in the YREB. While industrial structure had a negative effect, economic development, scientific and technological level, and environmental regulation all had a positive effect on GEE. Finally, we offered policy recommendations to improve the level of green development in the YREB. [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]

Introduction: China is a large agricultural nation with the majority of the population residing in rural areas. The allocation of health resources in rural areas significantly affects the basic rights to life and health for rural residents. Despite the progress made by the Chinese government in improving rural healthcare, there is still room for improvement. This study aims to assess the spatial spillover effects of rural health resource allocation efficiency in China, particularly focusing on township health centers (THCs), and examine the factors influencing this efficiency to provide recommendations to optimize the allocation of health resources in rural China., Methods: This study analyzed health resource allocation efficiency in Chinese rural areas from 2012 to 2021 by using the super-efficiency SBM model and the global Malmquist model. Additionally, the spatial auto-correlation of THC health resource allocation efficiency was verified through Moran test, and three spatial econometric models were constructed to further analyze the factors influencing efficiency., Results: The key findings are: firstly, the average efficiency of health resource allocation in THCs was 0.676, suggesting a generally inefficient allocation of health resources over the decade. Secondly, the average Malmquist productivity index of THCs was 0.968, indicating a downward trend in efficiency with both non-scale and non-technical efficient features. Thirdly, Moran's Index analysis revealed that efficiency has a significant spatial auto-correlation and most provinces' values are located in the spatial agglomeration quadrant. Fourthly, the SDM model identified several factors that impact THC health resource allocation efficiency to varying degrees, including the efficiency of total health resource allocation, population density, PGDP, urban unemployment rate, per capita disposable income, per capita healthcare expenditure ratio, public health budget, and passenger traffic volume., Discussion: To enhance the efficiency of THC healthcare resource allocation in China, the government should not only manage the investment of health resources to align with the actual demand for health services but also make use of the spatial spillover effect of efficiency. This involves focusing on factors such as total healthcare resource allocation efficiency, population density, etc. to effectively enhance the efficiency of health resource allocation and ensure the health of rural residents., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Meng, Shen, Zheng, Li, Lin, Pei, Wu and Meng.)

The spatial auto-correlation analysis approach is used to examine the agglomeration features and geographical correlation of industrial agglomeration and pollutant discharge using 91 prefecture-level cities in the Yellow River Basin from 2005 to 2020 as the research target. The Yellow River Basin and the upper, middle, and lower reaches were investigated for the effects of industrial agglomeration on industrial pollution emissions. The Dubin model of the spatial panel is used to investigate the drivers of industrial pollution from both indirect effects and direct effects. The findings indicated that ① the spatial pattern of industrial pollution agglomeration and industrial agglomeration in the Yellow River Basin has spatial convergence; ② the global clustering characteristics of industrial pollution in the Yellow River Basin are apparent. The global correlations of the three pollutants are industrial wastewater > industrial smoke and dust > industrial SO2; there is a spatial association between industrial agglomeration and pollution agglomeration, and there are differences in the spatial interconnection between industrial agglomeration and the accumulation of different pollutants; ③ from the overall point of view of the Yellow River Basin, industrial agglomeration will reduce the pollution levels of industrial wastewater, industrial SO2, and industrial smoke and dust. From the perspective of different regions, industrial agglomeration in the upper, middle, and lower reaches is conducive to the decline of industrial pollution, and the role is in the middle, downstream, and upstream in order from large to small; and ④ the Yellow River Basin’s industrial pollution is primarily caused by population agglomeration, industrial structure, environmental regulation, opening to the outside world, and economic development. Their effects on industrial wastewater, industrial SO2, industrial smoke, and dust pollution vary.

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract 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.

The forestry industry is a low-carbon green industry with great potential for development, but its current development model is facing multiple dilemmas that urgently require a shift to high-quality forestry development. Neoclassical theory and endogenous growth theory consider technology innovation as the foundation of economic growth. In order to explore the impact of forestry technology innovation (FTI) on the economic growth of forestry industry, this paper uses the entropy method, spatial Durbin model, and threshold model to explore the spatial effect of forestry technology innovation on forestry industry economy for analysis and exploration of the non-linear relationship between the two, and the panel data from 31 provinces in China from 2002 to 2020 are used as the sample for empirical study. Results show that: (1) Forestry technology innovation has a significant role in promoting the economic growth of the forestry industry and has obvious spatial spillover effects, which also promotes the economic growth of forestry in neighboring regions. (2) There is a threshold effect of forestry technology innovation on the economic growth of the forestry industry. When the forestry technology innovation exceeds its optimal interval, its effect on the economic growth of forestry industry shows diminishing marginal benefits. (3) Forestry technology innovation has industrial heterogeneity on the economic growth of the forestry industry. Therefore, managers should take advantage of the growth effect of forestry technology innovation in the forestry industry at the level of sustainability, and further make full use of the spatial effect resulting from the flow of technology to establish a system of communication and learning to form a virtuous coordination of the forestry economic environment for the high-quality development of the forestry industry.

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.

Improving the logistics ecological efficiency (LEE) has become a significant part of ensuring a sustainable development and tackling environmental pollution. Previous studies in the logistics industry seldom considered air pollutants and the association of spatial information. Therefore, innovatively considering SO2, NOx, and PM, this study adopted the super-SBM-undesirable model to calculate the LEE of 30 provinces in China from 2005 to 2019 and, thereafter, developed information-based matrix to explore its influencing factors by using the spatial Dubin model. The results indicated that (1) the overall LEE was low with the average of 0.657, presenting a three-stage trend of "decreasing first, then rising, and later stable," and significant regional differences with the decreasing gradient pattern of the "Eastern-Central-Western." (2) A spatial directionality distributed from the northeast to southwest and a significant spatial autocorrelation were observed. (3) The LEE had a significant positive spillover effect. Industrial structure, urbanization level, environmental regulation, and technological innovation level had a positive impact on the local LEE, and industrial structure displayed the most promoting effects. Energy intensity, economic level, energy structure, and opening level had a significant effect on the local LEE with varying degree of inhibition. Local energy intensity and environmental regulation had a positive influence on the LEE in neighboring areas, while local opening level had inhibiting effects. In addition, policy recommendations for enhancing the LEE were made. [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]

The rapid development of the urban network has led to the fact that cities are no longer single individuals, and the network has changed the urban development environment. The interaction between cities has gradually become an important factor for the high-quality development (HQD) of cities. From the perspective of externalities, it is of great significance to explore the impact of agglomeration externalities and network externalities on the HQD of cities to promote the high-quality and sustainable development of the region. Taking the urban agglomeration in the middle reaches of the Yangtze River as an example, this study constructs a theoretical framework to empirically study the influence of agglomeration externalities and network externalities on the HQD of the city. The results show that the integrated network of the urban agglomeration from 2011 to 2020 had a high clustering coefficient and a small average path length with the characteristics of a “small world”. The centrality of urban nodes was hierarchical and had a “pyramid” structure. From 2011 to 2020, the high-quality development level (HQDL) of the urban agglomeration steadily improved and the regional “development gap” gradually narrowed. Wuhan, Changsha, and Nanchang were in a relatively advantageous position in the urban agglomeration. Furthermore, there was a spatial agglomeration effect and a spatial spillover effect in the HQD of urban agglomeration. Network externalities presented difference in different cities, and the influence of agglomeration externalities on HQD presented a u-shaped nonlinear relationship. Network externalities could significantly promote HQD, and the indirect effect of HQD was greater than its direct effect. In addition, factors such as government capacity and level of opening to the outside world also had a significant impact on the HQD of the region.

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on China's provincial panel data from 1990 to 2017 and the improved Lucas, Nelson & Phelps model, the Spatial Dubin Model is used to test the spatial effects of higher education and human capital quality. The results showed that high-level human capital, characterized by higher education and urban labor income index, indirectly promoted local economic growth through technological innovation. There was also a "local-neighborhood" synergy effect. The neighborhood effect was manifested in that it affected the economic development of neighbors by promoting technological catch-up. After considering the quality factor, both the local and neighborhood effects were enhanced. From a regional perspective, higher education in the Yangtze River Delta, where the level of economic development is relatively high, was manifested as a spatial spillover effect of technological innovation and the neighborhood effect in the northeastern Bohai Rim and the Pearl River Delta was manifested as a technological catch-up. [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]

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]

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]

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.