Spatiotemporal evolution and influencing factors of ecological welfare performance in the Dongting Lake area, China

Spatiotemporal evolution and driving factors of tourism ecological adaptation in the Dongting Lake Area, China

Spatiotemporal evolution for early warning of ecological carrying capacity during the urbanization process in the Dongting Lake area, China

Amid tightening environmental constraints and rising demand for high-quality development, evaluating ecological welfare performance (EWP) is critical for sustainable development. However, existing research on EWP exhibits limitations in terms of scale, methodology, and spatiotemporal evolution. To address these limitations, this study quantitatively assesses the spatiotemporal evolution and influencing factors of EWP across 110 cities in the Yangtze River Economic Belt from 2013 to 2022, using city-level data and analytical methods such as Geodetector. The results indicate that the overall EWP was relatively low during the study period, with a mean value of only 0.4321, exhibiting a “U”-shaped trend. Significant disparities among cities were observed, with 53.64% of cities remaining in the low-efficiency range in 2022, exhibiting an “N-shaped” spatial distribution pattern of “low-high-low-high” from west to east. EWP exhibited significant negative global spatial autocorrelation in 2013, while other years showed no significant correlation. Local spatial autocorrelation revealed four clustering types: high-high, high-low, low-low, and low-high. The degree of spatial dispersion initially expanded and then contracted, with the center shifting southwestward overall. The explanatory power of the influencing factors on EWP were ranked, from strongest to weakest, as follows: urbanization level, industrial structure, population density, technological investment, and environmental regulation.

BackgroundThe reasonable allocation of social care resources for the older adults is a key measure to actively respond to population aging. This study aims to evaluate the evolutionary trend, spatial differences and influencing factors of the social elderly care resources (SECR) allocation in China.MethodsThis study constructed a comprehensive index system consisting of three dimensions: material resources, human resources and financial resources, to measure the level of SECR in mainland China. The Kernel density estimation was used to reveal the dynamic evolution trend, and Dagum Gini Coefficient and its decomposition method were used to investigate the equity of SECR allocation. Spatial panel regression models were used to analyze the influencing factors of the allocation of SECR.ResultsThe level of SECR is rising from 0.197 in 2013 to 0.208 in 2019. The middle-high- and high-level areas of SECR were mainly distributed in the eastern and western China. The Gini coefficient of SECR decreased from 0.262 in 2013 to 0.249 in 2019. Per capita GDP, the proportion of social welfare expenditure in GDP and the proportion of the tertiary industry in GDP have significant positive effects on the allocation of SECR. Population aging and the development of service industry exhibit significant negative spatial spillover effects on the allocation of SECR.ConclusionsThe fairness of the allocation of SECR in China has been improved, while the spatial distribution is imbalanced. Economic development, fiscal input and the development of service industry have significant positive effects while population aging has significant negative effects on the SECR allocation.

IntroductionWithin the context of the yearly improvement of particulate matter (PM) pollution in Chinese cities, Surface ozone (O3) concentrations are increasing instead of decreasing and are becoming the second most important air pollutant after PM. Long-term exposure to high concentrations of O3 can have adverse effects on human health. In-depth investigation of the spatiotemporal patterns, exposure risks, and drivers of O3 is relevant for assessing the future health burden of O3 pollution and implementing air pollution control policies in China.MethodsBased on high-resolution O3 concentration reanalysis data, we investigated the spatial and temporal patterns, population exposure risks, and dominant drivers of O3 pollution in China from 2013 to 2018 utilizing trend analysis methods, spatial clustering models, exposure-response functions, and multi-scale geographically weighted regression models (MGWR).ResultsThe results show that the annual average O3 concentration in China increased significantly at a rate of 1.84 μg/m3/year from 2013 to 2018 (160 μg/m3) in China increased from 1.2% in 2013 to 28.9% in 2018, and over 20,000 people suffered premature death from respiratory diseases attributed to O3 exposure each year. Thus, the sustained increase in O3 concentrations in China is an important factor contributing to the increasing threat to human health. Furthermore, the results of spatial regression models indicate that population, the share of secondary industry in GDP, NOx emissions, temperature, average wind speed, and relative humidity are important determinants of O3 concentration variation and significant spatial differences are observed.DiscussionThe spatial differences of drivers result in the spatial heterogeneity of O3 concentration and exposure risks in China. Therefore, the O3 control policies adapted to various regions should be formulated in the future O3 regulation process in China.

Aims: This study aims to determine the relationship between population density and altitude of the territory and DHF incidence in Yogyakarta City in 2017-2018.
 Study Design: The study was designed as an observational cross-sectional study.
 Place and Duration of Study: This study was conducted in Yogyakarta from 2017 to 2018. Methodology: This quantitative study employs an analytic observational cross-sectional design. Moran's I and LISA tests were used to analyze the data.
 Results: Based on bivariate Moran's Scatterplot analysis, the density of residents with dengue incidence showed negative spatial autocorrelation (I=-0.308), and the altitude with dengue incidence showed negative spatial autocorrelation (I=-0.128), indicating that the majority of the sub-district was scattered in quadrants II and IV. The LISA density bivariate test results on residents with dengue incidence revealed that the Gondomanan Sub-district has positive autocorrelation (li= 0.30) with a Low-Low quadrant and statistical significance (P-Value=0.040.05). In contrast, Kotagede Sub-district has negative autocorrelation (li=-2.31) with a Low-High quadrant and statistical significance (P-Value=0.000.05). Gondomanan Sub-District has positive autocorrelation (li=0.67) at the Low-Low quadrant and statistical significance (P-Value=0.03 0.05), whereas Kotagede Sub-District has negative autocorrelation (li=-2.86) at the Low-High quadrant and statistical significance (P-Value=0.000.05).
 Conclusion: Density residents have a negative autocorrelation with the incidence of DHF in Yogyakarta City. Local spatial density residents with DHF cases were found in the Gondomanan Sub-District with positive spatial autocorrelation. At the same time, Kotagede Sub-District had negative spatial autocorrelation in contrast to an altitude with a global spatial connection toward DHF incidence in Yogyakarta with negative autocorrelation.

Irrational land use has triggered many serious ecological problems worldwide, especially in regions where human–land conflicts are prominent. However, the driving mechanisms at different spatiotemporal scales vary and have not been addressed in detail. This study explored the variations of land use and landscape patterns from 1990 to 2020 in the Dongting Lake Ecological Economic Zone (DLEEZ) by land-use transfer matrix, landscape pattern indices methods, etc. We also combined the geographical detector and geographically and temporally weighted regression (GTWR) methods to analyze the spatiotemporal changes in driving forces of land-use changes. The results indicated that forest land, cropland, and wetland were the predominant land-use types, accounting for nearly 90% of the total. Cropland decreased by a total of 1787.55 km2 during the 30-year period, and land-use changes mainly involved the conversion of cropland to other land-use types. The overall fragmentation of the regional landscape increased, with the fragmentation of forest land and cropland increasing. Land-use changes were mainly influenced by human activities like socio-economic and policy factors. Notably, the development of agriculture and aquaculture poses a threat to the maintenance of the area of wetland in the DLEEZ. This study provides a reliable scientific basis for mitigating land-use conflicts and rationally planning land use to formulate a sustainable development strategy for land resources in the DLEEZ.

Rapid expansion in Mongolia has resulted in an inequality in livelihood, growing air pollution, and unevenly distributed social goods, which implies that the urbanization of Mongolia is experiencing a chronic imbalance. Using 21 Aimags and 1 capital in Mongolia as the research object, this paper provides a detailed assessment of the coupling coordination degree (CCD) to analyze the coupling and coordination relationship between urbanization and urban residents’ livelihood level. The results suggest that there is a serious imbalance in the coordination of urban development and urban residents’ livelihood among the Aimags of Mongolia. The high CCD values were mainly concentrated in the central regions of Mongolia, while the low values were clustered in the western and eastern areas. The spatial Durbin model was employed to analyze the driving forces of the CCD, and the results imply that the secondary industry share and the number of registered enterprises play significant roles in promoting coordination between urbanization and urban residents’ livelihood level. Here, we present an overview of how our analysis can provide useful insights for policymakers and decision makers.

Negative spatial autocorrelation is one of the most neglected concepts in quantitative geography, regional science, and spatial statistics/econometrics in general. This paper focuses on and contributes to the literature in terms of the following three reasons why this neglect exists: Existing spatial autocorrelation quantification, the popular form of georeferenced variables studied, and the presence of both hidden negative spatial autocorrelation, and mixtures of positive and negative spatial autocorrelation in georeferenced variables. This paper also presents details and insights by furnishing concrete empirical examples of negative spatial autocorrelation. These examples include: Multi-locational chain store market areas, the shrinking city of Detroit, Dallas-Fort Worth journey-to-work flows, and county crime data. This paper concludes by enumerating a number of future research topics that would help increase the literature profile of negative spatial autocorrelation.

As an important ecological hinterland in Hunan Province, the Dongting Lake area has an irreplaceable role in regional socioeconomic development. However, owing to rapid environmental changes and complex land use relationships, land use/land cover (LULC) changes are actively occurring in the region. Therefore, assessment of the current LULC status and the future development trend for sustainable economic development is of considerable importance. In this study, the driving mechanisms of spatiotemporal evolution for land use conflicts (LUCF) in Dongting Lake from 2000 to 2020 were analyzed by constructing a LUCF model. Additionally, a new model, EnKF-PLUS, which couples ensemble Kalman filtering (EnKF) with patch-generating land use simulation (PLUS), was developed to predict the LULC changes and LUCF in 2030 under different scenarios. The results provide three insights. First, during the period of 2000–2020, high LUCF values were concentrated in highly urbanized and densely populated areas, whereas low LUCF values were centered in hilly regions. Secondly, the impacts of static factors (topographical factors) and dynamic factors (population, GDP, and climate factors) on changes in LUCF were regionally differentiated. Thirdly, our results indicate that the implementation of land use strategies of cropland conservation and ecological conservation can effectively mitigate the degree of LUCF changes in the region and contribute to the promotion of the rational allocation of land resources.

Negative spatial autocorrelation refers to a geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values, the neighbors of locations with intermediate values have intermediate values, and the neighbors of locations with small values have large values. Little is known about negative spatial autocorrelation and its consequences in statistical inference in general, and regression-based inference in particular, with spatial researchers to date concentrating mostly on understanding the much more frequently encountered case of positive spatial autocorrelation. What are the spatial contexts within which negative spatial autocorrelation should be readily found? What are its inferential consequences for regression models? This paper presents selected empirical examples of negative spatial autocorrelation, adding to the slowly growing literature about this phenomenon.

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.

Using grey incidence analysis method, this paper analyzed the impact of various factors on the coordinated development of economy-society-environment system of Wanjiang urban belt to explore the mechanism of the coordinated development of regional economy-society-environment system. From 2011 to 2018, in the coordinated development of economic, social and environmental systems under the overall level of Wanjiang urban belt, the degree of influence of the development of each subsystem on the coordinated development is ranked: environmental subsystem economic subsystem social subsystem. In the environmental subsystem, environmental state = environmental pressure environmental response, and surface water quality, per capita industrial solid waste production, green coverage of constructed areas are three most influential social indicators. In the economic subsystem, economic structure developing quality economic benefits, proportion of foreign direct investment in GDP, the proportion of R & D expenditure in GDP, the proportion of tertiary industry in GDP are three most influential economic indicators. In the social subsystem, public service living standards quality of life, and urbanization rate of permanent population, per capita road mileage, and the number of hospital beds per 10,000 people are three most influential social indicators.

Water Resource Carrying Capacity (WRCC) plays a crucial role in promoting regional sustainable development. However, current studies on WRCC in coastal regions rarely consider marine-related indicators and future projections. To address these gaps, this study incorporates marine-related indicators to analyze the spatiotemporal evolution and key obstacles of WRCC in China’s coastal provinces from 2013 to 2022, and further predicts its trends over the next five years. The results indicate that from 2013 to 2022, WRCC in the region exhibited a fluctuating upward trend but remained at a relatively low level, showing a spatial distribution pattern of Southern > Eastern > Northern regions. A significant negative global spatial autocorrelation in WRCC was observed only in 2022. The spatial dispersion first expanded and then contracted, with the center gradually shifting northeastward each year. The main obstacles affecting WRCC in China’s coastal provinces include per capita coastline length, ecological water use ratio, per capita water resources, and the scale of seawater desalination projects. WRCC is expected to continue a modest increase over the next five years, though it will remain at Level III. These findings provide valuable insights for managing water resource challenges in coastal regions worldwide.

Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.