Population mobility : spatial spillover effect of government health expenditure in China

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.

Exploring spatiotemporal trends and impacts of health resources and services on under-5 mortality in West African countries, 2010-2019: a spatial data analysis

Background

West African countries experienced the highest under-5 mortality rate (U5MR), the lowest life expectancy, and the poorest economic development. This study aimed to explore the spatiotemporal trend of U5MR and spatial spillover effects of health resources and services to provide a basis for establishing health policies and international cooperative mechanisms in West Africa.

Methods

We retrieved data from the World Health Organization's Global Health Observatory, the United Nations Human Development Report, and the Global Burden of Disease Study 2019. Joinpoint regression analysis and Moran's I method were used to examine the temporal trend and spatial dependence of U5MR, respectively. Spatial regression analysis was used to examine the spatial spillover effects.

Results

The long-term downward trends in U5MR were divided into multiple segments by two or three change points in 2013, 2014, or 2015, and the annual percentage change after 2015 was higher than before 2015. Global Moran's I was positive, significantly indicating positive spatial autocorrelation, which increased from 0.2850 (p = 0.0210) to 0.3597 (p = 0.0080). Based on spatial regression analysis, human development index (HDI), physicians density, nurses and midwives density, health center density, percentage of infants lacking immunization for diphtheria and measles, and coverage rate of at least one antenatal visit had negative spatial spillover effects on U5MR. HDI had the strongest negative correlation (β = -0.0187 to -0.1054, p < 0.0001). Current health expenditure (CHE) per capita had positive spatial spillover effects on U5MR.

Conclusion

This study revealed the spatiotemporal trend of U5MR in West African countries and spatial spillover effects of health resources and services. Promoting economic development, increasing health human resources, health expenditure, vaccination rate, antenatal care coverage, and the proportion of health professionals attending births not only reduced the local U5MR but also exerted spatial spillover effects on adjacent countries. The West African Health Organization may consider regional spillover mechanisms to develop regional health policy and intervention cooperation mechanisms, which will contribute to achieving the sustainable development goal on U5MR, Africa Agenda 2063, and universal health coverage.

The spatial effect of digital economy on public psychological resilience during the diffusive crisis

Purpose

To explore whether the digital economy has spatial effects and spatial heterogeneity on public psychological resilience during the diffusive crisis and to analyze the specific impact mechanisms.

Methods

This study is based on the Baidu Search Index from 2011 to 2020 and the provincial panel data of 30 provinces in China. It constructs measures of public psychological resilience and digital economy development level and employs the spatial Durbin model to empirically analyze the relationship between the two, revealing their spatial impact.

Results

(1) Public psychological resilience exhibits a spatial distribution characterized by high values in the west, medium values in the central region, and low values in the east, while the digital economy development level shows a "U"-shaped spatial structure with high levels in the eastern and western regions and low levels in the middle; (2) The digital economy development level in a local region has a negative effect on the public psychological resilience of that region, while the digital economy development level in surrounding regions has a positive spatial spillover effect on the local region's public psychological resilience.

Conclusion

It is essential to strengthen crisis management, focus on the coordinated development of the digital economy in different regions, share the benefits of digital society development more equitably and broadly, and further improve the psychological resilience of regions under the context of digital economy development.

Can Green-Technology Innovation Reduce Atmospheric Environmental Pollution?

Rapid economic growth leads to such problems as resource scarcity and environmental degradation. Local governments successively take measures such as technological innovation to solve atmospheric environmental pollution; however, technological innovation fails to fundamentally alleviate atmospheric environmental pollution. Therefore, local governments come to realize the importance of green-technology innovation, which means an inevitable choice for various countries in the world to seek long-term development and win competitive advantage. Under such circumstances, this paper chooses the panel data of 30 provinces and regions in China from 2005 to 2018, takes environmental regulation as the threshold variable, and empirically analyzes the relationship between green-technology innovation and atmospheric environmental pollution by constructing a Spatial Measurement Model and Panel Regression Model. As evinced, green-technology innovation has a significant inhibitory effect and a spatial spillover effect on atmospheric environmental pollution. When environmental regulation reaches a level of intensity, green-technology innovation can effectively curb atmospheric environmental pollution. Accordingly, relevant parties should strengthen green-technology innovation, coordinate the development of the governance system of green-technology innovation, establish a joint prevention and control mechanism, increase the investment in green technology research and development, and augment the role of green-technology innovation.

The Low-Carbon Policy and Urban Green Total Factor Energy Efficiency: Evidence from a Spatial Difference-in-Difference Method

In the post-epidemic background of the low-carbon economy and sustainable development, the low-carbon city pilot program (LCCP) is viewed as a practical method of improving energy efficiency. This study explores the spatial spillover effects of LCCP on green total factor energy efficiency (GTFEE) by developing a spatial difference-in-difference (SDID) model. Furthermore, we apply the mediating effects model to verify whether the rational allocation of resources is an influential channel for the spillover effect of LCCP policies. The results indicate that the LCCP policy has not only improved the local GTFEE by approximately 1.8%, but it also has a profound impact on the surrounding regions as well, which is about 76.5% that of the pilot cities. Additionally, the estimated results of the mediating effect model indicate that optimizing labor force and capital allocations are two essential channels through which the LCCP policy may contribute to improving regional cities' GTFEE. Accordingly, the pilot cities should establish specific measures for rational resource allocation and promote the spatial spillover model of sustainable development.

The Spatial Spillover Effect of Clean Energy Development on Economic Development: A Case of Theoretical and Empirical Analyses from China

Does clean energy development (CED) have a spatial spillover effect on economic growth (EG)? Using the panel data of 30 provincial administrative units from 2000 to 2019 in China, this study empirically investigates the spatial spillover effect of CED on EG. From the perspective of the supply side rather than the consumption side, using the spatial Durbin model (SDM), the study finds that CED does not have a significant impact on EG, while there is an apparent positive spillover effect of CED on EG in China, meaning that CED in one province can boost EG in the surrounding provinces. Theoretically, this paper provides a new perspective for studying the relationship between CED and EG. In practice, it provides a reference for further improving the government's future energy policy.

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China's AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

Pollution Effect of the Agglomeration of Thermal Power and Other Air Pollution-Intensive Industries in China

With the rapid development of the Chinese economy, air pollution is becoming increasingly serious, which greatly impacts the lives and activities of people, and the spatial agglomeration of thermal power and other air pollution-intensive industries (TPAPIs) is an important factor. The purpose of this article is to find the air pollution-intensive industries scientifically, to analyze the effects of pollution from TPAPIs in China, and to provide a basis for the planning and adjustment of TPAPIs. In this study, an air pollution index was adopted to identify TPAPIs, a location quotient was employed to measure the agglomeration of TPAPIs in China, and the global Moran's I index was determined to examine the spatial agglomeration characteristics of these industries and the spatial characteristics of air pollution. On this basis, a spatial panel Durbin model describing atmospheric pollution was constructed. The pollution effects of the agglomeration of TPAPIs were examined in regard to spatial agglomeration and spillover effects. In the study, it was found that the agglomeration of TPAPIs in different regions of China exhibited a significant positive spatial correlation, and spatial dependence becomes increasingly notable. A significant inverted U-shaped relationship was found to exist between the spatial agglomeration of TPAPIs and air pollution, and thus the spatial agglomeration of TPAPIs imposes a significant spatial spillover effect on air pollution. Specific policy suggestions are proposed, such as the formulation of science-based policies targeting TPAPIs, promotion of interregional cooperation, establishment of a regional joint prevention and control mechanism, and effective elimination of the excess capacity of outdated TPAPIs.

Driving Factors and Spatiotemporal Characteristics of CO2 Emissions from Marine Fisheries in China: A Commonly Neglected Carbon-Intensive Sector

The CO₂ emissions from marine fisheries have a significant impact on marine ecology, despite generally being overlooked in studies on global climate change. Few studies have estimated the carbon emissions from marine fisheries while taking into account all pertinent sectors. This study evaluated marine fisheries' CO₂ emissions based on three sectors: marine fishing, mariculture, and the marine aquatic product processing industry. Kernel density estimation and the spatial Durbin model were used to investigate the spatial and temporal characteristics and the key socioeconomic drivers of the CO₂ emissions from marine fisheries in 11 coastal provinces of China from 2005 to 2020. The results are as follows: (1) marine fishing is the sector that produces the most CO₂ emissions; trawling operations generate more CO₂ than all other modes of operation combined; (2) China's marine fisheries' CO₂ emissions show a rising, then declining, trend, with significant differences in coastal provinces; (3) the development of the marine fishery economy and trade have a positive driving effect on CO₂ emissions, the expansion of the tertiary industry does not decrease CO₂, the technical advancement and income growth of fishermen are negatively related to carbon emissions, and the effect of environmental regulation has failed to pass the significance test; (4) the carbon emissions of marine fisheries have significant spatial spillover effects.

Identifying impacts of industrial co-agglomeration on carbon emissions: Evidence from China

Based on panel data of 285 cities in China at the prefecture level and above from 2005 to 2020, this paper aims to study the nexus between industrial co-agglomeration and carbon emissions from dual perspectives including space and time. It adopts multiple approaches including a dynamic general method of moment, panel quantile regression model, panel threshold model, and dynamic spatial Durbin model. The non-spatial empirical results support the establishment of the threshold effect and the imbalance effect. The spatial empirical results indicate that industrial co-agglomeration poses a dramatic stimulating effect on urban carbon emissions, and its spatial spillover effect and spatial heterogeneity are conditionally established. Furthermore, heterogeneous effects are supported, such as the positive spillover effects of industrial co-agglomeration are more significant in western cities, resource-oriented cities, and non-low-carbon pilot cities. The heterogeneous influence of cost factors on industrial agglomeration and carbon emissions has also been partially confirmed. In terms of the channels and mechanism of action, the negative externalities of industrial co-agglomeration occupy a dominant position in the current status of economic development. The dynamic equilibrium between government intervention and marketization is a solid foundation for the optimization of carbon emission reduction paths.

A Key to Stimulate Green Technology Innovation in China: The Expansion of High-Speed Railways

Mankind is seeking a green development path. This paper places emphasis on whether high-speed railways (HSRs), as a typical representative of green transportation, can effectively promote green technology innovation in cities. Based on the panel data of 286 Chinese prefecture-level cities from 2007 to 2018, we employ the Panel Negative Binomial Regression Model and the Spatial Dubin Model for empirical analysis. The results illustrate that the expansion of HSRs not only has a direct and substantial promotion influence on local green technology innovation but also on the surrounding area. We further find that circulation node cities reap more benefits of the opening of HSRs than other ordinary cities. The higher the degree of marketization, the weaker the marginal impact of HSRs on green technology innovation. Meanwhile, the mechanism test confirms that HSRs can indirectly stimulate the progress of green technology innovation by influencing the creative class flow and the government's environmental concerns. Our findings present new insights for enhancing green technology innovation and provide policy recommendations for local governments to take advantage of HSRs to obtain resources.

How Big Data Affect Urban Low-Carbon Transformation-A Quasi-Natural Experiment from China

As a new factor of production, data play a key role in driving low-carbon and sustainable development relying on the digital economy. However, previous studies have ignored this point. Based on the panel data of 283 cities in China from 2007 to 2019, we investigated the construction of national big data comprehensive pilot zones (NBDCPZs) in China as a quasi-natural experiment, using the difference-in-differences (DID) model to empirically test the impact of NBDCPZ policies on urban low-carbon transformation. The following conclusions can be drawn: NBDCPZ construction significantly promotes urban low-carbon transformation, and a series of robustness analysis supports this conclusion. NBDCPZ constructions mainly promotes urban low-carbon transformation by stimulating urban green innovation and optimizing the allocation of urban resource elements. Compared with eastern cities, small and medium-sized cities, and resource-based cities, the construction of NBDCPZs can promote the low-carbon transformation of cities in central and western China, large cities, and non-resource-based cities. Further analysis shows that the construction of NBDCPZs can only improve the low-carbon transformation of local cities, with negative spatial spillover effects on the low-carbon transformation of surrounding cities. Therefore, in the future, it is vital to consider the promotion effect of the construction of NBDCPZs on the low-carbon transformation of local cities and prevent its negative impact on the low-carbon transformation of surrounding cities.

Dynamic Evolution, Regional Differences, and Spatial Spillover Effects of Urban Ecological Welfare Performance in China from the Perspective of Ecological Value

Ecological welfare performance (EWP) is a necessary condition for achieving sustainable economic development and is a crucial initiative for resolving the dilemma of balancing economic development, social welfare, ecology, and the environment. This paper constructs and enhances a comprehensive evaluation system of ecological welfare performance (EWP) from an ecological value viewpoint for the purpose of making the results of the evaluation both comprehensive and objective. In the meantime, the Dagum Gini decomposition, kernel density, and the spatial Durbin model were initiated to measure and analyze urban EWP, which supplies new empirical results for studies on the dynamic evolution, regional differences and driving factors of urban EWP. The findings indicate the following: (1) In each spatial dimension, the urban EWP roughly demonstrates first a decreased and then an increased trend. There is a discrepancy in the east-central-west distribution of urban EWP in space, in which urban EWP in the east and west is larger than that in the central area. (2) For relative differences, intra-regional and inter-regional differences in urban EWP are significantly spatially uneven. Supervariable density is the main source of regional differences. For absolute differences, the EWP demonstrates a significant polarization effect. (3) The urban EWP does not have σ-convergence; nonetheless, it has spatial absolute β-convergence and spatial conditional β-convergence. (4) The urban EWP has a significant spatial correlation. Industrial structure, science and technology innovation, foreign investment, urbanization, government intervention, finance development, and environmental regulations all have influence effects and spatial effects on urban EWP; notwithstanding, the direction and magnitude of the effects vary across the different spatial dimensions.

The Relationship between Environmental Regulation, Green-Technology Innovation and Green Total-Factor Productivity-Evidence from 279 Cities in China

This paper employs the SBM-DDF method to measure the index of green total-factor productivity (GTFP), based on the panel data of 279 prefecture-level cities in China from 2007 to 2019, and constructs a spatial Durbin model (SDM) and a threshold effect to empirically test the effects of dual environmental-regulations and green technological innovation on GTFP. The results are as follows: (1) the SDM supports a nonlinear contribution of dual environmental-regulations spillover to GTFP. The relationship between formal environmental-regulation and GTFP is an inverted U-shape, while a U-shaped nonlinear relationship is found between informal environmental regulation and GTFP. (2) Green technology innovation has a significant negative moderating effect on the process of dual environmental-regulations affecting GTFP in local regions, but a positive moderating effect on informal environmental regulation in neighboring regions. (3) There is a significant green technology innovation threshold effect of dual environmental-regulations affecting GTFP. Specifically, the promotion effect of dual environmental-regulations on GFFP gradually increases as the level of green technology innovation increases.

Does air quality improvement promote enterprise productivity increase? Based on the spatial spillover effect of 242 cities in China

Introduction

Air pollution not only harms people's health, but also impedes urban economic development. This study aims to analyze how air quality improvement affects enterprise productivity. And then from regional and time heterogeneities' aspects to investigate if the air quality improvement increase enterprise productivity.

Methods

The data were obtained from China Industrial Enterprise Database and China Patent Database,and this study used Spatial Durbin Model to analyze how air quality improvement affects enterprise productivity.

Results

The results show that: (1) air quality improvement and its spatial spillover effect can significantly increase enterprise productivity in adjacent areas. (2) After 2010, the government implemented more stringent measures to prevent and control air pollution, which made the air quality improvement promote enterprise productivity increase more obviously. The air quality improvement in eastern and central regions was less obvious than in western regions. (3) Air quality improvement can increase enterprise productivity by improving enterprise innovation quality, ensuring the health of urban residents, and increasing the stock of urban human capital.

Conclusion

Air quality improvement and its spatial spillover effect can significantly increase enterprise productivity in adjacent areas. So this study puts forward some policy enlightenment, such as establishing an air pollution detection system, using an intelligent network supervision platform, and implementing a coordinated defense and governance system.

Moving towards Environmental Sustainability: Can Digital Economy Reduce Environmental Degradation in China?

In the context of environmental sustainability and accelerated digital technology development, China attaches great importance to the prominent role of digital economy in addressing environmental degradation. Utilizing Chinese provincial panel data from 2011 to 2019, this study investigates whether the digital economy can improve China's environmental sustainability proxy by reducing carbon emission intensity. Based on the fixed effects model, the findings reveal that the digital economy has a significant negative effect on carbon emission intensity and the conclusion remains robust after conducting several robustness checks. However, this impact shows regional heterogeneity, which is more effective in resource-based eastern regions and the Belt and Road provinces. Moreover, mediating effect analyses indicate that the transmission mechanisms are energy consumption structure, total factor energy productivity, and green technology innovation. Furthermore, the results based on the spatial Durbin model (SDM) demonstrate that digital economy development has a significant spatial spillover effect. Finally, on the basis of results analysis and discussion, policy recommendations are provided for achieving environmental sustainability.

Can Green Innovation Improve Regional Environmental Carrying Capacity? An Empirical Analysis from China

Green innovation has become an important driving force for China's economic transformation and development. This paper selects the 2010-2020 provincial-level regions in China as samples, and adopts a multi-indicator comprehensive evaluation method to comprehensively, objectively and scientifically evaluate the environmental carrying capacity of air pollution in two dimensions: natural resource endowment and human activity impact, and also measures and calculates the green innovation in each province, city and autonomous region to explore the specific impact of green innovation on environmental carrying capacity and its spatial spillover effect; it also explores the heterogeneous effects of green innovation on environmental carrying capacity under different pollution environments. The conclusions show that: (1) Green innovation has a positive impact on environmental carrying capacity. (2) There is a spatial spillover effect of green innovation on environmental carrying capacity. In other words, in areas with higher PM2.5 concentration, that is, lower environmental quality, green innovation has a weaker ability to improve environmental carrying capacity; in areas with lower PM2.5 concentration, that is higher environmental quality, green innovation has a stronger ability to improve environmental carrying capacity. (3) In the process of green innovation affecting environmental carrying capacity, PM2.5 plays the part of a mediating effect, indicating that green innovation is an intermediate transmission mechanism affecting environmental carrying capacity, and the results show that the absolute value of the short-term indirect effect is greater than the absolute value of the short-term direct effect, and the long-term direct effect is greater than the long-term indirect effect.

Efficiency measurement and spatial spillover effect of provincial health systems in China: Based on the two-stage network DEA model

The effectiveness of a health care system is an important factor for improving people's health and quality of life. The purpose of this research is to analyze the efficiency and spatial spillover effects of provincial health systems in China using panel data from 2009 to 2020. We employ the two-stage network DEA model to evaluate their efficiencies and use a spatial econometric model for empirical estimation. The results suggest that the overall efficiency, resource allocation efficiency, and service operation efficiency of health systems in different regions of China generally have fluctuating upward trends, with large differences in efficiency among the various regions. Further analysis reveals that the efficiency of China's health system has a significant spatial spillover effect. The level of economic development, fiscal decentralization and old-age dependency ratio are important factors affecting the health system efficiency. Our findings help to identify the efficiency and internal operating mechanisms of China's health system at different stages, and are expected to contribute to policymakers' efforts to build a high-quality health service system.

Impact of Coastal Urbanization on Marine Pollution: Evidence from China

The increasing marine pollution in China's coastal areas has seriously affected the sustainable development of the economy and the living standards of residents. It is of great significance to explore the relationship between urbanization and marine pollution in coastal areas for the sustainable development of coastal cities. Based on the marine pollution data and nighttime light (NTL) data of 46 coastal cities from 2006 to 2015, the paper discusses the impact of urbanization on marine pollution by using the generalized spatial two-stage least square method (GS2SlS), and analyzes the role of technological innovation, financial development, and human capital in the impact of urbanization on marine pollution by using the three-stage least square method (3SLS). Results show that China's coastal marine pollution has a strong spatial spillover effect, and a U-shaped relationship exists between urbanization and marine pollution. Regional heterogeneity analysis shows that an inverted U-shaped relationship was found between coastal urbanization and marine pollution in the northern marine economic circle, while the eastern and southern marine economic circles have a U-shaped correlation. The heterogeneity of the urbanization pattern indicates that the relationship between different urbanization patterns and marine pollution in coastal areas is generally in a positive correlation stage, but the depth of urbanization occupies a dominant position. Further mechanism tests show that urbanization can effectively reduce coastal marine pollution and improve the marine environment through the technological innovation effect, financial development effect, and human capital effect.

The effects of environmental tax reform on urban air pollution: A quasi-natural experiment based on the Environmental Protection Tax Law

Air pollution significantly impacts sustainable development and public health. Taking the implementation of China's Environmental Protection Tax Law in China as a quasi-natural experiment, this paper employs the difference-in-differences (DID) and spatial DID models to evaluate the effects of environmental tax reform on urban air pollution. The findings are as follows. (1) Environmental tax reform can significantly reduce urban air pollution, and a series of robustness tests have also been conducted to provide further evidence. (2) Green technology innovation and industrial structure upgrading from a vital transmission mechanism for environmental tax reform to improve air quality. (3) Environmental tax reform significantly inhibits urban air pollution in cities located north of the Qinling-Huaihe line and big cities. (4) Moreover, environmental tax reform not only promotes the improvement of local air quality but also has a significant negative spatial spillover effect, reducing air pollution in neighboring cities. The research conclusions provide theoretical support and policy suggestions for promoting sustainable economic development, rationally optimizing environmental protection tax policies and improving urban air quality.

Spatial Spillover Effect of Rural Labor Transfer on the Eco-Efficiency of Cultivated Land Use: Evidence from China

In this study, the influence of rural labor transfer and its spatial spillover effect on the eco-efficiency of cultivated land use (ECLU) in different regions were investigated using the undesirable super-efficiency epsilon based measure (EBM) and spatial Durbin models and data of 31 Chinese provinces for the period 1990-2018. The results show that: (1) China's rural labor transfer rate increased; (2) in the east region, the ECLU has exceeded the national average level since 2001. In the west and northeast regions, the ECLU was higher, whereas it remained below the national average level in Central China; (3) in the whole country, west, and northeast regions, the effect of rural labor transfer on the ECLU was first negative and then positive, whereas it was insignificant in East and Central China. In Central, West, and Northeast China, the effect of the labor transfer on the ECLU had significant spatial spillover effects; (4) a significant U-shaped trend was observed between the local labor transfer and ECLU in the whole country, west, and northeast regions. A positive linear correlation was determined for Central China; labor transfer in other regions had significant indirect effects on the ECLU in Central and Northeastern China. In conclusion, China's rural labor transfer had a significant spatial spillover effect on the ECLU, and differences were observed between East, Central, West, and Northeast China.

Spatio-Temporal Heterogeneity of Carbon Emissions and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2006 to 2019

The Yellow River Economic Belt (YREB) performs an essential function in the low-carbon development of China as an important ecological protection barrier, and it is of great importance to identify its spatio-temporal heterogeneity and key influencing factors. In this study, we propose a comprehensively empirical framework to conduct this issue. The STIRPAT model was applied to determine the influencing factors of carbon emissions in the YREB from 2006 to 2019. The results show that the carbon emissions in the YREB had significant clustering characteristics in the spatial auto-correlation analysis. In addition, the estimation results of the spatial panel analysis demonstrate that the carbon emissions showed a distinct spatial lag effect and temporal lag effect. Moreover, the three traditional factors including population, affluence, technology are identified as the key influencing factors of carbon emissions in the YREB of China. Furthermore, the spatio-temporal heterogeneity is illustrated vividly by employing the GTWR-STIRPAT model. Finally, policy implications are provided to respond to the demand for low-carbon development.

Evaluation of Tourism Development Efficiency and Spatial Spillover Effect Based on EBM Model: The Case of Hainan Island, China

Tourism development efficiency is one of the key scales to measure the development quality of tourism destination. This study improves the existing input–output index system of tourism efficiency evaluation; knowledge innovation is introduced into the input index, and environmental health pressure is introduced into the output index. Based on the case of Hainan Island, we used the EBM model compatible with radial and non-radial data to evaluate the tourism development efficiency. In order to make up the deficiency of spatial effect analysis based on the geographical distance weight matrix, the spatial spillover effect of tourism development in Hainan Island was analyzed based on a geographical distance weight matrix and an economic distance weight matrix. The findings indicate that nearly 20 years of the Hainan tourism development efficiency mean value was 0.7435, represented by Sanya, and Haikou city of Hainan’s tourism industry development level was higher. However, the spatial spillover effect of Hainan’s overall tourism development is not good. In addition to Tunchang, Ledong city suggests that an appropriate increase in tourism elements, such as investment, expands the scale of the tourism industry, and most cities follow the law of diminishing marginal utility and inappropriate scale blindly. Especially in the face of knowledge innovation becoming the main factor hindering the efficiency of tourism development, we should pay more attention to technological innovation and management reform and coordinate the relationship between tourism development and ecological environment protection.

Whether Green Finance Can Effectively Moderate the Green Technology Innovation Effect of Heterogeneous Environmental Regulation

As an essential way to promote ecological civilization, green finance is attracting wide attention. However, whether green finance can successfully regulate the green technology innovation effect of heterogeneous environmental regulations and boost green technology innovation in coordination with heterogeneous environmental regulations remains unclear. Based on the re-measurement of the green finance development index of various provinces and cities in China, this study uses the spatial Durbin model to test the above problems empirically. The results show that green finance and “market incentive” environmental regulations can promote regional green technology innovation, while “command and control” environmental regulations inhibit regional green technology innovation. Green finance plays a negative regulatory role in the mechanism of heterogeneous environmental regulations affecting green technology innovation. Green finance alleviates the negative impact of “command and control” environmental regulations on green technology innovation and weakens the positive impact of “market-incentive” environmental regulations on green technology innovation. In terms of spillover effects, green finance can effectively promote green technology innovation in neighboring regions, while heterogeneous environmental regulations have a crowding-out effect on green technology innovation in neighboring regions.

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

[Spatial effects and influencing factors of natural capital utilization in the Yangtze River Economic Belt, China]

Clarifying the regional natural capital utilization and its spatial effects and influencing factors has important theoretical and practical significance for regional sustainable development. Here, we used a three-dimensional ecological footprint model, exploratory spatial data analysis, a spatial Markov chain, and a spatial panel Durbin model to explore the spatial effects and influencing factors of overall and subcategory natural capital flow and stock occupancy of 112 prefecture-level cities in the Yangtze River Economic Belt from 2008 to 2018. The results showed that the total, per capita, and subcategory natural capital flow and stock occupancy increased during the study period. Most of the regions with low overall and subcategory natural flow occupancy were located in the East, whereas most of the regions with high overall and subcategory natural flow occupancy were mostly located in the Central and Wes-tern area. However, their stock occupancy behaved differently. Except for farmland stock, the overall and subcategory natural capital flow and stock occupancy showed spatial agglomeration. The traditional and spatial Markov chain presented a "Matthew effect" and a "spatial spillover effect" in natural capital flow and stock occupancy. Economic growth and industrial structure positively affected natural capital flow and stock occupancy. Population growth positively affected natural capital flow occupancy. Urbanization rate negatively affected natural capital flow occupancy, and positively impacted natural capital stock occupancy. Government intervention negatively affected the farmland flow and fossil fuels land stock occupancy. Environmental governance negatively affected the natural capital flow occupancy, but positively affected the natural capital stock occupancy. Environmental pollution positively affected natural capital flow and stock occupancy.

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Urban population density distribution contributes towards a deeper understanding of peoples’ activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of “rapid growth-gentle decline-rapid growth-rapid decline”. Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

Does Low-Carbon City Pilot Policy Alleviate Urban Haze Pollution? Empirical Evidence from a Quasi-Natural Experiment in China

As a comprehensive environmental regulation, the low-carbon city pilot policy (LCCP) may have an impact on haze pollution. The evaluation of the effectiveness of LCCP on haze pollution is greatly significant for air pollution prevention and control. Taking LCCP as the starting point, in this study we constructed DID, PSM-DID, and intermediary effect models to empirically test the impact and mechanism of LCCP on haze pollution, based on the panel data of 271 cities in China from 2005 to 2018. The findings show that (1) LCCP has significantly reduced the urban haze pollution, and the average annual concentration of PM2.5 in pilot cities decreased by 14.29%. (2) LCCP can inhibit haze pollution by promoting technological innovation, upgrading the industrial structure, and reducing energy consumption. Among these impacts, the effect of technological innovation is the strongest, followed by industrial structure, and energy consumption. (3) LCCP has significantly curbed the haze pollution of non-resource dependent cities, Eastern cities, and large cities, but exerted little impact on resource-dependent cities, Central and Western regions, and small and medium-sized cities. (4) LCCP has a spatial spillover effect. It can inhibit the haze pollution of adjacent cities through demonstration and warning effects. This study enriches the relevant research on LCCP and provides empirical support and policy enlightenment for pollution reduction.

Will Policy Uncertainty Deteriorate Haze Pollution? A Spatial Spillover Perspective

Haze has been a severe problem in China for some time, jeopardizing air quality, public health and sustainable growth. This paper examines the direct effect and spatial spillover effect of policy uncertainty on haze pollution with a spatial panel model, using prefecture-level data from 2004 to 2016. This study shows that: (1) policy uncertainty has increased the level of local haze pollution and has a significant spatial spillover effect on surrounding areas; (2) although local policy uncertainty has increased the haze pollution in geographically adjacent cities, it only affects the cities within the province with similar economic distances; and (3) the policy at the central level can effectively alleviate the impact of policy uncertainty at the local level on haze pollution, especially in relation to the spatial spillover effect, but still has limitations in eliminating the direct effect, which is due to the ineradicable nature of policy uncertainty.

Spatial Spillover Effects of Air Pollution on the Health Expenditure of Rural Residents: Based on Spatial Durbin Model

Background: Air pollution is one source of harm to the health of residents, and the impact of air pollution on health expenditure has become a hot topic worldwide. However, few studies aim at the spatial spillover effects of air pollution on the health expenditure of rural residents (HE-RR), including the impact on the health expenditure in neighboring areas. Objective: Based on the existing research, this paper further introduces the spatial dimension and uses the Spatial Durbin model to discuss the impact of environmental pollution on the health expenditure of rural residents (HE-RR). Methods: Based on provincial panel data during 2002–2015 in China, the Spatial Durbin model was used to investigate the spatial spillover effect of the average annual concentration of PM2.5 (AAC-PM2.5) on the health expenditure of rural residents (HE-RR). Results: There was a significant positive correlation between AAC-PM2.5 and health expenditure of rural residents (HE-RR) in neighboring areas at a significant level of 5% (COEF: 2.546, Z: 2.340), that is, AAC-PM2.5 has a spatial spillover effect on PC-HE-RR in neighboring areas, and the spatial spillover effect is greater than the direct effect. The migration and diffusion of PM2.5 pollution will affect the air quality of neighboring areas, leading to the health risk not only from the local PM2.5 pollution but also the nearby PM2.5 pollution. Conclusion: The results show a significant positive relationship between air pollution and HE-RR in neighboring areas, and the spatial spillover effect is greater than the direct effect.

Does Environmental Innovation Improve Environmental Productivity?-An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental innovation on environmental productivity will reveal its economic and environmental effects. This paper measures environmental productivity by value added per unit of pollution emissions (four types of emissions are used) using panel data of 10 Chinese urban agglomerations from 2003 to 2016 to analyze the spatial correlation of environmental productivity, and constructs a spatial panel data model to empirically test the impact of environmental innovation on environmental productivity. It was found that environmental productivity measured by value added per unit of carbon dioxide emissions (gross domestic product (GDP)/CO₂) had a significant positive spatial spillover effect, and measured by value added per unit of sulfur dioxide emissions (GDP/SO₂), smoke (dust) emissions (GDP/SDE), and industrial sewage emissions (GDP/IS) had a significant negative spatial spillover effect. Environmental innovation has a significant negative inhibitory effect on environmental productivity measured by GDP/SDE and GDP/IS, but no obvious effect measured by GDP/CO₂ and GDP/SO₂. Control variables such as economic development level, industrial agglomeration, foreign direct investment, and endowment structure factor also show significant differences in environmental productivity measured by value added per unit of pollution emissions. In addition, there are significant differences in direct effects of explanatory variables on environmental productivity of local regions and indirect effects on neighboring regions. These differences are also related to the types of pollution emissions. Therefore, policymakers should set different policies for different types of pollution and encourage different types of environmental innovation, so as to achieve reduced pollution emissions and improved environmental productivity.

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM_2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM_2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM_2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM_2.5 pollution has a significant temporal lag effect on residents' health burden. PM_2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM_2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM_2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM_2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action

It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.

Intercity Passenger Rails: Facilitating the Spatial Spillover Effects of Population and Employment Growth in the United States, 2000-2010

This research examines the association that intercity passenger rails have with population and employment growth at the county level in the continental United States from 2000 to 2010. This research adopts an integrated spatial regression approach that incorporates both spatial lag and spatial error dependence. The data come from the U.S. Census Bureau, the Bureau of Transportation Statistics, the Land Developability Index, and the National Atlas of the United States. Population and employment change are regressed on intercity passenger rails, controlling for 14 socioeconomic variables. Intercity passenger rails are measured by the number of intercity passenger rail terminals in each county. The results suggest that the associations that intercity passenger rails have with population and employment change are both direct and indirect. Intercity passenger rails have a negative and direct association with population and employment change from 2000 to 2010. The Great Recession during this period may have compelled people to move out of their home county in search of jobs; having intercity passenger rails facilitated this process. The results also indicate that intercity passenger rails have a positive and indirect association with population and employment change. Population and employment change in one county influences those in the adjacent counties. This indirect association shows the spatial spillover effect of population and employment growth through passenger rails. The indirect association does not come from within the county; rather, it is a spread effect from its neighbors. This research suggests that intercity passenger rails, although built long ago, still play an important role in facilitating the spread of change and the integration of local communities into a larger regional economy.