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In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China's agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China's major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth. [ABSTRACT FROM AUTHOR]

New urbanization (NU) construction is the basic direction of urbanization development in contemporary China. This paper selects the panel data of 36 representative cities in the Yangtze River economic belt (YREB) from 2006 to 2020, calculates the comprehensive index of NU and ecological environment (EE) by employing the entropy method, analyzes the spatial agglomeration characteristics of EE through spatial autocorrelation analysis, and tests the spatial effect of NU on EE on the basis of the dynamic spatial Durbin model. Results show that first, "time inertia" and spatial dependence exist in the urban EE of the YREB. Second, the NU has a positive role in promoting the EE. The development of NU in this area not only promotes the EE in this area, but also promotes the improvement of the EE in neighboring areas. Third, the increase in per capita wealth and the adjustment in industrial structure are beneficial to the improvement of EE. This paper discusses the impact of China's NU development on the urban EE. Results reveal the relationship between affluence, technological progress, industrial structure, opening to the outside world, environmental regulation, and EE. This study provides decision-making reference for the government in formulating sustainable strategies. [ABSTRACT FROM AUTHOR]

China has recently implemented a dual-carbon strategy to combat climate change and other environmental issues and is committed to modernizing it sustainably. This paper supports these goals and explores how the digital economy and green finance intersect and impact carbon emissions. Using panel data from 30 Chinese provinces over the period 2011–2021, this paper finds that the digital economy and green finance can together reduce carbon emissions, and conducts several robustness tests supporting this conclusion. A heterogeneity analysis shows that these synergistic effects are more important in regions with low levels of social consumption Meanwhile, in the spatial dimension, the synergistic effect of the local digital economy and green finance adversely impacts the level of carbon emissions in surrounding areas. The findings of this paper provide insights for policymakers in guiding capital flow and implementing carbon-reduction policies while fostering the growth of China’s digital economy and environmental sustainability.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies. [ABSTRACT FROM AUTHOR]

This article is based on panel data from 266 cities in China from 2009 to 2020. Classical economics theory and Marxist political economy theory are used as the theoretical basis for the study. Static spatial Durbin model regression and dynamic spatial Durbin model regression are conducted to analyze the impact of education investment on carbon dioxide emissions. The results show that education investment can significantly reduce carbon dioxide emissions, not only through spatial spillover effects, but also through dynamic effects over time. The implementation of education investment in one city can have a positive impact on surrounding cities, thereby achieving a certain degree of reduction in carbon emissions. Additionally, the inhibitory effect of education investment on carbon emissions is more significant in the short term. Furthermore, the study found that there is significant heterogeneity in the effect of education investment on carbon dioxide emissions between northern and southern cities, with education investment in southern cities having a more significant effect. This may be related to the industrial structure and climate of southern cities. The conclusions of this study provide useful reference for the development of education policies and environmental protection policies. In the future, the government can increase education investment appropriately to achieve the goal of reducing carbon dioxide emissions. And research has shown that education investment can suppress carbon emissions by influencing changes in energy and industrial structures. Therefore, when using measures to reduce emissions in education investment, we can focus on these two aspects. [ABSTRACT FROM AUTHOR]

Background Since the 20th century, pursuing Universal Health Coverage (UHC) has emerged as an important developmental objective in numerous countries and across the global health community. With the intricate ramifications of population mobility (PM), the government faces a mounting imperative to judiciously deploy health expenditure to realise UHC effectively. Objective This study aimed to construct a comprehensive UHC index for China, assess the spatial effects of Government Health Expenditure (GHE) on UHC, and explore the moderating effects of PM on this association. Method A Dynamic Spatial Durbin Model (DSDM) was employed to investigate the influence of the GHE on UHC. Therefore, we tested the moderating effect of PM. Results In the short-term, the GHE negatively impacted local UHC. However, it enhanced the UHC in neighbouring regions. Over the long term, GHE improved local UHC but decreased UHC in neighbouring regions. In the short-term, when the PM exceeded 1.42, the GHE increased the local UHC. Over the long term, when the PM exceeded 1.107, the GHE impeded local UHC. If the PM exceeded 0.91 in the long term, the GHE promoted UHC in neighbouring regions. The results of this study offer a partial explanation of GHE decisions and behaviours. Conclusions To enhance UHC, a viable strategy involves augmenting vertical transfer payments from the central government to local governments. Local governments should institute healthcare systems tailored to the urban scale and developmental stages, with due consideration for PM. Optimising the information disclosure mechanism is also a worthwhile endeavour.

The reduction of haze and carbon emissions is extremely important for promoting sustainable development, improving air quality, enhancing health, and mitigating climate change. However, there is not enough research available on the impact of fiscal decentralization in China on the management of carbon and haze reduction. In order to thoroughly examine the effects of Chinese-style fiscal decentralization on the synergy between haze reduction and carbon reduction in different provinces, this study utilizes a dynamic spatial panel Durbin model using Han-Phillips Generalized Method of Moments (GMM) estimation and a multi-scale geographically and temporally weighted regression model. Our findings indicate that the eastern region consistently takes the lead in reducing haze and achieving carbon synergy. Fiscal technology decentralization has a direct positive impact and spatial spillover effect on carbon haze synergy with significant inverted U-shaped characteristics. These effects primarily arise from the promotion of technological innovation through fiscal technology decentralization. Furthermore, the influence of decentralizing fiscal technology expenditures on the degree of synergy between haze mitigation and carbon reduction varies significantly across China's provinces, both spatially and temporally. This entails promoting coordination between fiscal decentralization and policies related to haze and carbon emission reduction and encouraging information sharing, technology exchange, and collaborative projects between different regions to create a synergistic linkage effect. This will help achieve joint development and environmental protection goals in all regions. The discoveries carry significant consequences for directing the synchronized administration of haze and carbon and can serve as a solid basis for governmental decision-making aimed at enhancing air quality and attaining carbon neutrality through collaborative actions and policies. [ABSTRACT FROM AUTHOR]

Improving the logistics industry's green total factor productivity (LGTFP) is a key source of power to achieve its high-quality development (HQD) in China. Technological innovation (TI) and environmental regulation (ER) policies are vital factors that affect the improvement of LGTFP. Although the impact of ERs or TI on LGTFP has been extensively researched, few studies have examined their combined effect. This study utilizes panel data from 30 Chinese provinces during 2006-2020 to estimate LGTFP using the Epsilon-based measure and global Malmquist-Luenberger index. Moreover, this study employs a dynamic spatial Durbin model to explore the influence of TI on LGTFP and its spatial spillovers, as well as the modulatory effect of ER in the relations between TI and LGTFP. The results show that (1) China's LGTFP displays positive spatial spillovers and is influenced by the preceding period's level. (2) In the short term, an improvement in TI levels had a positive effect on local LGTFP, but negative spillovers on neighboring districts. Nevertheless, in the long run, an improvement in TI levels had a notably negative influence on local LGTFP but positive spillovers on adjacent regions. (3) ER had an "inverted U-shaped" modulatory roles with spatial spillovers on the relations between TI and LGTFP in the long-term. These discoveries offer valuable insights into the coordinated development of TI systems and ER policies, enabling the formation of a policy system that aligns with the logistics industry's HQD in China. [ABSTRACT FROM AUTHOR]

The enhancement of ecological and environmental quality relies on the ability of environmental governance, which can be significantly improved through the utilization of digital technology. This study develops two separate index systems for digital technology and environmental governance. It utilizes a modified coupling coordination degree model to assess the level of synergy between digital technology and ecological and environmental governance. The study also investigates the temporal development patterns and spatial distribution characteristics of this synergy. Finally, it employs a spatial Durbin model to examine the impact of digital technology on the improvement of environmental governance. The findings indicate a significant improvement in the coupling coordination degree (EDD) of DT, EG, and their combination in China from 2007 to 2021. Moreover, the coupling coordination status in the eastern region surpasses that in the central and western regions. Throughout the study period, the EDD exhibits clear spatial clustering properties. The local autocorrelation analysis of EDD reveals that the eastern region is predominantly characterized by “high-high” clustering, whilst the middle and western regions display “low-low” clustering. The local autocorrelation analysis of EDD indicates that the eastern region exhibits predominantly “high-high” aggregation, whilst the middle and western regions display “low-low” aggregation. The presence of digital infrastructure and user activity has a beneficial impact on environmental governance, leading to positive promotion and spatial spillover effects. However, the digital economy has a negative influence on regional environmental governance. Additionally, the incongruity between the construction of governmental websites and the capacity of environmental governance is reflected in digital governance.

In the context of increasingly severe resource and environmental constraints, examining the impact of citrus industry agglomeration on the green total factor productivity (GTFP) of citrus is of great importance for the sustainable development of the citrus industry and is crucial for promoting the green, high-quality growth of China’s agricultural sector. In this study, the global Malmquist–Luenberger productivity index (GMLPI) model was used to measure the GTFP of mandarins and tangerines based on inter-provincial panel data from China’s major citrus-producing regions between 2007 and 2022. The dynamic spatial Durbin model was employed to empirically analyze the effects of citrus industry agglomeration on the GTFP of mandarins and tangerines, including the disaggregation of its spatial spillover effects. The results indicate that, in terms of temporal dynamics, the GTFP, technical progress index (GTC), and technical efficiency index (GEC) of mandarins and tangerines significantly fluctuated, especially during the period from 2007 to 2015. Regional disparities in GTFP and the GTC are more pronounced for mandarins than for tangerines, while the GEC shows greater regional disparities for tangerines than for mandarins. The intensification of citrus industry agglomeration has had a significant positive impact on the GTFP of mandarins and tangerines, both locally and in neighboring regions. The spatial correlation of the green total factor productivity of mandarins and tangerines fluctuated; mandarins showed significant spatial aggregation in some years, while tangerines showed significant spatial dispersion in several years. The local Moran scatterplot further reveals the significant negative spatial autocorrelation of mandarin and tangerine green total factor productivity from 2007 to 2022. The direct, indirect, and total effects of citrus industry agglomeration on the GTFP of mandarins and tangerines are significant and positive in both the short- and long-term, with short-term benefits exceeding long-term effects. Consequently, enhancing regional cooperation and exchange while advancing citrus industry agglomeration is essential for sustained productivity growth.

This paper delves into the spillover effects of fiscal decentralization on access to essential social services within communes in Burkina Faso. It employs a Dynamic Spatial Durbin Model that comprehensively incorporates temporal dynamics, spatial interdependence concerning the dependent variable, and spatial interdependence concerning exogenous variables. The analysis covers 280 municipalities, which represents 80% of the municipalities in Burkina Faso, over the period 2011–2020. The findings substantiate the presence of spillover effects stemming from fiscal decentralization on the availability of fundamental social services. However, the nature of these spatial effects hinges on the particular type of fiscal resources and local public goods under consideration. Notably, both capital expenditures and decentralized cooperation resources yield short‐term and long‐term direct as well as indirect enhancements to the quality of education and accessibility to potable water. On the other hand, self‐generated revenue and operational grants augment water accessibility but bear no impact on educational quality. The outcomes put forth imply that municipal councils should concurrently bolster resource mobilization by fortifying decentralized cooperation and widening the tax base. Simultaneously, the introduction of flexible tax payment mechanisms is advisable. [ABSTRACT FROM AUTHOR]

In response to global climate change, the green benefits of digital transformation are attracting attention. As agriculture is the basis of the national economy and the second largest source of greenhouse gas emissions, how to simultaneously reduce carbon emissions and increase production in agriculture by means of digitalization is a key issue for China to achieve the 'dual carbon' goals. Based on the panel data of 30 provinces in China from 2011 to 2020, this study constructed a comprehensive index of rural digitalization following the logic of 'development foundation, development dynamics, and development results,' and applied the EBM-GML method to accurately quantify the agricultural total factor carbon productivity (ATFCP). The study found that: ① Rural digitalization could increase ATFCP, and this finding still held after a series of robustness tests and endogeneity treatments. ② Rural digitalization promoted technological innovation, scale operation, structural upgrading, industrial agglomeration, and non-farm employment, which in turn had an enhancing effect on ATFCP. ③ The results of the heterogeneity analysis demonstrated that the positive effect of rural digitalization on ATFCP showed a marginal decreasing effect, which could play a significant enhancing role in both high and low-green development level areas. In addition, the existence of a financial threshold for rural digitalization, which had a stronger effect in areas with high rural financial development levels, was a main constraint to digitalization in the process of promoting ATFCP. ④ Spatial econometric analysis showed that there was a significant spatial effect between rural digitalization and ATFCP, and the long-term and short-term indirect effects were significantly positive, indicating that rural digitalization not only enhanced local ATFCP, but also positively affected ATFCP in neighboring areas through technology diffusion. Based on this, we should further strengthen the dividend effect of rural digitalization by reducing carbon emissions and increasing production. We should also promote factor flow and technological application and enhance the development potential of rural digitalization by increasing financial investment and improving digital infrastructure construction. At the same time, we should implement dynamic and differentiated digital rural development strategies, improve regional cooperation mechanisms, and promote the coordinated development of the digital economy in various regions. [ABSTRACT FROM AUTHOR]

To observe the dynamic changes in the silage corn production, the study was based on panel data from 30 provinces in China from 2001 to 2021, and used a center of gravity model to analyze the changes in the silage corn production agglomeration areas. On this basis, the dynamic spatial Durbin model was used to analyze the impact of factor input, socio-economic and policy support on regional changes in Chinese silage corn production. The results showed that there was a spatial clustering phenomenon in Chinese silage corn, which meant that the Moran index of Chinese silage corn production from 2001 to 2021 showed an overall development trend of 'decreasing-increasing-decreasing- increasing'. The evolution trajectory of the production center showed that the center of gravity in the Chinese silage corn production area showed an evolution trend of 'first northeast, then northwest, and southwest'. The results of the mechanism of action indicated that the main driving factors for promoting the production of silage corn were grain advantages, financial support, and increased income from migrant workers. The production infrastructure, fertilizer application, and agricultural benefits had a positive impact on the production of silage corn in this province, but the impact on the production of silage corn in neighboring provinces was not significant. [ABSTRACT FROM AUTHOR]

To study the impact of the digital economy on industrial structure upgrading, this paper constructs the dynamic spatial Durbin model, dynamic spatial intermediary model, and spatial threshold model based on the panel data of 155 prefecturelevel cities in China from 2011 to 2021. It is found that: (a) the improvement of the digital economy in the region can directly promote the upgrading of local industrial structure, but there is a significant "siphon effect," which makes the spatial spillover effect negative and significant. (b) The digital transformation of enterprises plays a mediating role in the path of digital economy-driven industrial structure upgrading, with a short-term mediating effect size of about 17.18%. (c) The digital economy has a threshold effect on the path of enterprise digital transformation for industrial structure upgrading. These findings provide suggestions for policy. [ABSTRACT FROM AUTHOR]

The recent multidimensional crisis has harmed the global economy because countries are currently integrated. In this study, we examine the spillover effect of the United States (US) interest rate on the output gap of ASEAN+3 countries using the dynamic spatial Durbin model from 2010 to 2020. After controlling for various institutional variables, this analysis further examines the effect of the exchange rate and the COVID-19 pandemic on the output gap. This model captures the spatial interactions and short- and long-term effects of interest rate monetary policies. The findings indicate a negative spillover effect of the US central bank interest rate on ASEAN+3 countries' output gap. The result had significant direct and indirect short-term effects. Additionally, exchange rates negatively affect the output gap. The output gap decreased because of the COVID-19 pandemic. This study is expected to be useful for monetary authorities in developing the best monetary policy for economic recovery. [ABSTRACT FROM AUTHOR]

Transportation infrastructure plays a crucial role in the free movement and rational allocation of resources. Utilizing provincial panel data from China spanning from 2000 to 2017, we employ a dynamic spatial Durbin model (DSDM) and a panel threshold model to analyze the impact of transportation infrastructure on resource misallocation. The findings are as follows: (1) Overall, transportation infrastructure serves to correct misallocations of capital and labor, exhibiting spatial effects; (2) Under the influence of spatial spillover effects, transportation infrastructure improves capital misallocation in neighboring regions, yet exacerbates labor misallocation; (3) The impact of transportation infrastructure on capital and labor misallocations shows regional heterogeneity; (4) Transportation infrastructure indirectly ameliorates resource misallocation through three pathways: opening up to external markets, urbanization, and industrial upgrading; (5) There is a threshold effect of transportation infrastructure on labor misallocation - it only improves labor misallocation when it reaches a certain scale.

The aim of the paper is to investigate spatial interactions among the debt of 2,442 municipalities in Poland over the period 2005–2020. Using the dynamic spatial Durbin model with two-way fixed effects, it provides empirical evidence of positive spatial interactions. It is estimated that an increase in debt per capita in a given municipality by PLN 100 is related to an increase in the debt per capita in neighbouring municipalities by PLN 8–10. The result is robust to the various specifications of spatial weight matrix (contiguity vs. inverse-distance with a cut-off). It is also found that municipal debt in Poland suffers from high persistence over time. Keywords: municipalities, dynamic spatial Durbin model, debt persistence.

Appropriate industrial agglomeration has several benefits, including reducing environmental pollution, promoting innovation and enhancing grain green total factor productivity (GTFP). It is an effective strategy for promoting high-quality and sustainable development in the grain industry. In this study, the slacks-based measure of the global Malmquist–Luenberger (SBM-GML) model is used to estimate China's grain GTFP using the panel data of 31 provinces from 2001 to 2020. Furthermore, a dynamic spatial econometric model is employed to empirically investigate the impact of grain industry agglomeration on grain GTFP and its regional heterogeneity. The results show that GTFP exhibits a fluctuating growth tendency, with advancements in green technology serving as the primary engine of that expansion. (2) The deepening of grain industrial agglomeration has a long-term promoting effect on the grain GTFP of local and neighbouring areas, with the long-term effect being more significant than the short-term effect, as revealed by the dynamic spatial Durbin model. (3) According to the heterogeneity analysis, industrial agglomeration's impact on grain GTFP is most noticeable in the production and sales balance. Therefore, encouraging regional cooperation and communication while raising the grain industrial agglomeration standard is crucial. The degree of regional economic growth, the state of the agricultural infrastructure and the conditions of the natural resources should all be considered by policymakers when developing distinct and focused policy assistance for each region.

Green technology is a significant means to improve the environment and achieve sustainable development goals. According to the data of Chinese provincial panel from 2000 to 2016, our study investigated the spatial effect of environmental research and development (R&D) activities on SO2 intensity using the dynamic spatial Durbin model. First, SO2 intensity in China was shown to have obvious spatial correlation, strong path dependence, and spatial agglomeration features of 'high-high' as well as 'low-low'. Second, both in the short- and long-term, environmental R&D activities had an essential negative influence on local SO2 intensity, but no significant effect on SO2 intensity in the neighbouring areas, indicating that the SO2 intensity reduction effect of environmental R&D activities was confined to local areas. Moreover, the long-term effect of environmental R&D activities on SO2 intensity was not enhanced, indicating that China's existing green technology is insufficient, which hinders the spillover influences of environmental R&D activities. Third, the short- as well as long-term effects of practical-type R&D on SO2 intensity were significantly negative, indicating that practical-type R&D can effectively reduce SO2 intensity. Invention-type R&D had a significant negative effect on local SO2 intensity, but no significant effect on neighbouring areas. [ABSTRACT FROM AUTHOR]

Carbon emissions have risen in line with China's economic expansion. The key to sustainable development is finding a way to strike a balance between economic expansion and environmental protection, so improving carbon emission efficiency is vital. This paper uses provincial data from 2010 to 2020 to account for total carbon emissions using the emission factor method and obtains carbon emission efficiency data on this basis. A dynamic spatial Durbin model is then used to empirically test the possible influencing factors. The results show that, firstly, the growth rate of total carbon emissions is generally in line with the growth rate of GDP, indicating that there is no 'decoupling' in the economic system. Second, regional carbon emissions and carbon emission efficiency are not necessarily related. Thirdly, there is a clear spatial effect on carbon emission efficiency. The eastern region has the highest carbon emission efficiency, the western region has the lowest, and the northeastern and central regions have little difference in carbon emission efficiency. Further spatial and temporal migration analysis reveals that five provinces have made the migration between 2010 and 2020. Fourthly, in the short term, the direct and indirect effects of the factors affecting carbon emission efficiency are insignificant, but in the long term, most of the factors have significant direct and indirect effects on carbon emission efficiency. Finally, based on the above research findings, this paper makes policy recommendations. [ABSTRACT FROM AUTHOR]

Haze pollution not only has negative impact on public health and air quality, but also has restricted China's industrial upgrading and high-quality development, and Chinese urban agglomerations are one of the areas hardest hit of haze pollution. In the process of China's economic transformation, local governments will adopt local protectionism, leading to market segmentation. This is a phenomenon that refers to the distortion of resource allocation by local governments for the sake of vested interests and the existence of segmentation in commodity markets. This behavior is considered to be one of the important factors causing haze pollution. As Yangtze River Delta urban agglomerations are considered to be one of the earliest, fastest growing, and most mature for market integration in China, there is a lack of empirical testing on the impact of market segmentation on haze pollution in this urban agglomeration. Based on urban panel data from the period of 1998–2018 and the market segmentation index calculated by the relative price method, we use the dynamic spatial Durbin model and generalized space two-stage least squares method to explore the effect of market segmentation in urban agglomeration on haze pollution; the results are as follows: (1) Market segmentation significantly exacerbates haze pollution, in other words, haze pollution will increase by 2.14% when market segmentation increases by 1%. (2) Cities with a high degree of market segmentation and high levels of haze pollution have the potential to reduce pollution through market integration in the future. (3) Market segmentation in surrounding regions also has a significant worsening effect on haze pollution in the region. The indirect effect of market segmentation is 3.67 times that direct effect, indicating that the spatial spillover effect of market segmentation on haze pollution is greater than its own impact. (4) Mechanism analysis finds that it will aggravate haze pollution by hindering economic scale, industrial structure, and technological progress when the degree of market segmentation is high. [ABSTRACT FROM AUTHOR]

[Objective] This study aimed to clarify the spatial relationship between regions for the eco-civilization development and provide a reference for promoting the high-quality coordinated development of regional eco-civilization. [Methods] In this study, the eco-civilization development of provincial-level regions in China from 2010 to 2020 was measured by using the time space range entropy weight method. With the help of social network analysis based on the modified gravity model and dynamic spatial Durbin model, the spatial convergence and spatial correlation network of China’s eco-civilization development were analyzed. [Results] (1) Overall network structure characteristics showed that network accessibility of eco-civilization development and the multiple superposition of its spillover effects were obvious, the inter-provincial correlation degree and network stability were improved, showing a complex network structure of core-periphery form. (2) Individual network structure characteristics showed that the provinces of Jiangsu, Shandong, and Henan were at the core of the network and played the role of “hubs” and “bridges”. (3) The internal interaction characteristics showed that the areas in the western provinces were the main spillover parties of the elements in the network, while the areas in the eastern-central provinces were the main beneficiaries of the elements, and the polarization role in the development process of eco-civilization was greater than the diffusion role. (4) There existed absolute and conditional β spatial convergence of eco-civilization development in China as well as in all dimensions and its three regions, with different rates of convergence and a decreasing rate from the central region to the western region, followed by the eastern region. (5) The acceleration effect of spatial network externalities on convergence in low-level central and western regions were more obvious. Marketization, technological innovation, and industrial structure factors were important driving mechanisms affecting the convergence of eco-civilization development, but there was significant spatial heterogeneity. [Conclusion] There is still much room for improving the closeness of the spatial correlation of China’s eco-civilization development, and it is urgent to improve the national unified ecological and environmental factors market, strengthen inter-regional ecological cooperation, improve the linkage effect between plates, and strengthen the trans-regional network effect, so as to promote the coordinated development of eco-civilization.

The optimization of industrial structure and layout is essential for promoting the high-quality development of the regional economy. As a typical example of a green comprehensive industry, the agglomerations of the sports industry have the potential to release additional green benefits. Consequently, this paper uses the balanced panel data of 30 provinces, municipalities, and autonomous regions in China from 1998 to 2021 as samples and, based on the strategic background of China's dual-carbon target and the re-interpretation of the green economy, uses the Super-SBM model to re-measure the green economic efficiency of each region and applies the dynamic spatial Durbin model and the dynamic panel system GMM model to evaluate the direct effect, mediating mechanism, spatial spillover effect, and heterogeneity effect of sports industry agglomeration on the regional green economic efficiency. Empirical findings indicate that: (1) The improvement of green economic efficiency under China's dual-carbon target has the characteristics of dynamic accumulation, and there is a siphon effect between neighboring regions. (2) The effects of sports industry agglomeration on local green economy efficiency show an ''inverted U-shape'' with a positive spatial spillover effect on the green economy efficiency of neighboring regions; this conclusion is robust. (3) The green economy effect of sports industry agglomeration is more significant in the central and western regions, regions with strict environmental regulations, and regions with a higher willingness for resident participation in sports due to industrial density, compliance costs, and characteristics of sports industry development. (4) Sports industry agglomeration can promote regional green economy efficiency by escaping natural resource dependence and increasing healthy human capital; technological innovation, rationalization of industrial structure, and labor transfer serve as ''inverted U-shaped'' mediators between sports industry agglomeration and regional green economy efficiency. This study expands the meso- and spatial-level perspectives of the impact of the agglomeration of green industries and comprehensive industries on green development. It is of great theoretical and practical importance for promoting the construction of a regional green industrial system and the high-quality development of the green economy.

Starting from the theoretical mechanism of the impact of financial agglomeration on regional green innovation efficiency, and using panel data from 30 provinces in China from 2009 to 2017, this study calculates a financial agglomeration index and green innovation efficiency applying principal component analysis and the slacks-based measure (SBM) of super-efficiency. The study then constructs static and dynamic spatial Durbin models using two different spatial weight matrices to empirically test the spatial spillover effect of financial agglomeration on regional green innovation efficiency. The results reveal a significant positive spatial correlation between China's inter-provincial green innovation efficiency and financial agglomeration. Both static and dynamic models demonstrate that financial agglomeration has a significant spatial spillover effect on the green innovation efficiency of adjacent provinces, but the static model overestimates the impact of spatial spillover. In addition, human capital level, technology market activity, and environmental regulation all have significant positive impacts on the green innovation efficiency of provinces and adjacent provinces.

Under the new development pattern, green low-carbon and digital economy become two mainstream development directions in China. Against the background 'dual carbon' strategies, based on the data of China between 2010 and 2018 at the city level, The paper adopts dynamic spatial Durbin models to investigate the causal links causal between digital economy and carbon intensity by constructing different spatial weight matrices, and explore the influence of human capital with threshold model. Results show that：(1) Urban digital economy and carbon intensity show significant positive spatial correlation characteristics. The carbon reduction of digital economy has obvious spatial spillover effect under different spatial weight matrices. (2) Industrial structure upgrading, technological innovation and resource allocation optimization are effective channels through which digital economy contributes to carbon emission reduction. (3) A double-threshold effect of human capital is evident in the carbon reduction of digital economy. The findings offer new perspectives and empirical evidence for understanding the causality relation between the digital economy and carbon emission, and those conclusions have important policy implications for how to promote the digital economy development and thus achieve the 'double carbon goal'. [ABSTRACT FROM AUTHOR]

[Objective] With the deepening of the digital economy, digital inclusive finance has become an important path for reducing the carbon emission intensity of agriculture. This study explored the relationship between digital inclusive finance and the carbon emission intensity of agriculture. [Methods] Starting from the spatial effects and influencing mechanisms, this study used digital inclusive finance and agricultural carbon emission intensity data from 31 provinces in China from 2011 to 2020 and kernel density estimation and spatial autocorrelation analysis of the characteristics of agricultural carbon emission intensity, the dynamic spatial Durbin model, and mediation effect models to analyze the impact of digital inclusive finance on agricultural carbon emission intensity. [Results] The results show that: (1) The level of agricultural carbon emission intensity was continuously decreasing, with strong spatial heterogeneity and clear agglomeration characteristics, while the spatial pattern of digital inclusive finance and agricultural carbon emission intensity exhibited certain mismatch in their development. (2) Digital inclusive finance had a significant negative effect on agricultural carbon emission intensity, and the conclusion is robust based on endogeneity tests, sample exclusions, and policy effect tests. Broader coverage and higher level of digitalization of digital inclusive finance can significantly inhibit agricultural carbon emission intensity. (3) Digital inclusive finance affects the level of agricultural carbon emissions by influencing the level of agricultural technology innovation and entrepreneurial activities, and urbanization and marketization levels have significant moderating effects that can strengthen the carbon reduction effect of digital finance. [Conclusion] It is recommended to further optimize the organizational model of digital inclusive finance to support low-carbon agricultural development, strengthen the construction of digital infrastructure to stimulate agricultural innovation and entrepreneurship, and thus promote the development of low-carbon agriculture.

Improving the net carbon sink efficiency of mariculture is one of the important ways for China to achieve the goal of carbon neutrality. This paper used the super efficiency SBM model to calculate the net carbon sink efficiency of mariculture in 9 coastal provinces of China from 2008 to 2019, and analyzed its global and local spatial auto-correlation. Then, the dynamic spatial durbin model was used to explore the influencing factors of the net carbon sink efficiency of mariculture and its spatial spillover effect. The results showed that: (1) The net carbon sink efficiency of mariculture in China was generally high and rising, but there were obvious regional differences. (2) From the global perspective, the net carbon sink efficiency of mariculture between provinces had a spatial positive correlation, and the agglomeration effect was significant, but the degree of spatial agglomeration gradually weakened with the passage of time; from the local perspective, the spatial evolution path of net carbon sink efficiency in some provinces had not changed during the study period, showing a certain path dependence. (3) Science and technology promotion and yield structure had a promoting effect on the net carbon sink efficiency of mariculture, while economic scale and professional personnel investment intensity had an inhibitory effect; from the perspective of spatial spillover effect, professional personnel investment intensity,science and technology promotion and yield structure had positive spillover effect on the net carbon sink efficiency of adjacent areas, while economic scale had negative spillover effect.

Based on the panel data of 284 prefecture-level cities in China, this paper uses the dynamic spatial Durbin model to explore the impact of digital finance on green innovation from the dimensions of "quantity" and "quality." The results show that digital finance has a positive impact on both the quality and quantity of green innovation in local cities, but the development of digital finance in neighboring cities has a negative impact on the quantity and quality of green innovation in local cities, and the impact on the quality of green innovation is greater than that on the quantity of green innovation. And after a series of robustness tests, it was shown that the above conclusions are robust. In addition, digital finance can have a positive impact on green innovation mainly through industrial structure upgrading and informatization level. Heterogeneity analysis shows that the breadth of coverage and the degree of digitization are significantly related to green innovation, and digital finance has a more significant positive impact in eastern cities than in mid-western cities. [ABSTRACT FROM AUTHOR]

The recent multidimensional crisis has harmed the global economy because countries are currently integrated. In this study, we examine the spillover effect of the United States (US) interest rate on the output gap of ASEAN+3 countries using the dynamic spatial Durbin model from 2010 to 2020. After controlling for various institutional variables, this analysis further examines the effect of the exchange rate and the COVID-19 pandemic on the output gap. This model captures the spatial interactions and short- and long-term effects of interest rate monetary policies. The findings indicate a negative spillover effect of the US central bank interest rate on ASEAN+3 countries’ output gap. The result had significant direct and indirect short-term effects. Additionally, exchange rates negatively affect the output gap. The output gap decreased because of the COVID-19 pandemic. This study is expected to be useful for monetary authorities in developing the best monetary policy for economic recovery.

IntroductionThe global spread of the COVID-19 has brought about global changes, especially in terms of economic growth. Therefore, it has become a global issue to explore the impact of public health security on the economy.MethodsEmploying a dynamic spatial Durbin model, this study analyzes the spatial linkage mechanism of medical level, public health security, and economic climate in 19 countries as well as investigates the relationship between economic climate and COVID-19 by the panel data of 19 OECD European Union countries from March 2020 to September 2022.ResultsResults show that an improvement in the medical level can reduce the negative impact of public health security on the economy. Specifically, there is a significant spatial spillover effect. The degree of economic prosperity hurts the reproduction rate of COVID-19.DiscussionPolicymakers should consider both the severity of the public health security issues and the economic level when developing prevention and control policies. Given this, corresponding suggestions provide theoretical support for formulating policies to reduce the economic impact of public health security issues.

Regarding the goal of "carbon peaking and carbon neutrality," an urgent practical problem is how green finance decreases carbon emissions. This paper uses the dynamic spatial Durbin model and China's provincial panel data to empirically test the impact of green credit on carbon emissions. The results show that green credit effectively curbs carbon emissions, and has significant spatial spillover effects on the carbon emissions. In the short run, the spillover effects of green credit on carbon emissions are greater than the direct effects, but in the long run, the direct effects of green credit on carbon emissions are greater than the spillover effects. The mechanism test finds that upgrading industrial structure is an important transmission channel for green credit to affect carbon emissions, and energy consumption only plays a part of the intermediary role in the direct impact of green credit on carbon emissions. However, green credit has not achieved the goal of carbon emission reduction by promoting low-carbon technology innovation. [ABSTRACT FROM AUTHOR]

Low-carbon innovation plays an essential role in carbon reduction worldwide. This study investigates the nexus between low-carbon innovation, economic growth, and carbon emissions by the dynamic spatial Durbin model from 2007 to 2020. First, the Moran index results verify the provincial spatial agglomeration of carbon emissions. High-emission provinces concentrate in major economic zones and energy extraction areas. Second, the effect decomposition results show that long-term and short-term effects are consistent. Low-carbon innovation has a significant mitigation effect on carbon emissions in local regions, which effect, however, is not significant in the adjacent areas. The environmental Kuznets curve hypothesis is validated locally, but all provinces and cities have not reached the inflection point of the environmental Kuznets curve, and the linkage effect in adjacent regions remains insignificant. The above results have been tested to be robust. Third, the results of the mechanism analysis show that environmental policies, absorptive capacity, and financial development play a moderating role in the relationship between low-carbon innovation and carbon emissions. Finally, the heterogeneity test showed significant differences between Eastern, Central, and Western. The direct effect of low-carbon innovation exists in Eastern and central regions; the spillover effect of low-carbon innovation is only in the eastern region. In addition, corresponding measures are proposed based on the conclusions. [ABSTRACT FROM AUTHOR]

Information and communication technology (ICT) has exerted a great impact on the socio-economic development; however, this development has come with some potential influence on climate change. The academics are divided over this issue; some argue that ICT has contributed to carbon reduction, while others consider that ICT has increased carbon dioxide (CO2) emissions. Undoubtedly, the existing literature abounds in the relationship between ICT and CO2 emissions, but little attention has been paid to the spillover effect of ICT and CO2 emissions, especially in developing countries. Hence, with the panel data of 285 China's prefecture-level cities from 2004 to 2018, this study innovatively discusses the spatial and mechanism effects of ICT on CO2 emissions, further exploring the heterogeneous impact of ICT on CO2 emissions from multiple perspectives. The empirical results confirm the positive relationship between ICT and CO2 emissions and identify the spatial spillover effect in the relationship. Furthermore, notably, the intermediary effect of energy consumption on the impact of ICT on CO2 emissions is identified. Finally, due to the differences in the geographical position, population size, and urban agglomeration of prefecture-level cities in China, the impact of ICT on CO2 emissions varies in different cities. The findings not only contribute to advancing the existing literature but also have a significant and targeted policy guiding significance for the cities to maximize the favorable influences of ICT and promote the low-carbon transformation of the whole society. [ABSTRACT FROM AUTHOR]

Based on the panel data from 30 provincial capitals in China from 2005 to 2019, the level of green development in China's provincial capitals was measured using the entropy value method, and their spatial distribution was analyzed using ArcGIS software. The impact of excise tax and technological innovation on the green development of China's provincial capitals was examined by constructing a dynamic spatial Durbin model. This study shows that: (1) There are spatial differences in the green development level of the cities, with the overall characteristics of "east > central > west"; (2) The level of green development in Chinese cities has "time inertia" and positive "spatial spillover" effect; (3) Excise tax can promote green development in cities, and it exerts differential effects on green development in the eastern, central and western regions; (4) The results of the mediating effects model demonstrate that technological innovation mediates the relationship between consumption tax and green development in China's provincial capitals. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Technology Economics 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.)

As a composite indicator that incorporates economic efficiency and environmental protection, ecological efficiency is a valuable tool for measuring regional green development and accelerating regional green transformation. As the economy transitions, Chinese economic growth targets affect local governments' behaviors, thereby impacting ecological efficiency. In this study, the ecological efficiency level of 284 cities in China was measured using the EBM-DEA method from 2007 to 2019, and the spatial exploration analysis method and the dynamic double fixed effect spatial Durbin model were applied to analyze urban ecological efficiency's spatial correlations, impacts, and mechanisms. The conclusions are as follows: China's urban ecological efficiency has increased over time. At the spatial level, it shows the distribution characteristics of east > northeast > middle > west. In terms of spatial agglomeration, there are typically spatial agglomerations, high–high agglomerations, and low–low agglomerations in Chinese cities' ecological efficiency. There is an inverted U-shaped relationship between economic growth target and ecological efficiency. According to regional differences, the economic growth target in the eastern region has a U-shaped impact on ecological efficiency, while in the central, northeast, and western cities they have an inverted U-shaped effect on ecological efficiency. In terms of the impact mechanism, through the intermediary effect test, it is found that appropriate economic growth target setting can promote the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure. Excessive economic growth target setting can inhibit the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure. The proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure can promote ecological efficiency. The enlightenment is as follows: China should weaken the economic growth target in official promotion assessment, set differentiated economic growth targets for different regions, and increase the proportion of energy conservation and environmental protection expenditure and fiscal science and technology expenditure to promote ecological efficiency. [ABSTRACT FROM AUTHOR]

Faced with increasingly severe climate and energy crises, promoting renewable energy development has become an inevitable choice for realizing carbon reduction strategy and energy structure transformation. This paper selects the panel data of 30 provinces in China from 2001 to 2021 and constructs a dynamic spatial Durbin model to study the effect of renewable energy development on carbon emission intensity. The study shows a significant positive spatial correlation between renewable energy development and carbon emission intensity in Chinese provinces. Renewable energy development can significantly reduce carbon emission intensity, and affect the carbon emission intensity of neighboring regions through spatial spillover effects in both the short and long term. In the central-western and southern regions, energy-rich regions, regions with high levels of environmental pollution, high levels of fiscal decentralization, and high levels of green finance, the inhibitory effect of renewable energy development on carbon emission intensity is more significant, suggests that its impact effects are characterized by heterogeneity. Enhancing energy use efficiency, promoting industrial structure optimization, and strengthening green technology innovation capability can significantly improve the inhibition effect of renewable energy development on carbon emission intensity. • Renewable energy development has a spatial effect on reducing carbon emission intensity. • Different types of renewable energy have different effects on reducing carbon intensity. • The impact of renewable energy development on carbon emission intensity has significant heterogeneity. • Energy use efficiency, industrial structure optimization and green technology innovation have moderating effects. [ABSTRACT FROM AUTHOR]

• A method is developed for coupling NTL data with energy-related carbon emissions. • A grid-scale long-term series of carbon emissions is constructed. • The spatiotemporal dynamics of carbon emissions in urban agglomerations was analyzed. • The dynamic effects of drivers are discussed. • The long-term effects of carbon emissions are even more significant. Exploring the issues related to carbon emissions (CEs) in urban agglomerations (UAs) is an effective way to address global warming and achieve sustainable development. We propose a method to measure CEs using nighttime light data to characterize grid-scale CEs with high accuracy. Using seven UAs in the Yellow River Basin of China as examples, the dynamic evolution trend and dynamic spillover effect are discussed. The results show that there are significant correlations between CEs and nighttime light data, the R2 values of the fitting function are all greater than 0.9, and the maximum error is 0.1942%, indicating that the fitting results are good. The CEs of UAs exhibit significant club convergence and siphon effects, revealing certain spatial agglomeration characteristics during the study period. In addition, CEs exhibit a significant spatial spillover effect, their long-term impact is greater than their short-term impact, and they display a significant snowball effect. This study further expands the application of the spatial scale of CE measurement, and the research results provide new ideas for developing countries to formulate adaptive CE reduction measures. [ABSTRACT FROM AUTHOR]

Haze pollution not only has negative impact on public health and air quality, but also has restricted China’s industrial upgrading and high-quality development, and Chinese urban agglomerations are one of the areas hardest hit of haze pollution. In the process of China’s economic transformation, local governments will adopt local protectionism, leading to market segmentation. This is a phenomenon that refers to the distortion of resource allocation by local governments for the sake of vested interests and the existence of segmentation in commodity markets. This behavior is considered to be one of the important factors causing haze pollution. As Yangtze River Delta urban agglomerations are considered to be one of the earliest, fastest growing, and most mature for market integration in China, there is a lack of empirical testing on the impact of market segmentation on haze pollution in this urban agglomeration. Based on urban panel data from the period of 1998–2018 and the market segmentation index calculated by the relative price method, we use the dynamic spatial Durbin model and generalized space two-stage least squares method to explore the effect of market segmentation in urban agglomeration on haze pollution; the results are as follows: (1) Market segmentation significantly exacerbates haze pollution, in other words, haze pollution will increase by 2.14% when market segmentation increases by 1%. (2) Cities with a high degree of market segmentation and high levels of haze pollution have the potential to reduce pollution through market integration in the future. (3) Market segmentation in surrounding regions also has a significant worsening effect on haze pollution in the region. The indirect effect of market segmentation is 3.67 times that direct effect, indicating that the spatial spillover effect of market segmentation on haze pollution is greater than its own impact. (4) Mechanism analysis finds that it will aggravate haze pollution by hindering economic scale, industrial structure, and technological progress when the degree of market segmentation is high.

The integrated development of agriculture and tourism is conducive to the realization of agricultural ecological value, which will promote the green development of agriculture and improve the green total factor productivity of agriculture as well. Based on panel data in China from 2008 to 2019, the super-efficiency SBM method and the coupling coordination degree model were used to estimate the agricultural green total factor productivity (AGTFP) and the integration level of agriculture and tourism (ATL). The dynamic spatial Durbin model and threshold effect model were used to demonstrate the effects and characteristics of the agriculture and tourism integration on AGTFP. Results showed that: 1) During the study period, AGTFP and ATL increased steadily, and showed obvious spatial agglomeration characteristics; 2) The integration of agriculture and tourism will directly promote the improvement of AGTFP in the local region, and this impact has a spatial spillover effect. The direct effect in the central region in China is the strongest, and the spillover effect in the eastern region is the largest. 3) The influence of the agriculture and tourism integration on AGTFP was enhanced with the improvement of ATL, showing a threshold characteristic. From the perspective of subregion, the threshold value of ATL in the eastern region is the lowest, while the threshold value in the western region is the highest. The results of this study provide useful enlightenment for promoting the deep integration of agriculture and tourism and improvement of AGTFP so as to promote the green development of agriculture.

Since the reform and opening-up, China has made remarkable achievements in economic growth, but also led to a substantial increase in carbon emissions. The Chinese government has actively formulated energy intensity reduction targets and taken carbon emission reduction measures. The paper investigates the impact of energy intensity reduction targets on carbon emissions using a dynamic spatial Durbin model based on panel data from 30 provinces in China from 2006 to 2019. The results show that energy intensity reduction targets promote the reduction of local carbon emissions, but have a positive spillover effect on carbon emissions in adjacent regions. Meanwhile, green technology innovation has a non-linear moderating effect between energy intensity reduction targets and carbon emissions. Energy intensity reduction targets promote carbon emission reduction when green technology innovation is less than a threshold, while the promotion effect disappears when green technology innovation exceeds a threshold. The mechanism analysis shows that energy consumption structure is a channel through which energy intensity reduction targets affect carbon emissions in both local and adjacent regions. Further research found that peer competitive pressure promotes carbon emission reduction and alleviates pollution spillover, while central assessment pressure increases carbon emissions and aggravates pollution spillover. Based on the above findings, this study provides suggestions for policymakers aiming at carbon emission reduction by implementing target management policies and optimizing target management systems.

Based on the data of explicit debt and implicit debt of local governments in China from 2012 to 2020, this paper theoretically analyzes and empirically tests the spatial correlation effect of fiscal decentralization and intergovernmental competition on local government debt risk. It’s found that local government debt risk has obvious inertia effect in time and positive spatial correlation effect in space, and local fiscal spending decentralization and intergovernmental competition have a significant positive impact on the local government debt risk, as well as a positive spatial spillover effect. The research of this paper has very important policy implications for perfecting China’s decentralization system, standardizing the competition among governments, and preventing and defusing local government debt risks.

Environment and poverty are the focus of global concern, and green poverty reduction is China's strategic choice to deal with these two major problems. However, due to the vast territory, there are regional differences in environment and poverty in China. On the basis of this, selecting renewable resource utilization, environmental protection, and incidence of poverty as measurement indicators from two dimensions of green and poverty reduction, this paper employed the analysis of spatial autocorrelation and spatial econometric regression based on dynamic spatial Durbin model to explore the internal mechanism of green and poverty in rural areas of 21 provinces in China. The results show that there was a significant spatial autocorrelation in the poverty in rural areas among provinces, which shows that poverty reduction has significant regional connections within spatial scopes. Meanwhile, it is green that has a spatial spillover effect on poverty reduction. Therefore, on the one hand, intergovernmental governments should establish effective communication and cooperation mechanisms between regions; on the other hand, intergovernmental governments should pay enough attention to the spatial spillover effects of green on poverty reduction in green poverty reduction, so as to promote green poverty reduction to achieve overall and sustainable development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Humanities & Social Sciences is the property of Journal of Shenzhen University (Humanities & Social Sciences) Editorial Office 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.)

Copyright of Science Technology & Industry / Keji he Chanye 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.)

Background: Since the 20th century, pursuing Universal Health Coverage (UHC) has emerged as an important developmental objective in numerous countries and across the global health community. With the intricate ramifications of population mobility (PM), the government faces a mounting imperative to judiciously deploy health expenditure to realise UHC effectively., Objective: This study aimed to construct a comprehensive UHC index for China, assess the spatial effects of Government Health Expenditure (GHE) on UHC, and explore the moderating effects of PM on this association., Method: A Dynamic Spatial Durbin Model (DSDM) was employed to investigate the influence of the GHE on UHC. Therefore, we tested the moderating effect of PM., Results: In the short-term, the GHE negatively impacted local UHC. However, it enhanced the UHC in neighbouring regions. Over the long term, GHE improved local UHC but decreased UHC in neighbouring regions. In the short-term, when the PM exceeded 1.42, the GHE increased the local UHC. Over the long term, when the PM exceeded 1.107, the GHE impeded local UHC. If the PM exceeded 0.91 in the long term, the GHE promoted UHC in neighbouring regions. The results of this study offer a partial explanation of GHE decisions and behaviours., Conclusions: To enhance UHC, a viable strategy involves augmenting vertical transfer payments from the central government to local governments. Local governments should institute healthcare systems tailored to the urban scale and developmental stages, with due consideration for PM. Optimising the information disclosure mechanism is also a worthwhile endeavour.

The determinants of foreign direct investment (FDI) always attract the attention of researchers and policymakers. However, the spatial spillovers of FDI have not been given due attention. This study analyses the dynamic spatial effects of determinants of FDI in emerging markets: a case of the southern key economic region of Vietnam. The model employed in the research is the Dynamic Spatial Durbin Model for testing hypotheses of data collected from the General Statistics Office of Vietnam in the period of 2005–2016. The results show that there is a spatial interaction in FDI attraction to the above‐mentioned region. At the same time, factors such as market size, trade openness, population growth, agglomeration and institutional quality affect FDI inflows in the short and long term. In particular, the population growth of a locality reduces FDI inflows of that locality and neighbouring localities. Enterprise agglomeration of a province increases the FDI inflows of that province and reduces FDI inflows in adjacent provinces. This paper's findings highlight the determinants of FDI and spatial spillovers of FDI in emerging markets. In addition, previous models have not fully identified the spatial effects of FDI in the short and long term, which may skew policy decisions. Therefore, the article contributes to redefining the key factors to attract FDI inflows into emerging markets like Vietnam. [ABSTRACT FROM AUTHOR]