Public health impacts of air pollution from the spatiotemporal heterogeneity perspective: 31 provinces and municipalities in China from 2013 to 2020.

Air pollution has long been a significant environmental health issue. Previous studies have employed diverse methodologies to investigate the impacts of air pollution on public health, yet few have thoroughly examined its spatiotemporal heterogeneity. Based on this, this study investigated the spatiotemporal heterogeneity of the impacts of air pollution on public health in 31 provinces in China from 2013 to 2020 based on the theoretical framework of multifactorial health decision-making and combined with the spatial durbin model and the geographically and temporally weighted regression model. The findings indicate that: (1) Air pollution and public health as measured by the incidence of respiratory diseases (IRD) in China exhibit significant spatial positive correlation and local spatial aggregation. (2) Air pollution demonstrates noteworthy spatial spillover effects. After controlling for economic development and living environment factors, including disposable income, population density, and urbanization rate, the direct and indirect spatial impacts of air pollution on IRD are measured at 3.552 and 2.848, correspondingly. (3) China's IRD is primarily influenced by various factors such as air pollution, economic development, living conditions, and healthcare, and the degree of its influence demonstrates an uneven spatiotemporal distribution trend. The findings of this study hold considerable practical significance for mitigating air pollution and safeguarding public health.

Spatiotemporal Changes and Influencing Factors of Hand, Foot, and Mouth Disease in Guangzhou, China, From 2013 to 2022: Retrospective Analysis.

Background: In the past 10 years, the number of hand, foot, and mouth disease (HFMD) cases reported in Guangzhou, China, has averaged about 60,000 per year. It is necessary to conduct an in-depth analysis to understand the epidemiological pattern and related influencing factors of HFMD in this region. Objective: This study aims to describe the epidemiological characteristics and spatiotemporal distribution of HFMD cases in Guangzhou from 2013 to 2022 and explore the relationship between sociodemographic factors and HFMD incidence. Methods: The data of HFMD cases in Guangzhou come from the Infectious Disease Information Management System of the Guangzhou Center for Disease Control and Prevention. Spatial analysis and space-time scan statistics were used to visualize the spatiotemporal distribution of HFMD cases. Multifactor ordinary minimum regression model, geographically weighted regression, and geographically and temporally weighted regression were used to analyze the influencing factors, including population, economy, education, and medical care. Results: From 2013 to 2022, a total of 599,353 HFMD cases were reported in Guangzhou, with an average annual incidence rate of 403.62/100,000. Children aged 5 years and younger accounted for 93.64% (561,218/599,353) of all cases. HFMD cases showed obvious bimodal distribution characteristics, with the peak period from May to July and the secondary peak period from August to October. HFMDs in Guangzhou exhibited a spatial aggregation trend, with the central urban area showing a pattern of low-low aggregation and the peripheral urban area demonstrating high-high aggregation. High-risk areas showed a dynamic trend of shifting from the west to the east of peripheral urban areas, with coverage first increasing and then decreasing. The geographically and temporally weighted regression model results indicated that population density (ß=-0.016) and average annual income of employees (ß=-0.007) were protective factors for HFMD incidence, while the average number of students in each primary school (ß=1.416) and kindergarten (ß=0.412) was a risk factor. Conclusions: HFMD cases in Guangzhou were mainly infants and young children, and there were obvious differences in time and space. HFMD is highly prevalent in summer and autumn, and peripheral urban areas were identified as high-risk areas. Improving the economic level of peripheral urban areas and reducing the number of students in preschool education institutions are key strategies to controlling HFMD.

[Exploring the Spatial and Temporal Trajectories of Land Use Carbon Emissions and Influencing Factors in the Aksu River Basin from 1990 to 2020].

Land use changes lead to changes in the functions of different types of carbon sources and sinks, which are key sources of carbon emissions. The study of carbon emissions and its influencing factors in the Aksu River Basin from the perspective of land use change is of great importance for the promotion of integrated protection and restoration of mountains, water, forests, fields, lakes, grasslands, sand, and ice in the basin and to help achieve the goal of carbon peaking and carbon neutrality. Based on four periods of land use data and socio-economic data from 1990 to 2020, the total carbon emissions from land use were measured, and the spatial and temporal trajectories of carbon emissions and their influencing factors were explored. The results showed that:① from 1990 to 2020, arable land, forest land, construction land, and unused land showed a general increasing trend, whereas grasslands and water areas showed a decreasing trend. The spatial change in land use types was mainly characterized by the conversion of grasslands and unused land into arable land, and 83.58 % of the arable land conversion areas were concentrated in the southwest of Wensu, Aksu, and the northern part of Awat. ② The total net carbon emissions in the basin showed a continuous growth trend from 1990 to 2020, with a cumulative increase of 14.78×104 t. The increase in arable land was a key factor causing an increase in net carbon emissions in the basin. ③ The spatial distribution pattern of land use carbon emissions in the basin was high in the middle and low in the fourth, with significant changes in net carbon emissions mainly in the southern part of Wensu, Aksu, Awat, and Alaer. ④ Human activities had the strongest driving effect on land use carbon emissions, with their effects gradually increasing from east to west. The contribution of average annual temperature to land use carbon emissions was mainly concentrated in the eastern part of Aksu and the northern part of Awat, whereas average annual rainfall had a strong inhibitory effect on the northern part of Wensu and the western part of Aheqi.

Promoting the sustainable development of traditional villages: Exploring the comprehensive assessment, spatial and temporal evolution, and internal and external impacts of traditional village human settlements in hunan province.

The protection and development of traditional villages are crucial for improving the human settlement suitability (HSS). The paper takes 703 traditional villages in Hunan Province as the research object and establishes the HSS evaluation system by using the pressure-state-response model. Then this paper introduces the vector autoregressive model to explore the interactions and contributions within the three major subsystems. Finally, this paper adopts Geodetector model and GTWR model to study the external driving effects and temporal-spatial influence mechanisms. The main findings of this paper are as follows. First, the overall trend of the composite index of traditional villages is upward. Its spatial pattern transitions from a low index in the northwest to a medium index in the central region and a high index in the southeast. Second, the state system becomes the main driver of the response system change and it is highly influenced by the pressure system. Distance from medical facilities, Distance from educational institutions, Distance from the intangible cultural heritage sites, and Degree of relief are the four most important driving factors affecting the HSS in Hunan Province. At the same time, Distance to medical facilities and Distance to intangible cultural heritage sites have a positive impact, while Distance to educational institutions and Degree of relief have a negative impact. Fourly, these four factors have a significant spatiotemporal impact on the HSS in the Xiangxi region. This paper provides a scientific basis for the sustainable development and conservation of traditional villages in Hunan from multiple perspectives.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in YunnanBorder Regions.

Objective: This study employs the Geographically and Temporally Weighted Regression (GTWR) model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks, emphasizing the spatial-temporal variability of these factors in border regions. Methods: We conducted a descriptive analysis of dengue fever's temporal-spatial distribution in Yunnan border areas. Utilizing annual data from 2013 to 2019, with each county in the Yunnan border serving as a spatial unit, we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region. Results: The GTWR model, proving more effective than Ordinary Least Squares (OLS) analysis, identified significant spatial and temporal heterogeneity in factors influencing dengue fever's spread along the Yunnan border. Notably, the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence, meteorological variables, and imported cases across different counties. Conclusion: In the Yunnan border areas, local dengue incidence is affected by temperature, humidity, precipitation, wind speed, and imported cases, with these factors' influence exhibiting notable spatial and temporal variation.

How does cultivated land fragmentation affect soil erosion: Evidence from the Yangtze River Basin in China.

Reducing soil erosion (SE) is crucial for achieving harmony between human society and the ecological environment. The cultivated land fragmentation (CLF), directly or indirectly, alters soil structure, diminishes its water-holding capacity, and escalates the risk of SE. Scientific assessment of the effect of CLF on SE can provide new insights into controlling of SE across watersheds in China. However, few studies have quantified the effect of CLF on SE. Therefore, we utilized land use change data in the Yangtze River basin from 2000 to 2020, measuring the levels of CLF and SE using Fragstats and InVEST models. The bivariate spatial autocorrelation model was employed to reveal the spatial relationship between CLF and SE. Additionally, we constructed a spatial Durbin model and introduced the geographically and temporally weighted regression model to analyze the role of CLF on SE. The south bank of the upper and middle reaches of the Yangtze River basin exhibited high CLF and SE. The bivariate spatial autocorrelation results showed a significant positive spatial correlation between CLF and SE. The spatial Durbin model results showed that CLF had a spatial spillover effect and time lag on SE, and the effect of CLF on SE had an inverted "N" curve. The study also confirmed that last SE and neighboring SE areas influenced local SE. Currently, CLF had a negative effect on SE in the Sichuan Basin, Yunnan-Guizhou Plateau, and the middle and lower Yangtze River Plain, and positively in Qinghai, Hunan, and Jiangxi provinces. These findings suggest that the government should enhance cross-regional and cross-sectoral cooperation and monitoring of cultivated land changes to prevent and control SE effectively.

Analysis of sustainable water resource management and driving mechanism in arid region: a case study of Xinjiang, China, from 2005 to 2020.

The long-term dynamic comprehensive evaluation of the water resource carrying capacity (WRCC) and the analysis of its potential driving mechanism in arid areas are contemporary research issues and technical means of mitigating and coordinating the conflict between severe resource shortages and human needs. The purpose of this study was to explore the distribution of the WRCC and the spatiotemporal heterogeneity of drivers in arid areas based on an improved two-dimensional spatiotemporal dynamic evaluation model. The results show that (1) the spatial distribution of the WRCC in Xinjiang, China, is high in the north, low in the south, high in the west, and low in the east. (2) From 2005 to 2020, the centers of gravity of the WRCC in northern and southern Xinjiang moved to the southeast and west, respectively, and the spatial distribution exhibited slight diffusion. (3) The factors influencing the WRCC exhibit more obvious spatial and temporal heterogeneity. The domestic waste disposal rate and ecological water use rate were the main factors influencing the WRCC in the early stage, while the GDP per capita gradually played a dominant role in the later stage. (4) In the next 30 years, the WRCC in Xinjiang will increase. The results provide a theoretical reference for the sustainable development of water resources in arid areas.

Factors affecting the incidence of pulmonary tuberculosis based on the GTWR model in China, 2004-2021.

Contra-posing panel data on the incidence of pulmonary tuberculosis (PTB) at the provincial level in China through the years of 2004-2021 and introducing a geographically and temporally weighted regression (GTWR) model were used to explore the effect of various factors on the incidence of PTB from the perspective of spatial heterogeneity. The principal component analysis (PCA) was used to extract the main information from twenty-two indexes under six macro-factors. The main influencing factors were determined by the Spearman correlation and multi-collinearity tests. After fitting different models, the GTWR model was used to analyse and obtain the distribution changes of regression coefficients. Six macro-factors and incidence of PTB were both correlated, and there was no collinearity between the variables. The fitting effect of the GTWR model was better than ordinary least-squares (OLS) and geographically weighted regression (GWR) models. The incidence of PTB in China was mainly affected by six macro-factors, namely medicine and health, transportation, environment, economy, disease, and educational quality. The influence degree showed an unbalanced trend in the spatial and temporal distribution.

Spatial and temporal analysis of influential factors on motor vehicle carbon monoxide emissions in China considering emissions trading scheme.

The number of cars is increasing every year and the environmental aspects of transport are becoming a hot topic. The spatial and temporal patterns of motor vehicle carbon monoxide (CO) emissions are still unclear due to the unbalanced economic development and heterogeneous geographic conditions of China. With the objective of realizing a reduction in motor vehicle CO emissions, his study explores the transport carbon emission reduction pathways of China from motor vehicle CO emission. Firstly, the entropy method is adopted to comprehensively evaluate the CO emissions from motor vehicles in each province; secondly, the development of a Geographically and Temporally Weighted Regression (GTWR) model facilitates the examination of the spatiotemporal dynamics pertaining to the influencing factors of motor vehicle CO emissions within each province.; finally, the characteristics of motor vehicle CO emissions in ETS pilot areas and non-ETS pilot areas are compared. The results show that: (1) After the completion of the six ETS pilot areas in 2011, the CO emission from motor vehicles is reduced by 18% compared with 2010.(2)The entropy method shows that the largest CO emissions from motor vehicles are from Beijing, Shanghai, Guangdong and other provinces with high economic levels.(3) The results of the GTWR model show that the positive effects of economic level, population size, road mileage intensity and motor vehicle intensity on motor vehicle CO emissions are decreasing year by year. The negative effect of metro line intensity on CO emission decreases year by year. This study can help decision makers to identify the high emission areas, understand the influencing factors, and formulate emission reduction measures to achieve the purpose of carbon emission reduction in transport.

[Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area].

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 µg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 µg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 µg·m-3 to 112.780 µg·m-3 and the lowest values ranging from 5.000 µg·m-3 to 7.899 µg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 µg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

Research on the spatiotemporal evolution and driving mechanism of coupling coordinating between green transition of urban land use and urban land use efficiency: a case study of the Yangtze River Delta Region in China.

Analyzing the spatiotemporal characteristics and driving mechanisms of the coupling coordination between the Green Transition of Urban Land Use and urban land use efficiency can help explore the future development direction of sustainable land use in cities. This paper constructs a theoretical framework for the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency. We use several models, including the super-efficiency slack-based model, the coupling coordination degree model, the non-parametric kernel density estimation method, exploratory spatial data analysis, and the geographically and temporally weighted regression model to examine the real level of Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region from 2003 to 2020. Based on this, we investigate the spatiotemporal evolution characteristics and driving mechanisms of the two coupling coordination processes. The study found that (1) from 2003 to 2020, the overall trend of the coupling coordination between Green Transition of Urban Land Use and urban land use efficiency in the Yangtze River Delta region tended to be coordinated and developed, but still at a primary coordination level, with sufficient room for improvement in the future. (2) The coupling coordination level of each city in the Yangtze River Delta region from 2003 to 2020 showed obvious spatial non-equilibrium and correlation characteristics, and overall dynamic polarization effects were exhibited during the study period; the spatial pattern of high-value areas showed a regularity of prioritizing Shanghai and Zhejiang Province, gradually penetrating into Jiangsu Province and Anhui Province. (3) Economic and social factors have a positive influence on the degree of coupling coordination; natural factors and policy factors have a predominantly negative influence on the degree of coupling coordination. Research conclusions include establishing a regional collaborative development mechanism, utilizing the spatial spillover effect of leading cities; emphasizing science, education, and culture, strengthening the introduction of scientific and technological talents, increasing fiscal inputs, raising the level of economic development, and further expanding the driving effect of economic and social factors; and optimizing the layout of urban and rural construction land, developing urban land in an orderly manner, appropriately strengthening environmental regulation, thereby suppressing the negative effects caused by natural and policy factors.

[Relationship Between Urbanization and Carbon Emissions in the Chang-Zhu-Tan Region at the County Level].

Urbanization is a major source of carbon emissions. A quantitative study on the dynamic relationship between urbanization and its morphological characteristics and carbon emissions is crucial for formulating urban carbon emission reduction policies. Based on the carbon metabolism model, the carbon emissions at the country level in Chang-Zhu-Tan from 1995 to 2020 were calculated. The Tapio decoupling model was used to explore the decoupling relationship between the carbon emissions of Chang-Zhu-Tan and urban land, and a geographically and temporally weighted regression(GTWR) model was used to analyze the impact mechanism of urban spatial morphology on carbon emissions. The following conclusions were drawn:① carbon emissions at the county level in the study area formed a clustered distribution centered on the city jurisdiction and showed a trend of diffusion from year to year. Compared with those in 1995, there were seven new high carbon emission districts in 2020, all of which belonged to Changsha. ② From 1995-2020, the research area as a whole changed from mainly strong decoupling to mainly dilated negative decoupling, and the spatial decoupling state fluctuated back and forth between the decoupling and negative decoupling. By 2020, except for the seven regions with the uncoupling state regressing, all of them reached the uncoupling state or were close to the uncoupling state. ③ Urban patch area(CA), urban patch number(NP), and patch combination degree(COHESION) were positively correlated with urban carbon emissions, whereas landscape shape index(LSI), maximum patch index(LPI), and Euclidean distance mean(ENN_MN) were negatively correlated with urban carbon emissions, and the impact of different urban form indicators on carbon emissions had significant spatial heterogeneity.

The role of Chinese-style fiscal decentralization in promoting synergistic carbon and haze governance: insights from technological innovation.

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.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study.

BACKGROUND: Urbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China. METHODS: County-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography. RESULTS: The mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from - 0.205 to - 0.131. CONCLUSIONS: The influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Spatiotemporal heterogeneity of the relationships between PM2.5 concentrations and their drivers in China's coastal ports.

PM2.5 is one of the primary air pollutants that affect air quality and threat human health in the port areas. To prevent and control air pollution, it is essential to understand the spatiotemporal distributions of PM2.5 concentrations and their key drivers in ports. 19 coastal ports of China are selected to examine the spatiotemporal distributions of PM2.5 concentrations during 2013-2020. The annual average PM2.5 concentration decreases from 61.03 µg/m3 to 30.17 µg/m3, with an average decrease rate of 51.57%. Significant spatial autocorrelation exists among PM2.5 concentrations of ports. The result of the geographically and temporally weighted regression (GTWR) model shows significant spatiotemporal heterogeneity in the effects of meteorological and socioeconomic factors on PM2.5 concentrations. The effects of boundary layer height on PM2.5 concentrations are found to be negative in most ports, with a stronger effect found in the Pearl River Delta, Yangtze River Delta and some ports of the Bohai Rim Area. The total precipitation shows negative effects on PM2.5 concentrations, with the strongest effect found in ports of the Southeast Coast. The effects of surface pressure on PM2.5 concentrations are positive, with stronger effects found in Beibu Gulf Port and Zhanjiang Port. The effects of wind speed on PM2.5 concentrations generally increase from south to north. Cargo throughput shows strong and positive effects on PM2.5 concentrations in ports of Bohai Rim Area; the positive effects found in Beibu Gulf Port increased from 2013 to 2018 and decreased since 2019. The positive effects of GDP and nighttime light on PM2.5 concentrations gradually decrease and turn negative from south to north. Understandings obtained from this study can potentially support the prevention and control of air pollution in China's coastal ports.

A spatiotemporal analysis of the association between carbon productivity, socioeconomics, medical resources and cardiovascular diseases in southeast rural China.

Introduction: With China's rapid industrialization and urbanization, China has been increasing its carbon productivity annually. Understanding the association between carbon productivity, socioeconomics, and medical resources with cardiovascular diseases (CVDs) may help reduce CVDs burden. However, relevant studies are limited. Objectives: The study aimed to describe the temporal and spatial distribution pattern of CVDs hospitalization in southeast rural China and to explore its influencing factors. Methods: In this study, 1,925,129 hospitalization records of rural residents in southeast China with CVDs were analyzed from the New Rural Cooperative Medical Scheme (NRCMS). The spatial distribution patterns were explored using Global Moran's I and Local Indicators of Spatial Association (LISA). The relationships with influencing factors were detected using both a geographically and temporally weighted regression model (GTWR) and multiscale geographically weighted regression (MGWR). Results: In southeast China, rural inpatients with CVDs increased by 95.29% from 2010 to 2016. The main groups affected were elderly and women, with essential hypertension (26.06%), cerebral infarction (17.97%), and chronic ischemic heart disease (13.81%) being the leading CVD subtypes. The results of LISA shows that central and midwestern counties, including Meilie, Sanyuan, Mingxi, Jiangle, and Shaxian, showed a high-high cluster pattern of CVDs hospitalization rates. Negative associations were observed between CVDs hospitalization rates and carbon productivity, and positive associations with per capita GDP and hospital beds in most counties (p < 0.05). The association between CVDs hospitalization rates and carbon productivity and per capita GDP was stronger in central and midwestern counties, while the relationship with hospital bed resources was stronger in northern counties. Conclusion: Rural hospitalizations for CVDs have increased dramatically, with spatial heterogeneity observed in hospitalization rates. Negative associations were found with carbon productivity, and positive associations with socioeconomic status and medical resources. Based on our findings, we recommend low-carbon development, use of carbon productivity as an environmental health metric, and rational allocation of medical resources in rural China.

A Spatio-Temporal Analysis of OECD Member Countries' Health Care Systems: Effects of Data Missingness and Geographically and Temporally Weighted Regression on Inference.

Determinants of health care quality and efficiency are of importance to researchers, policy-makers, and public health officials as they allow for improved human capital and resource allocation as well as long-term fiscal planning. Statistical analyses used to understand determinants have neglected to explicitly discuss how missing data are handled, and consequently, previous research has been limited in inferential capability. We study OECD health care data and highlight the importance of transparency in the assumptions grounding the treatment of data missingness. Attention is drawn to the variation in ordinary least squares coefficient estimates and performance resulting from different imputation methods, and how this variation can undermine statistical inference. We also suggest that parametric regression models used previously are limited and potentially ill-suited for analysis of OECD data due to the inability to deal with both spatial and temporal autocorrelation. We propose the use of an alternative method in geographically and temporally weighted regression. A spatio-temporal analysis of health care system efficiency and quality of care across OECD member countries is performed using four proxy variables. Through a forward selection procedure, medical imaging equipment in a country is identified as a key determinant of quality of care and health outcomes, while government and compulsory health insurance expenditure per capita is identified as a key determinant of health care system efficiency.

Impact of R&D Investment and Network Penetration on Human Development: Evidence from China.

This paper measures the human development indices of 31 inland provinces (municipalities) in China in a continuous time series during 2000-2017 according to the 2010 HDI compilation method. It uses a geographically and temporally weighted regression model for conducting an empirical study on the effects of R&D investment and network penetration on human development in each province (municipality) of China. There is significant spatial and temporal heterogeneity in the impact of R&D investment and network penetration on human development across provinces (municipalities) in China due to differences in resource endowments and economic and social development. For R&D investment, eastern provinces (municipalities) have mostly positive effects on human development, and central regions have mostly weak positive or negative effects. In contrast, western provinces (municipalities) show different development paths, with weak positive effects in the early stage and significant positive effects after 2010. Most provinces (municipalities) show a continuous and increasing positive effect for network penetration. The marginal contributions of this paper are mainly in improving the shortcomings in research perspectives, empirical methods, and research data in the study of human development influencing factors in China relative to the study of HDI itself in terms of measurement or application dimensions. This paper constructs a human development index for China, analyzes its spatial and temporal distribution, and explores the impact of R&D investment and network penetration on human development in China, with the hope of providing lessons for China and developing countries to promote the level of human development and cope with the pandemic.

Spatio-temporal effects of regional resilience construction on carbon emissions: Evidence from 30 Chinese provinces.

In response to the threat of rapidly rising carbon emissions, a variety of measures are being implemented to achieve carbon reduction. Resilience construction offers a fresh approach to improving the regional anti-interference ability to cope with various risks, and it is worth considering its impact on carbon emissions. The objective of this study is to investigate the spatio-temporal impacts of resilience construction (RCI) on carbon intensity (CI) in 30 Chinese provinces from 2010 to 2019. The relation pattern between RCI and CI is thoroughly examined after developing a hybrid model by integrating gray correlation analysis (GRA) and coupled coordination degree (CCD). Using the GTWR model, the coefficients reveal the spatio-temporal pattern of the influence of each variable on CI. Furthermore, this study pioneeringly blends GTWR regression results with the K-Means approach to identify areas with homogeneity and heterogeneity of the pattern. Firstly, the findings indicate that there is a significant link between CI and all dimensions -economic resilience (RE), social resilience (RS), and ecological resilience (REn). The relation between REn and CI is the greatest, although it has been declining recently while relations of RS, REn, and CI have all been steadily rising. Secondly, according to the results of CCD, resilience construction and carbon reduction are progressively reaching orderly development but there are still some provinces at low levels of CCD. Thirdly, the study area is divided into four clusters, and the structure of spatial grouping tends to become stable. Moreover, we analyze each cluster's features and suggest appropriate policy measures. The findings aid in the scientific planning of the direction of resilience construction with the goal of collaborative management of carbon emissions.

Characteristics, regional differences, and influencing factors of China's water-energy-food (W-E-F) pressure: evidence from Dagum Gini coefficient decomposition and PGTWR model.

Water, energy, and food security are global concerning issues especially in China. To promote regional environmental management cooperation as well as find resource security influencing factor differences among regions, this paper calculates the water-energy-food (W-E-F) pressure, find W-E-F pressure's regional differences, and the influencing factors by Dagum Gini coefficient decomposition and geographically and temporally weighted regression model for panel data (PGTWR). First, the temporal trend of W-E-F pressure is decreasing and then increasing during 2003-2019; pressure in the eastern provinces is significantly higher than in other provinces and structurally energy pressure is the dominant resource pressure in W-E-F in most provinces. Besides, inter-regional differences are the main source of regional differences in China's W-E-F pressure, particularly for the inter-regional differences between eastern regions and other regions. In addition, there are obvious spatio-temporal heterogeneity effects of population density, per capita GDP, urbanization, energy intensity, effective irrigated area, and forest cover on W-E-F pressure. Balancing regional development gaps and developing differentiated resource pressure mitigation strategies based on the characteristics of different regional drivers are of great importance.