High-high Clusters Research Articles

Spatial differentiation characteristics of the Hemiptera insects in China.

The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high-high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio-economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision-making basis for biodiversity conservation and efficient pest control.

Lack of knowledge about the human papillomavirus vaccine among Brazilian adolescents: A cross-sectional study.

To analyze the distribution and factors associated with lack of knowledge about the human papillomavirus (HPV) vaccine among Brazilian adolescents.Cross-sectional study using the 2019 National School Health Survey among 17,805 Brazilian students of public and private schools, aged 13-17 years. The outcome variable was "lack of knowledge about the HPV vaccine" and the explanatory variables were sociodemographic, behaviors, knowledge, and health conditions. Logistic regression model was used to calculate Odds Ratio (OR) and 95% confidence intervals (95%CI). Spatial analysis techniques were used to determine the formation of clusters in the federated units with similar proportions of adolescents who were unaware of the vaccine. The lack of knowledge about the HPV vaccine was reported by 45.54% of Brazilian students. There was a higher chance of lack of knowledge having had sexual intercourse (OR 1.43; 95% CI 1.20-1.70); attending public school (OR 1.72; 95%CI 1.47-2.02) and located in the Northeast Region (OR 1.35; 95%CI 1.08-1.69). The lower chance of lack of knowledge were female gender (OR 0.41; 95% CI 0.35-0.48), higher maternal education (OR 0.62; 95% CI 0.50-0.77) self-rated health as Poor/very poor (OR 0.64; 95% CI 0.49-0.86) and receiving contraceptive counseling (OR 0.77; 95% CI 0.65-0.91). The proportion of lack of knowledge about the HPV vaccine was higher with the formation of High-High spatial clusters in the states of Maranhão, Piauí, and Pernambuco. Sociodemographic, health, and behavioral conditions and knowledge of students, as well as school characteristics, were associated with lack of knowledge about the HPV vaccine. A higher frequency of lack of knowledge about the HPV vaccine among adolescents was found in the states of the Northeastern Region.

Assessing Africa's child survival gains and prospects for attaining SDG target on child mortality.

This study assessed Africa's child survival gains and prospects for attaining Sustainable Development Goals (SDG) target 3.2. We analysed multiple country-level secondary datasets of 54 African countries and presented spatial analysis. Results showed that only 8 out of the 54 African countries have achieved substantial reductions in under-5 mortality with an under-five mortality rate (U5MR) of 25 deaths per 1,000 live births or less. Many countries are far from achieving this target. Results of the predictions using supervised machine learning on the Bayesian network reveal that the probability of achieving the SDG target 3.2 (i.e., having U5MR of 25 deaths per 1000 live births or less) increases (from 21.6% to 100%) when the contraceptive prevalence increases from 49.8% to 78.5%; and the use of skilled birth attendants increases from 44.8% to 86.3%; and percentage of secondary school completion of female increases from 42.5 to 74.0%. Our results from Local indicator of spatial autocorrelation (LISA) cluster maps show that 7 countries (mainly in West/Central Africa) formed the high-high clusters (hotspots for U5M) and may not achieve the SDG target 3.2 unless urgent and appropriate investments are deployed. As 2030 approaches, there is a need to address the problem of limited access to quality health care, female illiteracy, limited access to safe water, and poor access to quality family planning services, particularly across many sub-Saharan African countries.

Study on Spatial Effects of Influencing Factors and Zoning Strategies for PM2.5 and CO2 Synergistic Reduction.

China has identified the synergistic reduction of pollution and carbon emissions as a crit ical component of its environmental protection and climate mitigation efforts. An assessment of this synergy can provide clarity on the strategic management of both air pollution and carbon emissions. Due to the extensive regional differences in China, the spatial effects of influencing factors on this synergy exhibit variation across different provinces. In this study, the reduction indexes of PM2.5 and CO2 were calculated based on their reduction bases, reduction efforts, and reduction stabilities across provinces. Then, the synergistic reduction effect was assessed using an exponential function with the PM2.5 reduction index as the base and the CO2 reduction index as the exponent. Next, the MGWR model was applied in order to analyze the influencing factors of the synergistic reduction effect, considering natural settings, socioeconomic conditions, and external emission impacts. Finally, the k-means clustering method was utilized to classify provinces into different categories based on the degree of impact of each influencing factor. The results indicated that air circulation, vegetation, tertiary industry ratio, and emission reduction efficiency are major impact indicators that have a positive effect. The topography and emissions from neighboring provinces have a statistically significant negative impact. The spatial influences of different factors exhibit a distribution trend characterized by a high-high cluster and a low-low cluster. A total of 31 provinces are divided into three categories, and suggestions on the corresponding category are proposed, to provide a scientific reference to the synergistic reduction of PM2.5 and CO2.

Dynamic evolution of utilization efficiency of medical and health services in China.

In order to optimize the Chinese medical and health system and improve people's health level, the SFA Malmquist model, the spatial econometric model, and the standard deviation ellipse method were used to measure the efficiency of medical and health services in China's 31 provinces between 2010 and 2020. Study results indicated that the average efficiency value of the 31 provinces generally exceeded 0.8. Specifically, the average efficiency values in the eastern and central regions increased from 0.852 to 0.875 and from 0.858 to 0.88, respectively. In the western and northeastern regions, these values rose from 0.804 to 0.835 and from 0.827 to 0.854, respectively. From the perspective of spatial distribution, there were high-high and low-low clusters in most provinces with significant spatial dependence among them. This analysis reveals that medical and health services efficiency in China demonstrates a spatial pattern extending from northeast to southwest.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

What were the spatiotemporal evolution characteristics and influencing factors of global land use carbon emission efficiency? A case study of the 136 countries

Nations worldwide must collaborate to tackle the challenge of improving carbon emission efficiency and optimizing land use effectively. This study extends the study area from provinces and cities, regional city clusters, and countries to 136 major countries in the world, and shows the differences between countries. The paper analyzed the spatiotemporal evolution characteristics and influencing factors of land use carbon emission efficiency (LCEE) in these 136 countries from 2000 to 2019 using the super-efficiency slacks-based measure data envelopment analysis (Super-SBM DEA) model, global malmquist index, spatial autocorrelation measurement tools and Spatial Durbin model. It can help to promote low-carbon and green development while simultaneously improving the global LCEE. The findings indicate that: (1) Over the study period, there is a trend toward a decline in the LCEE worldwide. (2) The primary cause of the worldwide LCEE’s declining trend during the study period is the reduction in technical efficiency. (3) The low-low cluster type accounts for more, while the high-high agglomeration type accounts for less and exhibits a sluggish growth pattern. On the whole, there is a phenomenon of non-aggregation and non-uniformity. It is necessary to increase the driving effect of high-high cluster radiation. (4) Economic development level, investment level, population size, public service level and environmental sustainability level all have a significant impact on LCEE, while industrial development level has no significant impact on LCEE. From a global perspective, the radiation driving role of countries with high LCEE should be increased. Improve the efficiency and technological level of land use emission in light of local conditions, and promote independent innovation in green and low-carbon technologies. This is conducive to the establishment of long-term low-carbon growth.

Spatial distribution patterns of human resources allocation in maternal and child healthcare institutions in China from 2016 to 2021

BackgroundIn China, economic, urbanization, and policy differences between the eastern and western regions lead to uneven healthcare resources. This disparity is more pronounced in the west due to fewer healthcare personnel per thousand individuals and imbalanced doctor-to-nurse ratios, which exacerbates healthcare challenges. This study examines the spatial distribution of human resources in maternal and child healthcare from 2016 to 2021, highlighting regional disparities and offering insights for future policy development.MethodsThe data were sourced from the “China Health and Family Planning Statistical Yearbook” (2017) and the “China Health and Health Statistics Yearbook” (2018–2022). This study utilized GeoDa 1.8.6 software to conduct both global and local spatial autocorrelation analyses, using China’s administrative map as the base dataset.ResultsFrom 2016 to 2021, there was an upward trend in the number of health personnel and various types of health technical personnel in Chinese maternal and child healthcare institutions. The spatial distribution of these personnel from 2016 to 2021 revealed clusters characterized as high-high, low-low, high-low and low-high. Specifically, high-high clusters were identified in Guangxi, Hunan, Jiangxi, and Guangdong provinces; low-low in Xinjiang Uygur Autonomous Region and Inner Mongolia Autonomous Region; high-low in Sichuan province; and low-high in Fujian and Anhui provinces.ConclusionsFrom 2016 to 2021, there was evident spatial clustering of health personnel and various health technical personnel in Chinese maternal and child healthcare institutions, indicating regional imbalances.

Assessment of ecosystem health and driving forces in response to landscape pattern dynamics: the Shibing Karst world natural heritage site case study

The Shibing Karst constitutes a pivotal component of the "South China Karst," and its ecosystem health integrity crucially influences the Outstanding Universal Value (OUV) of the corresponding Natural World Heritage (NWH). Consequently, robust ecosystem health assessment (EHA) is imperative for the judicious conservation and management of this heritage, as well as for the sustainable progression of the region. This research assessed the health of the Shibing Karst ecosystem from 2004 to 2020 by employing changes in landscape patterns through the Vigor-Organization-Resilience-Ecosystem Services (VORS) model. Spatial autocorrelation was employed to discern the spatial coherence and evolutionary patterns of ecosystem health, whereas a geo-detector ascertained the pivotal determinants impacting regional ecosystem vitality. The findings revealed that: (1) The landscape patterns distribution in the study area exhibited considerable constancy, primarily comprising forest land, with a rising trajectory in construction land and water, juxtaposed with a recession in shrubland, grassland, paddy land, and dryland expanses. (2) From 2004 to 2020, the ecosystem of the study area maintained its health and remained stable, with mean values of 0.8303, 0.7689, 0.6976, and 0.7824, respectively, showing an evolutionary trend of an initial downtrend trend followed by an upswing, with 2016 marking a pivotal juncture. (3) Spatial clustering analysis highlighted significant clustering characteristics of ecosystem health, with a nominal decrease in the Global Moran's I index from 0.666 to 0.665, which is indicative of a subtle decrease in clustering over time. High-high clustering areas were predominantly located within the World Heritage Site (WHS), while low-low clustering areas were mainly distributed in the southeastern part of buffer zone. (4) Land use and cover change (LUCC) and Ecosystem Services (ESs) were identified as the primary indexes of EHA, with Ecosystem Resilience (ER), Ecosystem Vigor (EV), and Ecosystem Organization (EO) exerting relatively mild influences. This study provides a scientific framework for policymakers in local governance to devise strategies for ecosystem conservation and management, enhances the analytical perspective on the integrity and conservation of Karst Natural World Heritage (KNWH).

Sarcoidosis Mortality in North Carolina: Role of Region, Race, and Other Sociodemographic Variables.

There is regional variability in sarcoidosis mortality across the United States. North Carolina ranks highly in sarcoidosis-related mortality, especially among African Americans (AA). We sought to determine any regional variability of sarcoidosis-related mortality and the relationship to sociodemographic determinants of health in North Carolina. Counties in North Carolina were categorized into three distinct geographic regions: Western, Piedmont, and Eastern. Sarcoidosis deaths were stratified by region, race, and gender. We conducted a mapping and cluster analysis utilizing ArcGIS; Global and Local Moran's I was used to determine the prevalence, spatial autocorrelation, and clustering of mortality vis-a-vis various sociodemographic variables, occupational/environmental exposures, and levels of atmospheric particulate matter less than 2.5 microns in size (PM2.5). Multivariate linear regression with exposure limited to the county level was used to determine the relationship between sarcoidosis mortality and the variables of interest. Eastern North Carolina (ENC) had the highest age-adjusted sarcoidosis mortality rate (1.16/100,000 versus 0.49/100,000 in Piedmont and 0.32/100,000 in the Western region) with statistically significant high-high mortality clusters (P < .001 for Global Moran's I). Several sociodemographic and occupational factors (proportion of AA, obese adults, and individuals working in nature) were more prevalent in ENC. Region and proportion of AA were the significant mortality predictors in our multivariate analysis. This was a cross-sectional study with exposure limited to the county level. Associations do not imply causality and risks cannot be extrapolated to the individual level. There is regional variability of sarcoidosis mortality in North Carolina. Eastern North Carolina had the highest mortality with high-high mortality clusters.

Association of neighborhood-level superfund sites with atmospheric toxic exposures and increasing cancer risk in the United States: A proof-of-concept geospatial study.

10577 Background: Decades of underinvestment have disproportionately exposed several neighborhoods in the US to hazardous conditions. Superfund is the name given to the environmental program established by the US Congress in 1980, to address abandoned hazardous waste sites. The extent to which Superfund sites have contributed to differences in cancer risk remains unknown. We investigated atmospheric toxic exposures increasing cancer risk by proximity to Superfund sites at the neighborhood level. Methods: In this cross-sectional study of US neighborhoods, census-tract level air toxic cancer risk data from the Environmental Protection Agency for 2019 were linked with census-tract level geolocation data of ~1300 Superfund sites from the Superfund Enterprise Management System database. Linear regression models were utilized to explore association between cancer risk and proximity to Superfunds. Geographically Weighted Regression models explored spatial non-stationarity by modeling spatial relationships that vary across the study area. Bivariate Local Indicators of Spatial Association (LISA) cluster maps were created for superfund site proximity and cancer risk (high-high, low-low, low-high, high-low). Kruskal-Wallis testing compared the distributions of different variables across the four LISA cluster types. Results: Study explored 82,770 census tracts nationwide. For every 10 percentile increase in Superfund proximity, there was an increase of 4% in cancer risk (p &lt; 0.001). This association was strongest in the Intermountain West, South-Southeast, and Northeast regions. High-high clusters (high proximity to Superfund, high cancer risk) had higher percentage of non-White, low-income, unemployed and lower education attainment populations compared to low-low clusters (all p &lt; 0.001). Within high-high clusters, non-White population had the strongest correlation with proximity to Superfunds. Cancer risk was much higher for the top 10% than the bottom 10% of proximity to Superfunds (Table). Conclusions: Our study highlights higher clustering of cancer risk around neighborhoods with existing Superfund sites. Further, we demonstrate the closer a neighborhood is to a Superfund site, the greater the cancer risk is with associated sociodemographic changes. Using a novel approach to geospatial analytics, the results demonstrate the first clear evidence of a dangerous association between Superfunds and healthcare, and encourage the urgent prioritization of Congressional policies that optimize investment in Superfund site clean up for public health. [Table: see text]

Geographical variation in dementia prevalence across China: a geospatial analysis

Dementia poses great health and social challenges in China. Dementia prevalence may vary across geographic areas, while comparable estimations on provincial level is lacking. This study aims to estimate dementia prevalence by provinces across China, taking into account risk factors of individual level and potential spatial correlation of provinces. In this study, 17,176 adults aged 50 years or older were included from the fourth wave of the ChinaHealth and Retirement Longitudinal Study (CHARLS 2018), covering 28 provinces, autonomous regions and municipalities. To improve provincial representativeness, we constructed provincial survey weights based on China 7th census (2020). The prevalence of dementia and 95% Bayesian credible intervals (BCIs) were estimated using a Bayesian conditional autoregressive (CAR) model with spatially varying coefficients of covariates. The weighted prevalence of dementia at provincial level in China in 2018 ranged from 2.62% (95%BCI: 1.70%, 3.91%) to 13.53% (95%BCI: 8.82%, 20.93%). High dementia prevalence was concentrated in North China, with a prominent high-high cluster, while provinces of low prevalence were concentrated on East and South China, characterized by a low-low cluster. Ordered by the median estimation of prevalence, the top 10% of provinces, include Xinjiang, Jilin, and Beijing. Meanwhile, Fujian, Zhejiang, and Guangdong rank among the last. The association between dementia prevalence and drinking, smoking, social isolation, physical inactivity, hearing impairment, hypertension, and diabetes exhibits provincial variation. Our study identifies a geospatial disparity in dementia prevalence and risk factor effects across China's provinces, with high-high and low-low clusters in some northern and southern provinces, respectively. The findings emphasize the need for targeted strategies, such as addressing hypertension and hearing impairment, in specific regions for more effective dementia prevention and treatment. National Science Foundation of China/the Economic and Social Research Council, UK Research and Innovation joint call: Understanding and Addressing Health and Social Challenges for Ageing in the UK and China. UK-China Health And Social Challenges Ageing Project (UKCHASCAP): present and future burden of dementia, and policy responses (grant number 72061137003, ES/T014377/1).

Analysis of spatial pattern of water supply continuity in an Indian metropolis: a case study of Hyderabad

ABSTRACT Water supply continuity is a crucial indicator of service efficiency. Multiple South Asian cities have Intermittent Water Supply (IWS). Water supply equity in cities depends on the spatial patterns of intermittency. The present study used Kernel Density Estimation (KDE) to investigate the spatial pattern of water supply intermittency for 3071 localities of Hyderabad, India. Further, the relationship between population density and supply continuity has been examined in 146 wards of Hyderabad using Spatial Autocorrelation and Geographically Weighted Regression (GWR). Spatial Autocorrelation defines two distinct clusters of central wards for high continuity (12 wards) and population density (19 wards). However, the ward clusters are different for continuity and density, with only three central wards (2.06% of all wards) being common in both the high-high clusters. The results highlight that the water supply continuity in Hyderabad has minimal consideration for population density, resulting in a spatially inequitable water supply.

Analysis of the evolution and influencing factors of ecological environment quality after the transformation of resource-exhausted cities based on GEE and RSEI: a case study of Xuzhou City, China

ABSTRACT This study used Google Earth Engine (GEE) to get and process Landsat images of Xuzhou from 2008 to 2020, and built remote sensing ecological indices (RSEI). This study analyzed the spatiotemporal features and drivers of ecological environment quality after Xuzhou’s transformation using landscape pattern index, spatial autocorrelation, and geographic detector methods. The results showed that: (1) The ecological environment quality of Xuzhou improved steadily but unevenly from 2008 to 2020; the west and central urban areas had poor but improving quality, while the east had the opposite trend; (2) The landscape pattern indices of ecological environment quality in different regions of Xuzhou did not change consistently from 2008 to 2020; (3) The spatial distribution of ecological environment quality had significant autocorrelation, with high-high clusters in the east and low-low clusters in the center and the urban areas of each district and county; (4) NDVI is the component index that has the greatest impact on the changes in the RSEI. Among the single factors, land use conditions have the strongest influence on ecological environmental quality. After the interaction of dual factors, the explanatory power is stronger than that of any single factor. The research results can support urban planning and design, ecological environment protection, etc. in Xuzhou, and offer insights for monitoring and evaluation of ecological environment quality after the transformation of resource-exhausted cities.

Analysis of the spatial–temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt

As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial–temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial–temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor’s effecting intensity and impacting direction, as well as the dual-factors’ interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006–2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of “downstream areas > midstream areas > upstream areas”, “high in the east and low in the west”, “hot in the east and cold in the west”, while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.

How public environmental appeals affect the collaborative governance in pollution and carbon reduction: Evidence from spatial effects across Chinese cities

China has always adhered to the strategy of sustainable development. It is prevalent the public want a good living environment, which requires local governments and businesses to enhance their environmental governance capabilities. Using the panel data from Chinese cities from 2012 to 2019 and econometrics models, we examine the impact mechanisms of public environmental appeals (PEA) on efficiency of collaborative governance in pollution reduction and carbon mitigation (GPC). Results indicate that there is a positive spatial clustering of GPC across cities, with high-high clustering is notably concentrated in the southern regions of China and low-low clustering is prevalent in the northern regions. Spatial econometrics model results reveal that the stronger PEA, the higher GPC. The result of mechanism analysis shows the mediation of environmentally friendly technological innovation is crucial. Subsequent inquiry uncovers that the digital economy positively moderates the impact of PEA on GPC. The Belt and Road policy region exhibits heightened sensitivity to PEA, thereby enhancing the positive impact of PEA on GPC.

Investigating the spatial distribution mechanisms of traditional villages from the human geography region: A case study of Jiangnan, China

Investigating the spatial distribution and adaptation mechanisms of traditional villages is crucial for their development and human well-being. However, relatively few studies have focused on the interaction and comprehensive influence of historical-cultural, natural-geographic and socio-economic factors on traditional villages in Jiangnan region. Employing geographically weighted regression (GWR) and Geodetector methods, we developed an integrated framework. We found that (1) Traditional villages exhibit a “one-core, multiple-periphery” clustering pattern (nearest neighbor index <1), with significant high-high and low-low local spatial clustering characteristics and preferring gentle and relatively Complex terrains to adapt to the need to avoid warfare. (2) Urbanization negatively affects traditional village density patterns, while natural-geographic complexity has a positive impact. Their interaction nonlinearly amplifies this effect, with the urbanization-GDP interplay being particularly significant (q = 0.765). (3) Factors exhibit significant spatial heterogeneity in their impact on traditional village density, with the explanatory power increasing by 20% (GWR/R2 = 0.65). The urbanization rate and slope are the most critical socio-economic and natural-geographic factors, respectively (local regression coefficient: 23.31 and 11.58). Our study transcends administrative boundaries, revealing how historical-cultural, natural-geographical, and socio-economic factors collectively influence the distribution of traditional villages. This not only provides a new theoretical foundation for the conservation and development of traditional villages but also provides a new methodology basis for research on traditional villages.

Provincial-scale assessment of vulnerability and resilience to drought in China

Frequent droughts have caused severe disaster losses in China. Such events can be minimized by enhancing the country's resilience and reducing its vulnerability, where this can ensure socioeconomic stability and sustainable development. Evaluating the vulnerability and resilience to drought is thus crucial for effectively managing the risk of disasters and promoting sustainable socioeconomic development. In this study, we constructed a comprehensive framework to assess the spatiotemporal characteristics of China's vulnerability and resilience to drought at the provincial scale from an input-output perspective by using the Super-efficiency Data Envelopment Analysis (DEA) model and the Super-efficiency Slacks-Based Measure DEA (SBM-DEA) model. This study focused on drought drivers, the disaster-forming environment, drought bearers, disaster intensity, and recovery. The results showed that the vulnerability to drought of 42 % of China's provinces decreased from 2010 to 2022, that of only 29 % of the provinces increased, while the status of a majority of provinces improved in general. The center of gravity of the vulnerability to drought moved toward the southwest over time and a spatial clustering of vulnerability was observed, with High-High clusters moving from the north to the south. Moreover, the resilience to drought declined in 36 % of provinces and increased in only 20 %, reflecting poor resilience overall. The center of gravity of China's overall resilience to drought moved northward, with a relatively stable spatial pattern and prominent clusters of Low-Low resilience indicating a pressing need for improvement. Areas with high vulnerability and low resilience were concentrated in inland western and eastern regions, and this highlights the importance of drought prevention and mitigation in provinces like Xinjiang, Inner Mongolia, Jiangxi, and Fujian. The findings here provide valuable insights for mitigating the risk of drought and promoting sustainable socioeconomic development.

Spatio-temporal variations of habitat quality and its driving factors in the Yangtze River Delta region of China

Global biodiversity is still being lost at an alarming rate. How to slow the destruction of habitats to protect biodiversity is an urgent issue. As one of the most rapidly developing and active economic regions in China, the habitat quality assessment and impact factor analysis in the Yangtze River Delta region are important for promoting socio-economic development and improving the local ecological environment. The InVEST mode and Geodetector method was used to explore the spatial and temporal variations of the habitat quality and its driving factors in the Yangtze River Delta region. The results show that the averaged habitat quality index showed a decreasing trend during 1980–2020, which accounted about 71.86 % of the Yangtze River Delta region. The habitat quality showed an overall spatial pattern of high in the south and low in the north, and the regional distribution of low habitat quality was dominated. The Moran's I values were larger than 0.8 during 1980–2020, showing a significant positive spatial correlation and a high degree of spatial clustering. The high-high clusters of habitat quality were mainly located in the southwest of the study area, while the low-low clusters were mainly located in the north. During 2025–2060, the habitat quality under the three scenarios exhibited SSP1-RCP19 > SSP2-RCP45 > SSP5-RCP85 and all showed a trend of decreasing in the north of the study area and increasing in the south. Under the two scenarios SSP1-RCP19 and SSP2-RCP45, the habitat quality generally showed an increasing trend, while under the SSP5-RCP85 scenario, it showed a decreasing trend. The slope, DEM, precipitation, construction land index and their interactions played a dominant role in the spatio-temporal variations of the habitat quality. Therefore, the slope, DEM, precipitation and construction land index should be considered when formulating habitat quality protection countermeasures.

Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect

It is crucial for urban greening studies to comprehend the spatial synergy effect of the urban green space ecosystem on air pollution and the heat island effect. However, there are limited studies that have considered the impact of green space ecosystems on different air pollutants and urban heat island effects across different seasons from an urban space perspective. Therefore, this study took an urban space perspective and established a high-precision spatial grid with a resolution of 500 m. The spatial econometric regression and bivariate local Moran's I test were employed to analyze the impacts of urban green space ecosystems on air pollutants (including summer ozone (O3) concentration, winter fine particulate matter (PM2.5) concentration and winter inhalable particulate matter (PM10) concentration) and heat islands in summer and winter.The results showed that the spatial regression coefficients between green space and heat islands, PM2.5 concentration and PM10 concentration are significantly negative, −0.00049***, −0.00329***, and −0.00365*, respectively. The urban green space ecosystems are observed to effectively mitigate the heat island effect and help to reduce PM2.5 and PM10 pollution in winter. There are 88 high-high clustering in the spatial interactions between green space and ozone concentration in summer, that is, the grids with high O3 concentration surrounded by the high green space grids. It implies that urban green space ecosystems can significantly increase O3 pollution in summer. This study reveals that low BVOCs tree species should be widely planted in urban green spaces, while large-scale green space parks should be established in the urban region to continuously increase the proportion of green space. This study would contribute to improving the awareness of the government and relevant employees on scientific urban greening methods.