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• The median increase in EU residents with basic or above basic digital skills rose by approximately 5%, from 53.53% in 2015 to 58.03% in 2021. • Throughout 2015–2021, a consistent spatial distribution of digital skills in the EU has been observed, with concentrations in 'high–high' and 'high–low' clusters in the northern and central EU countries and a 'low–low' cluster in the southern EU countries. • To assess the spatial and temporal evolution of digital skills, criteria such as education, infrastructure, demography, culture and others, selected through tree-based and random forest features, are employed. • The spatial model proves more suitable for indicators related to the economy, technology and innovations, and ICT infrastructure. • The need for diversity in digital skills policy is argued by the proven influence across all considered domains and the existence of spatial associations. Digital skills, integral to the functioning of the digital economy and information society, show temporal and spatial variations measured by various indicators. In this article, we assess the spatial and temporal evolution of digital skills under the influence of key factors and domains in the EU countries from 2015 to 2021. Applying spatial autocorrelation analysis, robust geographical heterogeneity and consistent spatial patterns in digital skills are outlined, resulting in two 'high–high' and 'high–low' clusters in the North and Center, and a 'low–low' cluster in the South. Using feature importance selection, key indicators within aggregate domains driving digital skills policy are identified. Spatial lag regression analysis highlights the significance of all domains, revealing spatial and spillover effects on digital skills, with the primary influence observed in the social sphere, technology and innovations, and demography domains. Although the ICT infrastructure domain is statistically more significant in our spatial model along with the economy and technology and innovations, its spillover effects appear relatively modest, indicating a corresponding degree of within-country localization. This study contributes to the understanding of the evolution of digital skills by revealing both spatial relationships and temporal dynamics and strengthening spatial digital policy measures in the EU. The spatial coherence of digital policies, the spatial network of technological and innovation centers in both 'high–low' clusters and cross-border locations, and improving the social, demographic, and economic profiles of citizens are critical among other measures to improve digital skills in EU countries. [ABSTRACT FROM AUTHOR]

Digital finance (DF) and green technology innovation (GTI) are vital components of China's high-quality economic development. Therefore, exploring the coupling coordination relationship between these two systems holds significant importance for China's economic development. Utilizing panel data spanning from 2011 to 2020 across 30 Chinese provinces, this study utilizes the coupling coordination model to assess the coupling coordination degree (CCD) between these two systems, and employs spatial autocorrelation analysis and the panel vector autoregression (PVAR) model for a more in-depth quantitative analysis of their coupling coordination relationship. The findings indicate that: (1) The comprehensive level of digital finance and green technology innovation in our country shows a year-on-year increasing trend, and the level of digital finance is higher than that of green technology innovation. (2) The coupling coordination degree between these two systems is generally on the rise but hasn't reached a high level. Furthermore, notable developmental variations exist among different regions. (3) There is a positive spatial correlation in the coupling coordination degree between these two systems. (4) While these two systems can mutually promote each other, the positive impact of digital finance on green technology innovation is greater than the positive impact of green technology innovation on digital finance. Therefore, this study will provide valuable references for the balanced development of these two systems, as well as the formulation of regional development strategies. [ABSTRACT FROM AUTHOR]

• Evaluating the Water Resources Constraint Intensity(WRCI) from the Perspective of High-quality Development. • An Analytic Hierarchy Process (AHP) model reformed by entropy technology and a multi objective fuzzy membership function were employed for indicator aggregation. • The spatiotemporal changes and correlation of UQDI, WRI, and WRCI in 78 prefecture level cities in the Yellow River Basin during 2011–2020 were analyzed. Promoting the high-quality development of urbanization is a crucial way to achieve sustainable development goals. Water resources are a rigid constraint for such development. However, There is still room for improvement in the research on the spatio-temporal pattern and key driving factors of high-quality urbanization development under the constraint of water resources. Aiming at the problem that the high-quality development of urbanization in the Yellow River Basin (YRB) is facing water resources constraints, this paper discussed the spatial–temporal pattern and key drivers of the high-quality development of urbanization in the YRB under water resource constraints by using the Analytic Hierarchy Process model reformed by entropy technology, multi-objective fuzzy membership function, spatial autocorrelation analysis and obstacle model. The results revealed that, first, we found the High-Quality Urbanization Development Index (UQDI) exhibited steady growth, while both the Water Resources Index (WRI) and the Water Resources Constraint Intensity (WRCI) initially increased and then decreased during the study period. Second, the UQDI displayed a dual-core pattern, with the Shandong Peninsula urban agglomeration and the Central Plains urban agglomeration serving as the cores. WRI exhibited relatively stable spatial distribution, with the southeast region outperforming the northwest. WRCI also demonstrated a relatively stable spatial distribution, resulting in two weak-constraint areas, represented by Shandong and Henan, and a strong-constraint area centered around Ningxia. Third, the UQDI, WRI, and WRCI exhibited spatial interdependence. The spatial interdependence of UQDI and WRI increased, while that of WRCI decreased during the study period. The three indexes also displayed a clear pattern of local clustering in space. Two low-value aggregation areas, namely Shandong and Henan, and a high-value aggregation area located in Ningxia, emerged during the study period. Last, the WRCI was primarily influenced by the fundamental conditions of water resources. The main influencing factors included per capita water resources, mean annual precipitation, total water resources, water resources modulus, and the total amount of foreign investment utilization. In light of the research findings, the paper proposes policy recommendations for promoting high-quality urbanization development in the YRB. This study provides decision-making guidance for countries and regions in arid areas to solve the water resources constraints on the high-quality development of urbanization. [ABSTRACT FROM AUTHOR]

The rural human settlement environment, closely related to the fundamental well-being of most farmers, is a crucial determinant of farmers' quality of life and is essential to the strategy for rural revitalization of China. This study first constructs an evaluation index system to assess the human settlement quality of 31 provinces and cities in China from 2018 to 2021 and to analyze the internal logical relationship of its dimensions based on the causal DPSIR (Driver-Pressure-State-Impact-Response) theoretical framework and PLS-SEM (Partial Least Squares Structural Equation Model). Secondly, using the evaluation results as a basis, this study examines the evolution characteristics in the spatial and temporal distribution pattern of rural human settlements in China. Finally, the study identifies the constraints by analyzing the variances among the five subsystems of the rural human settlements. It was found that: (1) The ecological "pressure" and environmental "impact" caused by socio-economic "driver" in the countryside, and "response" measures, have combined to result in a change in the "state" of rural human settlements. (2) Temporally, China's human settlements have improved markedly in all 31 provinces (cities). Spatially, the extent of rural human settlements is characterized by "strong in the east and weak in the west", with obvious spatial heterogeneity. (3) The distribution of rural human settlement quality in China's 31 provinces (cities) exhibits a strong positive spatial correlation, clearly exhibiting high-high (HH) type and low-low (LL) type agglomerations. (4) There are differences in the evaluation value and the magnitude of change of each subsystem, showing that the value of "pressure" dimension is the highest and the magnitude of change is the smallest. Based on the findings of this study, corresponding countermeasures are proposed to provide the current government with a foundation for making decisions to improve the rural human living environment. [Display omitted] • Evaluate rural human settlements combining model of DPSIR and PLS-SEM. • Test the causal relationships in the DPSIR theoretical framework. • The human settlement quality has increased significantly in China. • Obvious spatial heterogeneity with "strong in the east and weak in the west". • Spatial correlation with high-high type and low-low type agglomerations. [ABSTRACT FROM AUTHOR]

Paralytic shellfish toxins (PSTs) are produced by marine dinoflagellates and can be accumulated in filter feeding shellfish. In this study, we assessed acute dietary exposure to PSTs among residents of Chinese coastal provinces and cities, utilizing point and probabilistic estimates. Additionally, we performed global and local spatial autocorrelation analyses to evaluate PSTs contamination and dietary exposure levels in the coastal regions of China. The point estimate revealed that Hebei province exhibited the highest level of PSTs dietary exposure, measuring 39.57 μ g S T X e q. / k g b w. In all regions, except for Guangxi, Jiangsu, Shandong, and Shanghai, the ratio between acute dietary exposure and the acute reference dose (ARfD) exceeded 1. Probabilistic estimates indicated that P 99.9 exceeded the ARfD value exclusively in Guangdong, Hainan, and Liaoning. In comparison, the point estimate was significantly higher than the P 99.9 value of the probabilistic estimate. The spatial analysis results showed "low-high" aggregation of PSTs concentration and point estimate dietary exposure levels in Liaoning and Shandong, while probabilistic estimate dietary exposure level exhibited "low-high" aggregation in Guangxi. This implied the presence of regional variations in PSTs contamination and dietary exposure within the coastal regions of China, along with specific localized clustering features. Therefore, it is advisable to develop regionalized precision monitoring and dietary interventions to ensure the safety of shellfish consumption, particularly in response to the regional distribution characteristics of PSTs contamination and dietary exposure. • Comparing the results of point and probabilistic estimates. • Dietary exposure assessment was combined with spatial analysis to visualize dietary exposure results. • The probabilistic estimate provided a more realistic picture of dietary exposure. • There are regional variations in PSTs contamination and dietary exposure with specific local aggregation characteristics. [ABSTRACT FROM AUTHOR]

With the increasing severity of global warming, the goal of carbon sequestration and emission reduction in various industries has become an important responsibility for sustainable development. As an important part of carbon sink, the improvement of net carbon efficiency in the plantations industry will make a great contribution to the realization of the "double carbon" target in agriculture. However, there are large spatial and temporal differences in the net carbon efficiency of the plantations sector in various provinces, and there is still a large scope for increasing carbon sink and reducing carbon emission. In this paper, we have analyzed spatial and temporal trends in the net carbon sink efficiency of the plantations' production processes in 31 provinces in China (excluding Hong Kong, Macao and Taiwan) from 2000 to 2020. On this basis, we have analyzed the influencing factors of net carbon efficiency in the plantations' production process through an extended Kaya constant equation. Through the geographically and temporally weighted regression model, we explore the spatio-temporal heterogeneity of the influencing factors, and further analyze the driving and restraining factors of net carbon efficiency in plantations' production process, in order to propose appropriate countermeasures. The study shows that: (1) The net carbon efficiency of plantations sector in China increase between 2000 and 2020, and the development of net carbon efficiency is uneven across regions. (2) The geographic center of net carbon efficiency of China's plantations industry shift from the northeast to the southwest and is consistent with the migration direction of agricultural inputs. (3) The influencing factors of net carbon efficiency in the plantations sector show large regional differences, and can be specifically divided into four major types of zones. In this regard, this paper provides more appropriate countermeasure suggestions for increasing carbon sink and reducing carbon emission in each zone, with a view to achieving the goal of double carbon in agriculture with practical significance. • Net carbon efficiency is a new objective indicator to measure the carbon sink level of cropland. • Cultivated land carbon sink capacity is closely related to agricultural inputs. • Regional differences in the factors influencing net carbon efficiency. [ABSTRACT FROM AUTHOR]

• An evaluation index system of urban green growth efficiency in China was established. • The methods and knowledge of economics, management, and geography were integrated to test and analyze the green growth efficiency of Chinese cities. • The input-output index, comprehensive evaluation index, and spatio-temporal pattern of green growth efficiency were analyzed. • A practical reference for the coordinated development of regional economy and the environment was provided. Based on the panel data of 285 Chinese cities at prefecture level from 2005 to 2016, remote sensing data and socio-economic data were used to establish a green growth efficiency analysis database. Additionally, the TOPSIS model, super-efficient SBM model, and exploratory spatial analysis method supported by the entropy technology were applied to test and analyze the input-output index, comprehensive evaluation index, spatio-temporal pattern of green growth efficiency, spatial correlation effect, and the shift of the barycenter of green growth of Chinese cities. The following results were obtained: (1) There are obvious spatial differences between Chinese cities in terms of resources investment, socio-economic benefits, and environmental impact index of green growth; the efficiency of urban resource conversion is low, and there is much scope for improvement. (2) In 2005, 2010, and 2016, the comprehensive evaluation index of green growth of Chinese cities increased first and then decreased, and the regional gap was small but was gradually expanding. The green growth efficiency decreased first and then increased with significant spatial differences. (3) In 2005, 2010, and 2016, the Moran's I of China's green growth efficiency was 0.1888, 0.0745, and 0.0584, respectively. There was a positive correlation but a weakening trend spatially, featuring "small agglomeration and large dispersion." (4) From 2005 to 2016, the center of gravity of China's green growth efficiency moved toward the southwest-the moving track was from Suiping County to Biyang County, and to Suizhou City. The research results could improve China's green growth efficiency and provide scientific support and a practical reference for narrowing the imbalance of green growth between the regions. [ABSTRACT FROM AUTHOR]

The fragile ecosystems of arid areas are highly vulnerable, especially under the impact of human activities. Therefore, there is an urgent need to develop appropriate ecological vulnerability assessment models for these areas. In this study, Zhongwei City was taken as the study area. And a multi-indicator comprehensive assessment system was constructed using the optimized featured indicators-vulnerability scoping diagram (FVSD) model. The weights for each indicator were determined using the analytic hierarchy process-entropy weight (AHP-EW) method, leading to the creation of the ecological vulnerability map of Zhongwei City. The results showed that the overall ecological vulnerability of Zhongwei City is high and there is a significant spatial aggregation. There was obvious spatial heterogeneity in the distribution of vulnerability, with high vulnerability mainly concentrated in Haiyuan County, showing a decreasing trend from south to north. Watershed area index, annual average temperature, PM10 and annual afforestation index were the most important indicators affecting the ecological vulnerability, the fragile environmental conditions and human activities combine to shape the spatial heterogeneity of ecological vulnerability. The results of the study have important theoretical and practical implications in guiding ecological management of Zhongwei City. Moreover, the research framework can be replicated in different areas and is instructive for ecological vulnerability studies. [ABSTRACT FROM AUTHOR]

The continuous change of global land use and intense human activities lead to the constant change of landscape ecological risk (LER). The increase of LER will pose a serious threat to human welfare and sustainable development. In previous studies, there were few studies on coupling coordination between land use intensity (LUI) and LER in multi-scenario simulation. Therefore, it is of a certain significance to study the coupling and coordination relationship between LUI and LER in a long-term multi-scenario simulation in the future for the sustainable use of land resources and the management of ecological risk. Based on the multi-scenario land use simulation data of China in 2010, 2050, and 2100, this study selects the comprehensive index of land use intensity, landscape pattern index, spatial autocorrelation, and coupling coordination methods to analyze the spatio-temporal characteristics of LUI and LER from 2010 to 2100, as well as the coupling coordination relationship between them. The results show that the overall LUI of China remains stable at around 200 from 2010 to 2100, with a spatial distribution that is "low in the northwest and high in the southeast." The LUI of China decreases under the balanced resources scenario and global environmental protection scenario, while the regional economic development scenario and regional environmental protection scenario exhibit an increasing trend. The overall LER in China is approximately 0.150, with a spatial distribution that is "high in the north and low in the south." The four scenarios exhibit a downward trend from 2010 to 2100. There is significant spatial dependence between LUI and LER in China, with the largest number of counties exhibiting reluctant coordination between the two (accounting for nearly 45%), while their interaction and mutual influence remain strong. Thus, this study may contribute to understanding this relationship and to creating effective landscape ecological risk management policies. [Display omitted] • There was significant spatial dependence between LUI and LER in China. • The coupling coordination degree between the LUI and LER were explored under different scenarios. • Reluctant coordinated counties occupied the dominant position. [ABSTRACT FROM AUTHOR]

• Net carbon sink efficiency is used to measure carbon neutral performance. • A spatial spillover effect existed between urbanization and net carbon sink efficiency across counties. • Being surrounded by highly urbanized units resulted in positive effects on a unit's net carbon sink efficiency. • The impact of socioeconomic factors on net carbon sink efficiency is heterogeneous. Reducing carbon emissions and improving vegetation carbon sequestration (VCS) have long been recognized as the key ways to address the aim of carbon neutrality, requiring global effort and typical regional experiences. This paper develops an assessment framework for measuring the net carbon sink efficiency (NCSE) based on the concept of net-zero carbon sink to depict both carbon emission and VCS simultaneously. This paper primarily runs a spatial autocorrelation analysis to explore spatial characteristics of urbanization and NCSE of Yangtze River Delta urban agglomeration (YRD) in China from 2000 to 2020, followed by employs a spatial econometric model to investigate the spatial spillover effect of urbanization on NCSE. The results show that NCSE in YRD exhibits clear spatial disparities, descending from the southern part dominated by mountainous hills to the flatter northern region. The spatial evolutionary trend of urbanization and NCSE has a significant positive correlation, manifested as H-H and L-L agglomeration, covering most counties in southeast coastal areas and northern plains, respectively. The NCSE can improve even during the continued urbanization in YRD. Specifically, for every 1% increase in urbanization, NCSE will increase by 0.164%, of which 0.118% in direct and 0.046% in indirect growth, respectively. In addition, GDP per capita, industrial structure, technological progress, road area and direct foreign investment are benefit for NCSE. In contrast, population density, environmental regulation, and built-up area tend to decline NCSE. These findings may help to formulate the urban sustainable development planning and differentiated carbon neutrality policies. [ABSTRACT FROM AUTHOR]

The aim of this study was to analyze the spatial distribution of heavy metal monitoring data in paddy rice to provide a scientific basis for food safety risk assessment and suggestions for possible risk management. In this study, the spatial distribution data of heavy metals (cadmium, lead, total arsenic, total chromium, and total mercury) in rice collected in the main production provinces of China were analyzed by multidimensional visualization and aggregation analysis. The spatial correlation of elemental contamination was also identified. Monitoring data of cadmium, lead, and arsenic content in different varieties of rice was compared with their mean confidence intervals. Results showed that cadmium content in rice was higher than the limit value in some areas of Hunan, Sichuan, Guangxi and Anhui Provinces in China. With respect to other heavy metals, a small area of Sichuan Province experienced lead levels in rice higher than the limit value. Also, the arsenic level in rice was higher than the limit value in Jiangxi Province, a northern area of Liaoning Province and most parts of Guangzhou and its surrounding areas. In contrast, chromium was only detected at excessive levels in southern Sichuan Province. In addition, a small part of the eastern Sichuan Province was found to have excessive levels of arsenic. Moran's I index of cadmium, arsenic, chromium, lead, and mercury in rice was 0.50, 0.55, 0.21, 0.09, and 0.05, respectively, which revealed a spatial autocorrelation. Overall, there was moderate aggregation of cadmium and arsenic in the monitoring areas, while lead, chromium and mercury showed low aggregation. Geographically for the provinces, the high aggregation of cadmium in rice was evident in Hunan and Jiangxi Provinces and Guangdong border areas. The arsenic in Jiangxi Province and border areas of Jiangxi and Guangdong Provinces also showed high level of aggregation. Meanwhile, the parameter testing of the samples showed that the concentration of cadmium and lead were significantly higher in late Indica rice compared to early Indica rice, while the arsenic and chromium showed the opposite effect. In view of the high levels of certain heavy metals in rice in some provinces, more refined dietary intake assessments of rice as consumed are necessary to determine if populations are exposed to levels that exceed the health-based guidance levels. [ABSTRACT FROM AUTHOR]

Atmospheric haze pollution has become a global concern because of its severe effects on human health and the environment. The Beijing-Tianjin-Hebei urban agglomeration is located in northern China, and its haze is the most serious in China. The high concentration of PM2.5 is the main cause of haze pollution, and thus investigating the temporal and spatial characteristics of PM2.5 is important for understanding the mechanisms underlying PM2.5 pollution and for preventing haze. In this study, the PM2.5 concentration status in 13 cities from the Beijing-Tianjin-Hebei region was statistically analyzed from January 2016 to November 2016, and the spatial variation of PM2.5 was explored via spatial autocorrelation analysis. The research yielded three overall results. (1) The distribution of PM2.5 concentrations in this area varied greatly during the study period. The concentrations increased from late autumn to early winter, and the spatial range expanded from southeast to northwest. In contrast, the PM2.5 concentration decreased rapidly from late winter to early spring, and the spatial range narrowed from northwest to southeast. (2) The spatial dependence degree, by season from high to low, was in the order winter, autumn, spring, summer. Winter (from December to February of the subsequent year) and summer (from June to August) were, respectively, the highest and lowest seasons with regard to the spatial homogeneity of PM2.5 concentrations. (3) The PM2.5 concentration in the Beijing-Tianjin-Hebei region has significant spatial spillovers. Overall, cities far from Bohai Bay, such as Shijiazhuang and Hengshui, demonstrated a high-high concentration of PM2.5 pollution, while coastal cities, such as Chengde and Qinhuangdao, showed a low-low concentration. [ABSTRACT FROM AUTHOR]

General landscape metrics are mainly conducted in two dimensional space, while the terrain undulation is usually not considered. Terrain fluctuation affects a landscape’s spatial heterogeneity and its spatial-temporal dynamics by diversifying the spatial distribution of soil, vegetation and animals. This article proposed three methods to measure the landscape pattern in three dimensional space, including the 3D derivation of 2D landscape metrics, 3D surface roughness parameters, 3D power spectral analysis and 3D correlation analysis. 3D surface data were designed and validated using the proposed 3D landscape pattern analyzing metrics, and the results indicated that these measurements can effectively evaluate the 3D composition, spatial configuration and texture direction of 3D landscape. [ABSTRACT FROM AUTHOR]

The power industry produces nearly 40% of China’s carbon emission, thus, this sector should be regarded as priority for carbon emission reduction. Identifying the unevenness of regional development may be crucial for increasing the carbon emission efficiency of power plants. This work evaluates the carbon emission efficiency using the Undesirable-SBM (slacks-based measure) model and data from China’s power industry in 30 provinces from 2003 to 2014. Moreover, the global Malmquist index, which consists of efficiency changes (ECs) and technical changes (TCs), is used to determine the driving factors of these changes. Finally, a spatial autocorrelation analysis that is based on Moran’s index is performed to confirm the non-equilibrium spatial distribution of the carbon emission efficiency for the power industry. The main findings are as follows: (1) compared to economically underdeveloped provinces, the wealthy eastern coastal provinces exhibit higher carbon emission efficiency; (2) the positive effects of TCs on the efficiency changes are stronger, moreover, the provinces with lower efficiency are more likely to achieve greater improvements; and (3) significant spatial correlations exist among the power sectors of the 30 provinces in terms of carbon emission efficiency; the eastern regions have relatively high efficiency and tend to have a positive spillover effect on the neighboring provinces. Therefore, technological cooperation between various regions is beneficial to ameliorate carbon emission efficiency. Finally, policy implications are provided to address such spatial discrepancies. [ABSTRACT FROM AUTHOR]

China is the world's largest energy consumer and is also leading in the solar photovoltaic industry. The solar energy industry, with advantageous clean and highly efficient production, is integral in maintaining the sustainable energy supply in an environmentally sensitive manner. Further development of the Chinese photovoltaic industry requires a status assessment for the current industrial setting. An analysis on the 58 Chinese photovoltaic listed enterprises is conducted in this study to analyze the operating performance, industry agglomeration and spatial characteristics of Chinese photovoltaic industry. Comprehensive analysis and evaluation of the enterprises’ operating performance are based on financial data by utilizing the Data Envelopment Analysis (DEA) method; Systematic analysis examines spatial characteristics through the application of the spatial autocorrelation analysis method. Results confirm the Chinese photovoltaic industry preliminarily appeared industry agglomeration with enterprises mainly distributed in east and north China, and gathered in developed coastal provinces. Superior performing photovoltaic enterprises are specifically located in north China. Most of the enterprises are in the up-stream or mid-stream of the photovoltaic industrial chain. Overall operating performance of the photovoltaic industry in China suffers from weak profits due to low technical efficiency. The key to improve the future development of these enterprises is the improvement of operating performance by strengthening technical efficiencies. Concurrently, governmental guidance should address profitable photovoltaic industry investments and improvement of production rates. [ABSTRACT FROM AUTHOR]

In the modern metropolis, the coverage of community hospitals is getting larger and larger. But high-level hospitals treating serious and sudden illness are still kind of scarce social resources. Shenzhen is the forefront of China's reform and opening up, its solution to the problem of medical allocation can be used as a reference for other cities. In this paper, we assume that primary, secondary and tertiary hospitals are main places for treating major diseases in Shenzhen, and analyze each sub-districts' spatial accessibility to them based on travel time and spatial difference within the city. A kernel density two-step floating catchment area method (KD2SFCA) is used to calculate how many medical resources each sub-district could share, in which the travel time from residential district to hospitals is used as an important parameter in evaluating the accessibility between suppliers and demanders. According to statistics of travel time by both driving and public transportation, the impedance function in KD2SFCA is modified and actual data of Shenzhen is used to fit its parameters, which could better simulate the attenuation trend of hospital service capabilities over the travel time. The spatial accessibilities are calculated under different travel modes and multiple time thresholds, and then spatial autocorrelation analysis method is used to analyze the spatial correlation of residents' accessibility to these medical resources. From both analysis of spatial accessibility and spatial autocorrelation, distinct variations in spatial distribution of high-level hospitals could be observed: of which the south and central parts of Shenzhen have significantly higher accessibilities, while the eastern and western regions are relatively lower. In particular, 12 sub-districts gain quite lower scores than others, showing that constructions on high-level hospitals should be strengthened further. In conclusion, the spatial configuration of high level hospitals in Shenzhen is not well balanced, Further optimization is urgently needed. [ABSTRACT FROM AUTHOR]

• This study examines the relationships between social vulnerability and crashes. • MGWR can depict spatially varying relationships between crashes and SVIs. • CDC SVI can help reveal the social inequities of traffic safety in Texas. Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking. [ABSTRACT FROM AUTHOR]

Land use in coastal zones faces the challenge of overlapping and competing among agriculture, construction, and ecological functions. Identifying land use conflicts (LUCs) and exploring their driving factors are the premise to alleviate the problem of 'competing for land'. Firstly, this study constructed a multi-objective evaluation system from three perspectives: agricultural land, construction land, and ecological land suitability. Then, the types of LUC were divided based on the suitability of spatial combination. Finally, we analyzed the spatial distribution characteristics and the main driving factors of LUCs in the Bohai Rim region of China. In addition, we discussed the situation of LUCs along the sea-land direction. Results showed an obvious spatio-temporal mismatch in the spatial layout of agricultural land, construction land, and ecological land suitability in coastal zones. On a grid-scale, high, moderate, and low conflict zones accounted for 17.50%, 52.68%, and 4.64%, respectively. Moreover, there were significant differences in different forms of conflict. At the country scale, conflict intensity showed a regional distribution characteristic of "high in the South and low in the North", with significantly spatial positive correlation. In addition, both natural and socio-economic factors had significant effects on the spatial clustering distribution characteristics of LUCs. We recommend that land policymakers adopt different governance strategies according to conflict degree, spatial distribution, manifestation, and influencing factors. [Display omitted] • Established a research framework of potential LUCs based on land use suitability. • Ecological and construction suitability land are complementary in space. • Most serious conflicts are agriculture and construction, agriculture and ecology. • Natural factors are the basis of LUCs, while socio-economic factors are engine. • Each LUC type shows different trends along the sea-land direction. [ABSTRACT FROM AUTHOR]

Abstract: In 2005, the World Bank issued Natural Disaster Hotspots: A Global Risk Analysis, which indicted that Taiwan, may be the most vulnerable place to natural hazards. This paper aims at landslide points, land use data, socioeconomic indicators integrated with the mapping method to analyze the relation between spatial structural relationships and spatial patterns of disaggregate and aggregate levels in natural hazard areas. First, natural hazard locations are derived from geospatial data and coded into a GIS system. Second, the spatial analysis techniques includes different geospatial scale is derived from spatial autocorrelation analysis (SAA), space syntax. On the other hand, spatial analysis data such as digital terrain model (DTM) combination based on land use type relative significance to the process of landslide occurrence. Finally, this paper organizes a framework integrated with the land use plan and spatial analysis techniques (FILS). Results from 2006 showed that the natural hazard area hot spots include Taipei, Taichung, and Kaohsiung. [Copyright &y& Elsevier]

Abstract: Flood disasters and snow disasters are frequent disasters in China, causing considerable economic loss and serious damage to towns and farms. The problems of how these two disaster types distribute and what disaster-forming environments are important to their occurrence are the most pressing problems in disaster risk assessment and salvage material arrangement. The present study aims to establish the regularity of flood/snow disaster outbreaks and the important disaster-forming environmental factors, and a spatial autocorrelation analysis method and a canonical correlation analysis method are used to answer these two questions separately. Experimental results indicate that serious flood disasters distribute mainly on the south area of China and snow disasters occur on the north area; those areas should be allocated correspondingly more salvage materials. And some disaster-forming environmental factors are important for the occurrence of flood/snow disasters, and can be used in disaster risk assessment. [Copyright &y& Elsevier]

Abstract: To study the genetic diversity and population structure of Lilium tsingtauense Gilg (Qingdao Lily), we collected 648 samples from 12 sites in China and Korea, and analyzed their Inter-Simple Sequence Repeat (ISSR) molecular markers and morphological characters. ISSR data revealed a relatively high genetic diversity at the species level, with 72.31% polymorphic loci, effective numbers of alleles of 1.437, average expected heterozygosity of 0.231 and Shannon’s information index of 0.369. Considerable genetic differentiation among the natural populations (GST  = 0.144) and the gene flow (Nm  = 1.487) were detected. AMOVA analysis and UPGMA-dendrogram suggested a hierarchical regional structure among populations, and spatial autocorrelation analysis showed a micro-scaled spatial structure. Furthermore, there was a high correlation between morphological characters and genetic parameters obtained from ISSR parameters. There was only a low genetic differentiation among the different morphological types of L. tsingtauence in China. Based on these findings, we recommend in situ and ex situ conservation strategies for the preservation of L. tsingtauense. [Copyright &y& Elsevier]

Water scarcity and food security are major challenges facing the world in the 21st century. The water footprint provides a new and comprehensive approach for understanding the relationship between crop production and water utilization, and it has been used to estimate the water consumption of most crops at global and regional scales. However, the water footprint of soybean production (WFP) in China has not been extensively studied. Here, this study investigated firstly the components, spatial-temporal variation, as well as the driving factors of the county-scale WFP during the period of 1985–2015 in China. Annual WFP first increased and then decreased from 26.86 G m3 (109 m3) in 1985–24.62 G m3 in 2015. The WFP exhibited a clustered spatial distribution across the entire study area, and high-value areas for WFP were mainly distributed in the Northeast, North-central, and Middle and Lower Reaches of the Yangtze River Regions. The spatial distribution of WFP at the county level tends to be dispersed in studied period. The proportion of green water footprint (WFP green) reached as high as 70% of the WFP, whereas it decreased in the northern areas accompanied by the increased WFP blue proportion. Soybean yield was the major factor affecting WFP, and the increase in soybean yield contributed to the 32.80% decrease in the water footprint per ton soybean over the entire study period, further saved the 50.75 G m3 water resource for national soybean production in 2015. The results of this study provide new insights that could be used to improve agricultural water management, optimize regional water resource allocation, and alleviate water shortages in China. • The 30-year county-level soybean water footprint for China was quantified for the first time. • The soybean water footprint showed a clustered spatial distribution across China. • In northern areas, the green water footprint proportion decreased over studied period. • The water footprint of soybean production was mainly influenced by yield. • Increased yield contributed to reduce water footprint per unit of soybean by 33%. [ABSTRACT FROM AUTHOR]

• The annual averages of the AQI in the Yangtze River Delta (YRD) region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. • Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. • The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by rainfall, temperature, wind speed, air pressure, and normalized difference vegetation index (NDVI); of which relative humidity has a positive correlation with AQI while air pressure has negative correction with AQI in the whole study area. Air pollution is a great concern due to its adverse health effects, and reducing air pollution is a major challenge worldwide. Utilizing the air quality monitoring data of the Yangtze River Delta (YRD) from 2016 to 2018, this paper first investigates the spatiotemporal patterns of the Air Quality Index (AQI), identifies the primary pollutants for each metropolitan area using gray relational analysis (GRA) method, and analyzes the spatial heterogeneity of the effects of six influencing factors on AQI in different metropolitan areas using geographically weighted regression (GWR). The study results show that: (1) The annual averages of the AQI in the YRD region from 2016 to 2018 are 78.6, 74.2, and 69.7, respectively, exhibiting a pattern of year-to-year decrease. (2) Among the 41 metropolitan areas of the YRD, the correlation coefficients of PM 2.5 across metropolitan areas are between 0.75 and 0.95. PM 2.5 is the primary pollutant in 36 metropolitan areas, accounting for 88% of the pollution in the studied areas. (3) The study results demonstrate that the six impact factors have positive or negative correlations with AQI with significant spatial differences. The mean values of the regression coefficients indicate that relative humidity has the greatest effect on AQI values, followed by precipitation, temperature, wind speed, air pressure, and Normalized Difference Vegetation Index (NDVI); of which relative humidity has a negative correlation with AQI while air pressure has positive correction with AQI in the whole study area. Since there is spatial heterogeneity on the degree of influence of each influencing factor on AQI, it is necessary to consider the spatial variability on the degree of influence different from indicators when formulating control countermeasures of air quality, so that the management countermeasures can be more targeted. [ABSTRACT FROM AUTHOR]

• An accessibility measurement model is developed for electric vehicle public charging facilities. • A procedure of spatial equity evaluation is further devised. • The methodology is performed for ten representative Chinese cities to demonstrate its applicability and examine their spatial equity in charging infrastructure. • Over half of the cities exhibit spatial inequity in access to charging infrastructure. • Targeted polities are required to reduce regional disparities in recharging service provision. The widespread adoption of electric vehicles (EVs) is crucial for urban sustainability, which can promote the low carbon transition of urban mobility systems and increase energy independence. However, inadequate access to charging infrastructure has posed significant challenges on penetration to the mass market, which requires an equitable distribution of public charging facilities among residents. Current planning and deployment of public charging facilities only take efficiency into account, with limited attention to the spatial equity in distribution. Hence, this study proposes a spatial statistical approach to evaluate whether electric vehicle charging services (EVCS) are equitably distributed. An innovative accessibility measurement model is first developed for EVCS, considering competition over changing demand and different charging capacities. On this basis, a spatial autocorrelation analysis procedure is further designed to investigate spatial patterns of residential accessibility and detect regional disparities. The proposed methodology is tested with the top ten cities in China by their capacity of EVCS, where most cities exhibit significant inequitable distribution but with different regional characteristics. It is demonstrated that the methodology can improve the accuracy of measurement while remaining easy to implement. Therefore, it can serve as a policy tool for EVCS planning to maximize its capability to facilitate EV uptake. [ABSTRACT FROM AUTHOR]

As artificial ecosystems with high population concentrations, the security of urban ecosystems is more fragile than that of natural ecosystems. This paper aims to construct the methodology of an urban ecological security evaluation system. Firstly, based on the "driver–pressure–state–impact–response" (DPSIR) model, an evaluation index system of urban ecological security is established. Then gray clustering method is introduced to build an evaluation model of urban ecological security. A spatial autocorrelation analysis is also adopted to explore the spatial mechanism that affects the changes of urban ecological differences. Results indicate that the average level of ecological security in these 16 cities from 2007 to 2016 was between 0.535 and 0.647, and the southwest region is better than the northeast region. The results of the spatial analysis suggest that the surrounding areas easily affected the city's ecological security, showing a clustering effect. This study provides a theoretical reference for decision making of urban sustainable development. • Explore the methodology of urban ecological security evaluation and construct an urban ecological security evaluation system. • Based on DPSIR conceptual model, the evaluation index system of urban ecological security is constructed. • Gray clustering method is introduced to build an evaluation model of urban ecological security. • Studies the spatial distribution characteristics and dynamic evolution rules of ecological states between cities. • The city's ecological security is easily affected by the surrounding areas, showing a clustering effect. [ABSTRACT FROM AUTHOR]

• Spatial correlation analysis of near collision clusters with traffic characteristics. • Spatial autocorrelation methods identify near ship collision hotspots. • Co-occurrence analysis of near collision hotspots with maritime traffic features. • Automatic Identification System data off the coast of Portugal is used. A spatial correlation analysis of near collision clusters with local traffic characteristics is presented. The Moran's I and Getis-Ord Gi* spatial autocorrelation methods are used to determine whether near collisions show spatial clustering from global and local perspectives. The application of the developed approach to Automatic Identification System data of the maritime traffic off the coast of Portugal shows that there are several hotspots where the density of ship near collisions is relatively high. A co-occurrence analysis is then conducted to relate the near collision hotspots with selected local maritime traffic characteristics such as the average ship speed, speed dispersion, degree of speed acceleration, ship route overlaps and degree of angular deviation from ship route centreline. The identification of near collisions clusters and the assessment of the correlation of ship near collision hotspots with the maritime traffic features provide a means for improving maritime safety and reducing the occurrence of ship-ship collisions. [ABSTRACT FROM AUTHOR]

• A multilevel statistical model could better assess the ecological environment. • Conducting a spatio-temporal analysis of NPP from multiple time seires. • Exploring the potential drivers of ecological changes from different scales. • Providing more effective suggestions in view of the intensity of different factors. It is significant to clarify the driving mechanism of the forest ecosystem changes at different scales in Northeast China with serious forest degradation. With Changbai Mountains in Northeast China as the study area, this study integrated multi–source data to explore the spatio–temporal changes of Net Primary Productivity (NPP) and its spatial agglomeration patterns, and probed its multi–level driving mechanism based on the Hierarchical Linear Model (HLM). The results showed the overall NPP in the study area had a gradual declining trend from southeast to northwest from 2001 to 2015. Besides, the ecological risk regions, including Low-Low (L–L) and High–Low (H–L) cluster types, expanded from 27.56% during 2001–2008 to 28.21% during 2008–2015, suggesting the local departments should focus on optimizing these regions and strengthen the construction of complex forests with large age differences to make the ecological environment healthier. In addition, results from the HLM suggested that key driving factors, e.g., the precipitation and vegetation coverage rate, had significant promoting effects on NPP at the grid scale. Whereas the soil organic matter content, distance to the highway, irrigation rate, percentage of the disaster area had significant inhibitory effects (p <0.01) on ecological environment at the watershed scale. Finally, the increase of the total investment in ecological engineering might directly promote the ecological recovery at the county scale. Those results could provide a reasonable scientific basis for the rational development and utilization of regional forest resources, and sustainable socio–economic development. [ABSTRACT FROM AUTHOR]

Facing the challengeable PM2.5 pollution management across China, it is of significance to identify key pollution driving factors with a nation-region-city perspective and establish targeted joint management strategies on the trans-provincial scale. To identity hot study regions, the spatiotemporal pollution variation of PM2.5 in 2016 was explored using multi-scale spatial autocorrelation analysis based on the ground monitoring data for Chinese 366 cities. In the identified study regions, the relationship of natural factors and PM2.5 was analyzed on annual and daily scales via the correlation analyses, and the multiple socioeconomic driving forces of PM2.5 were identified utilizing geographic weighted regression (GWR) and principal component analysis. Consequently, 111 cities were identified as the hot regions, where their annual means of PM2.5 (AM) were 60.40 ± 14.23 μg·m−3 exceeding the Chinese level II standard and WHO Air Quality Guideline IT-1 (35 μg·m−3). The elevation and annual accumulative rainfall were found to be negatively correlated with the AM (p < 0.01). The increase of temperature and wind speed mainly helped daily PM2.5 reduce. Furthermore, the sensitive socioeconomic factors, mainly including the added value of the industry, the per capita gross domestic product and the per capital annual disposable income, were identified due to GWR. Then, based on the found spatial heterogeneity of sensitive factors, the selected 111 cities were identified into four types (Q 1 , Q 2 , Q 3 , Q 4) and two sub-regions, Q 1 +Q 2 (13+49 cities) and Q 3 +Q 4 (27+22 cities). Finally, for multi-scale environmental sustainability and equity, the joint management strategies were formulated for each cities pattern based on their feature of sensitive pollution driving forces and city role positioning. Image 1 • Spatiotemporal patterns of PM2.5 in 366 Chinese cities were explored on multi-scale. • Increase of elevation, rainfall/wind, temperature help annual/daily PM2.5 reduce. • PM2.5 was sensitive to social factors like Added value of industry, Per Capita GDP. • 111 hot cities were identified into four types on spatially heterogeneous features. • Joint management strategies were formulated for each kind based on driving factors. [ABSTRACT FROM AUTHOR]

Some literature has documented ambient fine particulate matter (PM 2.5) pollution and corresponding health burden in China, however, the knowledge of spatial disequilibrium of PM 2.5 and PM 2.5 -induced disease burden is scarce. With the help of gravity center model, spatial autocorrelation analysis as well as the region-specific exposure-response coefficient and ground-based PM 2.5 daily concentrations over 336 cities in China from January 2016 to December 2017, this paper provides a completive assessment for spatial disequilibrium of PM 2.5 -induced health burden. Improving PM 2.5 concentrations and declining PM 2.5 -induced health burden were found in China. The 2-year city-average concentration of the 336 cities was 45 ± 17μg/m3 with a range from 11μg/m3 to 128μg/m3. PM 2.5 -related deaths from stroke, ischemic heart disease, chronic obstructive pulmonary disease, hypertensive heart disease and lower respiratory infection amounted to 0.766 million in 2016 and 0.720 million in 2017. Much higher but decreasing PM 2.5 -induced mortality happened in the west, while higher and increasing PM 2.5 -induced deaths existed in the south. Strong spatial autocorrelation of PM 2.5 and PM 2.5 -induced disease burden existed among cities, illustrated by high-high (HH) clusters with significant spatial homogeneity and spatial spillover effect. The cities included in PM 2.5 concentration HH cluster had average PM 2.5 concentration 68.5 μg/m3, about 1.5 times higher than the national average; the cities involved in combined mortality HH cluster had mortality 90.5/105, 1.8 times as much as the national city-average level; about 46.4% of PM 2.5 -induced combined deaths happened in HH cluster cities. The cities embraced in the HH clusters of PM 2.5 concentration, population, deaths and mortality simultaneously, mostly located in Hebei, Shandong and Henan provinces, should be the top priorities for joint air pollution control. This paper provides important implications for policymakers regarding priority areas of joint air pollution control in China. Image 1 • PM 2.5 pollution contributed to around 0.74 million premature deaths per year. • Improving PM 2.5 levels and declining PM 2.5 -induced health burden were found. • Strong spatial disequilibrium and autocorrelation existed. • Much higher but decreasing PM 2.5 -induced mortality happened in west. • Cities overlapped in HH clusters should be the top priorities for joint control. [ABSTRACT FROM AUTHOR]

• Moran's I was applied to measure the spatial dependence of elemental associations. • The MEDIAN ± 1.5*IQR rule was used to identify negative and positive anomalies. • A case study based on Cu-Au-Mo, Cu-Mo and Sn mineralizations in the Jiurui, China was presented. Multivariate geochemical anomaly identification is one of the most important tasks in exploration geochemical data analysis. This paper proposes a new approach to identify multivariate geochemical anomalies which accounts for the degree of spatial autocorrelation among observations. This method is a combination of Robust Mahalanobis Distance (RMD), spatial autocorrelation analysis and robust statistics. The Minimum Covariance Determinant (MCD) estimator-based RMDs were first used to reduce the dimension of geochemical data sets. Spatial correlogram and local Moran's I statistics were then applied to determine the spatial variability (spatial scales) and to measure the degree of spatial autocorrelation at global and different local scales, respectively. Negative and positive anomalies were finally separated from a set of local Moran's I values using robust statistics, the MEDIAN ± 1.5*IQR (IQR: interquartile range) rule. We illustrated the proposed method by using a data set of 1842 stream sediment samples associated with Cu-Au-Mo and Sn mineralizations in the Jiurui ore district (Jiangxi province, southeastern China). The results showed that (i) the maximum spatial variability of RMDs were 12 km, in which strong positive spatial autocorrelation was detected at the first three spatial scales ranging from 0 to 6 km; (ii) both of negative and positive multivariate geochemical anomalies were strongly correlated with 13 known Cu-Au-Mo and Cu-Mo deposits, especially at spatial scales where high degrees of positive spatial autocorrelation were measured; (iii) weak anomalies associated with a magmatic-hydrothermal related Sn and polymetallic mineralization were successfully identified in the Zengiialong and Jianfengpo Sn deposits, which were not detected in previous studies. We demonstrate the combination of spatial autocorrelation analysis and robust statistics allows for improved identification of significant multivariate geochemical anomalies of the deposit-type sought from stream sediment geochemical data. [ABSTRACT FROM AUTHOR]