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In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units are crucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals.

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With the global land use/land cover (LULC) and climate change, the ecological resilience (ER) in typical Karst areas has become the focus of attention. Its future development trend and its spatial response to natural and anthropogenic factors are crucial for understanding the changes of ecologically fragile areas to human behavior. However, there is still a lack of relevant quantitative research. The study systematically analyzed the characteristics of LULC changes in Southwest China with typical Karst over the past 20 years. Drawing on the landscape ecology research paradigm, a potential-elasticity-stability ER assessment model was constructed. Revealing the characteristics and heterogeneity of the spatial distribution, annual evolution, and development trend of ER in the past and under different scenarios of shared socioeconomic pathways and representative concentration pathways (SSP-RCP) in the future. In addition, the spatial econometric model was utilized to reveal the spatial effect response mechanism of ER, and adaptive development strategies were proposed to promote the sustainable development of Southwest China. The study found that : (1) In the past 20 years, the LULC in Southwest China showed an accelerated change trend, the ER decreased declined in general, and there was significant spatial heterogeneity, showing the spatial distribution pattern of "west is larger than east, south is larger than north, and reduction in the west was slower than that in the east." (2) Under the same SSP scenario, with the increase of RCP emission concentration, the area of the lowest-resilience increased significantly, and the area of the highest-resilience decreased. (3) The woodland was the largest contributor to ER per unit area in the Southwest China, and grassland was the main LULC type, which had a prominent impact on the ER of the study area. (4) The average precipitation and the normalized difference vegetation index (NDVI) were significant natural drivers of ER in the study area, and the economic growth, innovation, and optimization of industrial structure contributed to the ER of Southwest China. Overall, the integration of quantitative assessment and multi-scenario-based modeling not only provides new perspectives for understanding the pattern of change and response mechanisms, but also provides valuable references for other typical Karst regions around the world to achieve sustainable development.

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In light of the escalating global climate risks threatening human survival, there is a global consensus on the necessity for collaborative reduction of pollutant and carbon emissions (CRPC). Within this context, digital inclusive finance (DIF) is recognized for its unique inclusiveness and digital characteristics as a critical factor in promoting environmentally friendly and sustainable development. DIF provides advantageous channels for environmental governance, thereby making the achievement of CRPC objectives feasible. However, the impact of DIF on CRPC has not been fully explored. This study employs a spatial econometric model to investigate the impact of DIF on CRPC in 278 prefecture-level cities in China from 2011 to 2020. The findings indicate that DIF has a positive impact on CRPC, with significant spatial spillover effects. The analysis highlights the pivotal mediating roles played by technology effect and electrified effect of the energy mix, while environmental regulation effect plays a moderating role. Notably, disparities in the impact of DIF on CRPC are evident, particularly in non-resource-based cities, cities with low carbon intensity, and eastern regions where spatial spillover effects are more pronounced. These experiences enrich the relevant thesis in terms of DIF on CRPC, providing a theoretical basis for formulating CRPC schemes.

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Clarifying the mechanism of influence of urban form on carbon emissions is an important prerequisite for achieving urban carbon emission reduction. Taking the Yangtze River Economic Belt as an example, this study elaborated on the general mechanism of urban form on carbon emissions, used multi-source data to quantitatively evaluate the urban form, and explored the impacts of urban form indicators on carbon emissions from 2005 to 2020 at global and sub-regional scales with the help of spatial econometric models and geodetector, respectively. The results showed that:① The carbon emissions of the Yangtze River Economic Belt increased from 2 365.31 Mt to 4 230.67 Mt, but the growth rate gradually decreased. Its spatial distribution pattern was bipolar, with high-value areas mainly distributed in core cities such as Shanghai and Chongqing and low-value areas concentrated in the western regions of Sichuan and Yunnan. ② The area of construction land in the study area expanded over the past 15 years, but the population density of construction land had been decreasing. The degree of urban fragmentation was decreasing, and the difference between cities was also progressively narrowing. The average regularity of urban shape improved, and the compactness increased significantly. ③ All indicators of urban scale had significant positive effects on carbon emissions at the global scale, urban fragmentation had a significant negative effect in 2005, and the effective mesh size (MESH) indicator of urban compactness showed a significant negative correlation with carbon emissions in the study period. ④ Total class area, patch density, and effective mesh size had the most significant impacts on carbon emissions in upstream cities. Effective mesh size, mean perimeter-area ratio, and total class area had higher influences in midstream cities. Effective mesh size, percentage of like adjacencies, and largest patch index were the key factors to promote carbon reduction in downstream cities. Cities in different regions should comprehensively consider the impacts of various urban form indicators on carbon emissions and then optimize their urban form to promote sustainable development.

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Utilizing city-level data from China, the paper employs a spatial econometric analysis to investigate the impact of fiscal decentralization on urban pollution. Empirical evidence indicates: (1) In the context of the emphasis of ecological civilization construction in China, an increase of fiscal autonomy for local governments is conducive to mitigating urban pollution intensity. Specifically, fiscal decentralization in one city not only promotes a reduction in local pollution intensity but alleviates environmental pollution problems in adjacent cities through spatial spillover effects. (2) Industrial structure upgrading and green technology progress become crucial measures for local governments to realize pollution reduction targets through fiscal expenditure. (3) Heterogeneity analysis reveals that the positive significance of decentralization is most prominent in the eastern China, while local governments with fiscal autonomy in central region tend to transfer pollution to neighboring cities. (4) There is a threshold characteristic for fiscal decentralization to promote a reduction in urban pollution intensity, and its marginal effect becomes more significant accompanied by continuous introduction of sophisticated foreign direct investment. Finally, the paper summarizes the potential significance of fiscal decentralization among Chinese local governments against the background of "Chinese-style decentralization" and proposes corresponding policy recommendations.

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Background: Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Methods: Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. Results: The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. Conclusion: There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

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Sub-Saharan Africa (SSA) is seeing exceptional urbanization and economic expansion rates. Therefore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) parameters and the spatial econometric framework are used in this work to examine the influence of economic growth and urbanization on SSA's CO2 emissions. Likewise, to determine the spatial effect and understand how factors influence the spatial dependence of carbon emissions, the study builds a spatial Durbin model (SDM). In line with the findings, the spatial correlation test revealed the spatial correlations across various countries. This indicates that the changes in sub-Saharan African country's CO2 emissions impacted nearby countries and the countries themselves. Additionally, the findings reveal that, in the SSA's countries, urbanization, economic growth, industrial structure, trade, and population, excluding energy intensity, which failed the significant test, all positively influence CO2 outflows, in line with the spatial econometric model's findings. Thus, energy intensity shares an adverse impact on carbon emissions. As an outcome, energy intensity reduces carbon dioxide emissions in nearby nations and the entire region. Thus, the study recommends that policymakers account for the effects of spatial spillover when establishing low-carbon policies, encouraging a low-carbon lifestyle, promoting environmentally friendly technologies, and improving regional collaboration.

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This article aims to precisely evaluate the catalytic impact of digital inclusive finance on economic growth, enhance the implementation of policies pertaining to digital inclusive finance, and foster high-quality economic development. Based on China's provincial panel data and the digital inclusive finance index from 2011 to 2021, this research investigates the influence of digital inclusive finance on high-quality economic development and the associated underlying mechanisms. The findings suggest that digital inclusive finance exerts a notable spatial impact on high-quality economic development. Moreover, there is heterogeneity in the spatial effects between different dimensions of digital inclusive finance and high-quality economic development. Through the threshold model and intermediary effect model, it is found that the Internet penetration rate has a dual-threshold effect on the impact of digital inclusive finance on promoting high-quality economic development. Specifically, digital inclusive finance contributes to elevating the level of high-quality economic development through its role in promoting the transformation of consumption structure. The findings of this study offer valuable insights for countries aiming to attain high-quality economic development through the enhancement of digital inclusive finance.

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Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

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Background Regional differences in economic development, natural environment, health care level, and social structure may lead to differences in the provincial distribution of the health status of the elderly population. Objective To explore the provincial distribution characteristics, regional differences, and influencing factors of the self-assessed health of the elderly population, with the aim of providing a policy basis for improving the health of the elderly population and promoting healthy aging according to local conditions. Methods Using 31 provinces (municipalities and autonomous regions) in China as the basicstudy unit and based on the method of Wagstaff, the self-rated health data of the elderly population (aged 60 years and above) in each province from the 2010 and 2020 national censuses and the 2015 1% National Population Sample Survey were converted into ill-health scores as a measure of self-assessed health, and higher scores represented worse health status perception. Global Moran's I was used to evaluate spatial autocorrelation, range [−1, 1], with a value of 1 as a perfect clustered pattern. Local Moran's I was used to evaluate the tendency of local autocorrelation, and high-high aggregation/low-low aggregation indicated that both target province and its neighboring provinces showed higher/lower ill-health scores. Spatial econometric models were selected by Lagrange multiplier test and Hausman test to explore influencing factors of the self-assessed health of the elderly population. Results In 2010, 2015, and 2020, the national ill-health scores of the elderly population were 1.831, 1.873, and 1.547, respectively, and the corresponding Global Moran's I statistics were 0.347, 0.482, and 0.511, respectively (P<0.01), indicating that the ill-health scores of the elderly population showed a significant spatial positive autocorrelation, and the degree of spatial aggregation was increasing gradually. From 2010 to 2020, the high-high aggregation of ill-health scores among the elderly population was concentrated in the inland northwest, while the low-low aggregation was concentrated in the southeast coast, gradually showing a "southeast-central-northwest" stepped incremental pattern of differentiation. The Lagrange multiplier test and Hausman test suggested that the fixed-effects spatial lagged model was a better choice, and the regression model showed a spatial autocorrelation in the ill-health scores of the elderly population, with an autocorrelation coefficient of 0.3969 (P<0.001); the ill-health scores of the elderly population were negatively correlated with the natural logarithms of gross regional product per capita, and the number of beds in health care facilities per 1000 population, with regression coefficients of −0.8297 and −0.0454 (P<0.05) respectively, and positively correlated with the annual average concentration of PM2.5, illiteracy rate, and the number of health technicians per 1000 population, with regression coefficients of 0.0033, 0.0297, and 0.0765 (P<0.05), respectively. Conclusion From 2010 to 2020, the overall self-assessed health level of China's elderly population showed an upward trend and a spatial positive autocorrelation, with better self-assessed health in the southeast coast and poorer ratings in the northwestern inland. Additionally, there was a gradual decline in self-assessed health of the elderly population from the southeast to the central regions and further to the northwest in terms of spatial distribution. Economic development level, environmental pollution, health resource allocation, and education level are important factors influencing the self-assessed health of the elderly population.

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There has always been controversy over how renewable energy technologies can play a role in reducing carbon emissions. Based on the energy patent data and the economic data of 244 prefecture-level cities from 2007 to 2017 in China, we explore the carbon reduction effect of renewable energy technology and its mechanism from the perspective of energy production, conservation, and management. The two-way fixed effect, instrumental variable, spatial Durbin, and mediation effect models are employed to explore empirical results. We found that (1) the impact of renewable energy technologies on carbon emissions is nonlinear, with an inverted U shape. However, this inverted U-shaped relationship only exists locally in cities and there are uncertainties in adjacent cities, which indicates that cross-regional cooperation in renewable energy technology needs to be improved. (2) The mechanism analysis shows that industrial agglomeration and energy consumption scale are the channels that renewable energy technologies affect carbon emissions. Thus, the implicit carbon emissions generated by industrial agglomeration and the failure to green upgrade energy consumption are the main reasons for the inverted U-shaped relationship. (3) The carbon reduction effect of renewable energy technologies of conservation type takes effect first, and renewable energy technologies of production type do not reduce carbon emissions in non-eastern cities, which means that non-eastern cities are likely to become pollution havens. This study provides evidence for renewable energy technologies to achieve efficient carbon emission reduction and cross-regional technical cooperation.

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As the novel coronavirus disease (COVID-19) has been rapidly spreading across the world, scholars have started paying attention to risk factors that affect the occurrence of the infectious disease. While various urban characteristics have been shown to influence the outbreak, less is known about whether COVID-19 is more likely to be transmitted in areas with a greater number of incidents of previous infectious diseases. This study examines a spatial relationship between COVID-19 and previous infectious diseases from a spatial perspective. Using the confirmed cases of COVID-19 and other types of infectious diseases across South Korea, we identified spatial clusters through regression and spatial econometric models. We found that COVID-19-confirmed case rates tended to be clustered despite no similarity with the spatial patterns of previous infectious diseases. Existing infectious diseases from abroad were associated with the occurrence of COVID-19, while the effect diminished after controlling for the spatial effect. Our findings highlight the importance of regional-level infectious disease surveillance for the effective prevention and control of COVID-19.

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Accurately assessing the impact of low-carbon urban construction on green economic development has great significance for achieving economic development with environmental protection, and for building an ecological civilization and a beautiful China. Based on panel data for 271 cities in China from 2004 to 2019, multi-period and spatial difference-in-difference econometric models were used to comprehensively investigate the impact of three batches of low-carbon city pilot policies on green economic development, finding the following: The contribution of low-carbon urban construction on urban green economic development is significant and positive, and still holds under a series of robustness tests. Parallel trend tests also show a lag in the policy effect, and the effect is strengthened over policy implementation time. Green orientation of technological progress, green transformation of industry, and green upgrade of consumption are important channels for the effect of the policies. The promotion effect of low-carbon city construction is stronger in the central and northern cities, and in cities with high green economic development, than in western and southern cities, and those with low green economic development. Construction of low-carbon pilot cities not only promotes their own green economic development, but also that in neighboring cities, exerting a demonstration effect. This effect is greater in urban areas. This study provides empirical support for policy planning to promote low-carbon urban construction across the country.

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Green technology innovation is one of the driving forces of industrial structure upgrading. This innovation is thought to be related to environmental regulation. The study uses panel data for 30 Chinese provinces and cities from 2009 to 2020 and presents a comprehensive research-based explanation of how environmental regulations impact green innovation. This study employs the spatial Durbin model to analyze the spillover effect of the region. The results show that the total impact of environmental regulations is 0.223%, of which the direct effect is 0.099%. This impact includes the effects of both formal and informal environmental regulation. It indicates that ecological regulations significantly enhance green technology innovation. Furthermore, the spatial spillover effect is significantly positive at the 1% level with a coefficient of 0.124. Such spillover effects represent a learning effect of regional environmental regulation. Based on the results, the study suggests a few policy measures based on the detailed outcomes.

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Foreign direct investment and environmental pollution have been a prominent topic in China's economic development. Therefore, this paper investigates the relationship between foreign direct investment and environmental pollution, and makes practical policy recommendations to support the harmonious development of economy and environment based on the study findings. In this paper, the spatial econometric approach framework with the spatial weight matrix of the Delaunay method is used to examine the influence of foreign direct investment on the level of urban air quality. It uses air quality index as an indicator to measure air pollution comprehensively. Based on the spatial autocorrelation with the aggregation and radiation effects of foreign direct investment, the results show that the distribution of air pollution has considerable spatial impact in terms of spatial nonequilibrium. Through the empirical analysis, our finding reveals that the increase in GDP, the share of secondary industry, and population growth are the important elements affecting the change in air quality.

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The topic of air pollution and its effect on public health has become a hot policy issue that has attracted worldwide attention, but this attention has seldom been extended to the causal relationship between atmospheric environmental policy (AEP), air pollution, and public health. This paper uses panel data from 30 provinces in China to construct spatial econometric models that analyze the impact of AEP on air pollution, the impact of air pollution on public health, and the mediation effect that air pollution may have between AEP and public health. The results demonstrate that there is a significant positive spatial spillover effect of soot and dust (SD) emission intensity and the overall air pollution level as measured by the Air Pollution Index (API). The AEP has significant inhibitory effects on the intensity of sulfur dioxide and SD emissions, as well as on overall air pollution. An increase in the overall air pollution level has a significant detrimental effect on public health as measured by average life expectancy. Air pollution as measured by API is a mediating factor in the relationship between AEP and public health. The study results could help to effectively control air pollution and promote public health by leading to improvements in regional pollution prevention and control mechanisms and strengthening of the central government's policy formulation and local governments' policy implementation process.

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Financial elements and R&D elements are significant drivers for enhancing regional innovation efficiency. This paper measures financial elements by financial development scale and the marketization level of the financial industry and R&D elements by the inputs and flow of R&D personnel and R&D capital and specifically considers R&D element flow to quantify the consequential spatial spillover effects. Based on provincial panel data from 2008 to 2018, the paper firstly estimates the regional innovation efficiency of China's 30 provincial-level administrative regions using super-efficiency DEA and then conducts an empirical analysis of the influence of financial elements and R&D elements on regional innovation efficiency by the use of the Tobit model and three spatial econometric models. It is found that, by and large, the financial development scale, the marketization level of the financial industry, the inputs of R&D personnel and R&D capital, and R&D capital flow all have significant effects on regional innovation efficiency. Nonetheless, by region, R&D personnel flow in central China can significantly boost regional innovation efficiency while fails in eastern and western China. From the spatial perspective, both financial elements and R&D elements have significant positive spatial spillover effects. Therefore, in order to bolster regional innovation efficiency, it is crucial to improve the allocation of financial elements and R&D elements and build a tight regional collaborative innovation network.

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According to the stratified poverty theory, poverty includes individual (people) and regional (place) poverty. Understanding the interaction mechanism between individual poverty and regional poverty is crucial to achieving the UN goal of poverty eradication by 2030. However, at present, the relevant empirical research is still limited by the availability of data. To fill this important gap, based on the multi-source data of poverty census, geo-environmental and socio-economic data of China's 1587 counties in 2013, we used exploratory spatial data analysis (ESDA) and spatial econometric models (spatial-lag and spatial-error model) to identify determinants of individual poverty and regional poverty in this county. Results show that the spatial distribution of the rural poor in China had strong spatial dependence (Global Moran's I = 0.574). There was a high degree of spatial overlap between individual poverty and regional poverty. The poverty-causing factors were complex and varied across regions and individuals. Disease of family members was the leading factor driving rural areas in Northeast, Central, and Southwest China. Northeast China was mainly affected by the illness and lack of labor skills of family members. The complex terrain conditions were the determinants driving rural poverty in most areas of China. Improved transportation can greatly reduce rural poverty. Geographical isolation or lack of geographical capital caused by complex terrain conditions, backward transportation, and regional closure promoted regional poverty. In turn, regional poverty-causing factors further restricted the improvement of rural residents' self-development ability and aggravated individual poverty. Our findings indicate that individual poverty and regional poverty have different poverty-causing mechanisms and poverty reduction priorities. Effective poverty reduction strategies require the coordinated promotion of individual and regional poverty reduction. The reduction of individual poverty should focus on enhancing the livelihood capital of the poor through differentiated policy intervention, while regional poverty alleviation should focus on creating a favorable development environment by increasing infrastructure investment and public service supply.

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The objective of this study is to attempt to assess the effect of green finance in reducing carbon emissions in China, analyze the transformative role of policy impact in the development of green finance markets, and investigate the impact mechanisms of how green finance affects carbon dioxide emissions. Our time frame from 2007 to 2018 is selected for the empirical study by integrating the availability of data due to the scarcity of relevant statistics in the early days of green finance. Location of this study is in China where 30 provinces are included, excluding Tibet due to severe data shortage. As for methodology, we construct a green finance evaluation index system containing five indicators by entropy weight method, choose dynamic spatial Durbin model (DSDM) for empirical research, and perform mechanism analysis of restructuring industry and greening technology as intermediary channel. Our findings demonstrate that green finance in China does significantly reduce carbon emissions, and its spatial spillover effect and long-term effect are also verified. Furthermore, green finance tends to reduce CO2 emissions through restructuring industry and greening technology. Correspondingly, policy implications are recommended. First, improving green financial market and strengthening information disclosure of green financial market are crucial to facilitate green finance development. Local governments formulate carbon emission reduction strategies focusing on space by joint conference or coordination mechanism like river head system. Lastly, a mechanism should be developed to strengthen the transformation of industrial structure and to promote greening technology.

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Green finance has the obvious impetus function to economic ecologization development. Through the test of the spatial agglomeration degree of China's green finance and ecological economic development index from 2001 to 2017 (30 provinces except for Hong Kong, Tibet, Taiwan, and Macao), this paper analyzes the spatial correlation of the green finance index and the ecological economic development index. It uses the spatial panel econometric regression analysis model to reveal the impact of Green Finance on China's economic ecology. Results show that (1) the development level of green finance and economic ecologization in China has improved to varying degrees in different periods. However, there are still noticeable regional differences between the eastern, central, western, and northeast regions; (2) green finance plays a positive role in promoting economic ecologization. Developing green finance in one province can promote its economic ecologization and positively impact on economic ecologization of surrounding provinces. (3) Economic ecologization also has a positive spillover effect. The improvement of the ecological economic level of a province can drive the improvement of the ecological economic level of other provinces. Therefore, improving green finance across the country has become a meaningful way to promote economic ecology and promote China's high-quality development.

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