Significant Positive Autocorrelation Research Articles

Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers

Understanding internal dynamics of state anxiety during COVID-19 pandemic: Seven-wave longitudinal findings via panel network analysis.

Research on state anxiety has long been dominated by the traditional psychometric approach that assumes anxiety symptoms have a common cause. Yet state anxiety can be conceptualized as a network system. In this study, we utilized data from the COVID-Dynamic dataset from waves 7 to 13, collected at three-week intervals from June 6, 2020, to October 13, 2020, and included 1,042 valid participants to characterize the internal dynamics of state anxiety. Using the Gaussian graphical model along with strength centrality, we estimated three network models of state anxiety. The between-subjects and contemporaneous network showed numerous positive relations between items and some unexpected negative relations. Three communities were identified in the between-subjects network, and two communities were identified in the contemporaneous network. The temporal network showed the coexistence of positive and negative predictions between items after three weeks. Several items exhibited significant positive autocorrelations after three weeks. These findings have implications for anxiety theory and clinical interventions at between-subjects and within-subjects levels.

Evaluation Index System of Rural Ecological Revitalization in China: A National Empirical Study Based on the Driver-Pressure-State-Impact-Response Framework

Rural ecological revitalization (RER) is one of the five goals of China’s rural revitalization strategy. However, there is a lack of an effective index system to evaluate RER levels, which hinders the implementation of this national policy and reduces the effectiveness and efficiency of public resource input. Using the driver-pressure-state-impact-response (DPSIR) framework, this study developed an evaluation framework consisting of 5 subsystems, 12 secondary indicators, and 33 tertiary indicators. Using the entropy-weighted TOPSIS method, we analyzed a set of 30 provinces’ data and empirically determined the weights of each indicator. We found that the response subsystem had the largest weight (0.338), followed by the state (0.271), impact (0.148), pressure (0.130), and driver (0.113). We then evaluated the RER level in each province and found that five provinces had high RER levels, 16 provinces had moderate RER levels, and nine provinces had low RER levels. Using Moran’s I, we examined spatial autocorrelation of provincial RER levels at global and local dimensions. We found significant positive global autocorrelations across all subsystems, indicating that geological aggregation exists in all RER subsystems. The local autocorrelation results showed that low–low and high–high patterns were the dominant local autocorrelation patterns. According to the findings, we discussed the possible implications of this RER evaluation index system and provided policy recommendations for strengthening RER in different regions across the country.

Exploring spatiotemporal distribution characteristics of air quality and driving factors: empirical evidence of 288 cities in China.

Excellent air quality is important for China to achieve high quality economic development. The paper analyses the spatial and temporal distribution characteristics of the air quality index (AQI) in 288 Chinese cities, and further investigates the driving factors affecting air quality using the spatial Durbin model (SDM) based on the panel data of 288 Chinese cities from 2014 to 2021. The results of the study show that: (1) China's air quality level has improved in general, but there are large differences in air quality between regions; (2) China's AQI has significant spatial positive autocorrelation, and the Moran's scatter plot shows a high-high and low-low agglomeration; (3) The driving factors of air quality have different effects, and regional heterogeneity is obvious. Some developed regions in China have already crossed the inflexion point of the environmental Kuznets curve (EKC); promoting industrial upgrading and reducing pollutant emissions can significantly improve urban PM2.5 concentrations; and the "Three-Year Strategy for Conquering the Blue Sky War" policy has lowered the AQI in North China and improved PM2.5 concentrations nationwide. Based on the above findings, the paper puts forward corresponding policy recommendations.

Towards zero-carbon cities: how does environmental information disclosure impact the risk of carbon locking?

Utilizing panel data from 119 cities spanning from 2013 to 2019, the study rigorously investigates the influence of environmental information disclosure (EID) on mitigating the risk of urban carbon lock-in through the application of both two-way fixed effects and spatial econometric models. The findings robustly indicate that EID plays a significant role in facilitating carbon unlocking within cities, a conclusion that remains steadfast even after addressing potential endogeneity issues. Mechanistically, it is revealed that EID can effectively decrease the risk of carbon lock-in by enhancing urbanization levels and reducing energy intensity. Furthermore, the study unveils that the efficacy of EID in curbing urban carbon lock-in risk is also contingent upon factors such as carbon intensity, population size, and environmental quality, highlighting the multifaceted nature of EID's impact on urban carbon management. Specifically, the increase in carbon intensity, the expansion of population size, and the deterioration of environmental quality will reduce the carbon unlocking effect, which may not be conducive to the achievement of the goal of "carbon neutrality" in cities. The spatial effect test found that EID and carbon locking risk are both spatially significant positive autocorrelation, the deepening of local EID can significantly cut the risk of carbon locking in neighboring cities, and the urbanization pathway and energy saving pathway exist both locally and in neighboring places.

Ecological Vulnerability Assessment and Spatiotemporal Characteristics Analysis of Urban Green-Space Systems in Beijing–Tianjin–Hebei Region

The evaluation and analysis of the ecological vulnerability of urban green-space systems are conducive to the sustainable development of urban green-space systems. Taking the urban green-space system in the Beijing–Tianjin–Hebei region in 2010, 2015, and 2020 as the research object, an ESSR model was first constructed, and a total of ten types of impact factors were integrated into the four dimensions of “Exposure, Sensitivity, State and Response”. The weight of the impact factors was objectively determined via spatial principal component analysis, and the ecological vulnerability of the urban green-space system was evaluated via superposition analysis; the evaluation’s results were graded. Moreover, the transfer matrix, center-of-gravity migration model, standard deviation ellipse, and spatial autocorrelation analysis were used to study the temporal and spatial variation characteristics of the evaluation results; then, the driving force of impact factors was analyzed based on a geographical detector. Finally, the rationality of the evaluation results was verified using the changing trend of the remote sensing ecological index (RSEI). The results show that the ecological vulnerability of the urban green-space system in the Beijing–Tianjin–Hebei region has decreased year by year for the past ten years. The distribution law of “Hebei surrounds Beijing and Tianjin” is presented in this space, and 2015 is the mutation node. In 2010, the moderately and severely vulnerable areas of Hebei surrounded the mildly vulnerability areas of Beijing and Tianjin. Moreover, in 2015 and 2020, the mildly vulnerable areas of Hebei surrounded the severely vulnerable areas of Beijing and Tianjin. Vulnerability expands slightly in the east–west direction and shrinks slightly in the north–south direction, and the center of gravity shifts towards Beijing year by year along the northeast direction. Moreover, the spatial distribution of vulnerability shows significant positive spatial autocorrelations and exhibits very obvious agglomeration. In addition, vulnerability is the result of the combined effect of various factors, and education degree, human disturbance index, and annual average precipitation are the dominant factors. The analysis results provide a reference for the effective application and sustainable development of urban green-space ecological functions.

A landscape-based ecological hazard evaluation and characterization of influencing factors in Laos

The study of the spatiotemporal evolution of landscape ecological hazard and human and natural influences is essential for conservative management and regional sustainable development. This study applied a landscape pattern analysis method and geodetector to multi-source data for 2000, 2010, and 2020 to analyze changes in and drivers of landscape ecological hazard in Laos. The results indicated that: (1) There were more prominent changes in landscape types in Laos. Forest area decreased, whereas the areas of other landscape types increased. There was an overall steady change in the landscape patterns of Laos. Besides for significant changes in the artificial surface landscape index, landscape indices remained stable; (2) The cumulative high and extreme ecological hazard areas increased by 1,947.81 km2, whereas the cumulative areas of low and minimal ecological hazard decreased by 8,461.8 km2. Areas of low and moderate ecological hazard accounted for > 85% of the total area. Areas of low ecological hazard were mainly in the northwest and southeast. The area of high ecological hazard was concentrated in the central and northeastern regions. The distributions of different landscape ecological hazards in Laos during the study period were similar, with general patterns of decreasing hazard from north to south; (3) A positive Moran’s I of landscape ecological hazard in Laos was obtained. While the agglomeration effect was pronounced, it decreased over time, resulting in a weakening in spatial autocorrelation. A significant positive autocorrelation was observed in the spatial distribution of landscape ecological hazard in the study area. Agglomerated areas of high and low ecological hazard were mainly concentrated in the northeast and southeast, respectively; (4) The spatiotemporal evolution of landscape ecological hazard in Laos over the last 20 years could be attributed to interactions between natural and anthropogenic influences. Natural influences were a significant driver of changes to landscape ecological hazard in Laos, with annual precipitation and average temperature being the most significant. Anthropogenic influences, including socioeconomic factors and regional accessibility, significantly impacted local ecological deterioration in Laos.

Performance and sustainability evaluation of rural digitalization and its driving mechanism: evidence from Hunan province of China

Quantitatively measuring rural digitalization performance and development sustainability, identifying their key influencing factors and figuring out their driving mechanisms are of great value to policy design for rural revitalization and management. This paper analyzed the sustainable development degree, spatial patterns, and influencing factors of rural digitization in Hunan Province, China, based on a combination of PSR, TOPSIS, ESDA, GWR and GeoDetector, in an attempt to provide a basis for the planning and policy design of rural management. The sustainability and construction performance of rural digitalization in Hunan were characterized by significant spatial inequality and positive autocorrelation, with coefficients of variation of 0.33 and 0.24, and Moran’s I values of 0.29 and 0.34, respectively. The rural digitalization in Hunan showed significant non-equilibrium across different dimensions and brought forward diversified combination patterns, including single dimensional leadership, dual dimensional leadership, three-dimensional leadership, and all-round development. The pattern dual dimensional leadership, especially PS (pressure + state), was dominant in the sustainability of rural digitalization, compared to the pattern single dimensional leadership dominant in the construction performance, especially I (rural infra-structure digitalization), IL (rural infrastructure + life digitalization), IG (rural infrastructure + governance digitalization). The sustainability and construction performance of rural digitalization in Hunan were subject to a complex driving mechanism, with different factors differing significantly in their action nature, force, spatial effects and interactions. Notably, economic development (gross domestic product) is a positive key factor, while government intervention capacity (fiscal self-sufficiency rate) is an important factor, and natural environment (relief amplitude) is a mixed auxiliary factor (both positive and negative). Factor interactions were mainly characterized by nonlinear enhancement and a large number of super factor pairs. Therefore, the policy design should take into account both localized and differentiated management; and also emphasize enhanced cooperation with adjacent counties and synergistic management. It is suggested to divide Hunan into four planning zonings of leading, potential, warning and general zone, and design the spatial policies for each of them according to the driving mechanism, so as to develop a more reasonable and practical combination of development projects and management policies.

Spatial Characteristics and Obstacle Factors of Cultivated Land Quality in an Intensive Agricultural Region of the North China Plain

Cultivated land quality (CLQ) is at the core of the trinity protection of cultivated land in China. Scientific evaluation of CLQ and identification of its obstacle factors are the foundation for the construction and improvement of the quality of cultivated land. The main objective of this study was to evaluate CLQ and identify its obstacle factors, and Quzhou County, an intensive agricultural region in the North China Plain (NCP), was selected as a case study. The evaluation index system of CLQ was constructed based on five dimensions, including climate condition, topographic characteristic, soil property, farming status, and environmental condition, by analyzing the logical evolution of elements, processes, functions, and quality of cultivated land. A methodological system based on the Weighted Summation Method (WSM) and the “1 + X” model was developed to evaluate the CLQ. Then, the obstacle diagnosis model constructed based on the Cask Law and relevant academic studies was used to identify the obstacle factors of CLQ. The results showed that the proportion of high-, medium-, and low-quality cultivated land in Quzhou County was 36.19%, 33.60%, and 30.21%, respectively, and the average grade of CLQ was 2.97, which was considered to be at a medium level. Moran’s I of global spatial autocorrelation in Quzhou County was 0.8782, indicating a significant positive autocorrelation of the cultivated land quality index (CLQI). The main obstacle factors of CLQ in Quzhou County were soil profile constitution, irrigation guarantee rate, groundwater depth, and soil microbial biomass carbon. Therefore, based on the stable and dynamic characteristics of the obstacle factors, suggestions were provided to improve the quality of cultivated land in terms of strengthening the consolidation of cultivated land, transforming the concept of agricultural fertilization, and carrying out cultivated land recuperation. This study provides a new perspective on the cognition, evaluation, and identification of obstacle factors of CLQ, and the findings of this study can provide a reference for the consolidation and improvement of CLQ in the NCP.

Research on the Development Level, Spatiotemporal Evolution Characteristics, and Sustainable Development Path of the Digital Business Environment

This paper aims to measure the development level and dynamic evolutionary characteristics of the digital business environment, and explore its sustainable development strategies, which would contribute to the sustainable development of the digital economy. This study employs ecosystem theory to construct evaluation indicators for the digital business environment. Utilizing panel data from 272 cities in China between 2011 and 2020, the study applies the entropy weight method to measure the development level of China’s digital business environment. It further examines the spatiotemporal evolution characteristics and sustainable development strategy of the digital business environment using methods such as kernel density estimation, Markov chain analysis, Moran’s I index analysis, Dagum’s Gini coefficient calculation and decomposition, and fsQCA analysis. The results indicate that the digital business environment steadily increased during the study period. Furthermore, there has been a gradual acceleration in the upward trend since 2017, with a higher probability of transition from cities with lower levels of the digital business environment to neighboring cities with higher levels. Cities with higher development levels are experiencing an increasing gap compared to the average level. There is a significant spatial positive autocorrelation in the efficiency of the digital business environment among Chinese cities, exhibiting a distribution pattern of “high-high” and “low-low” in spatial terms. Dagum’s Gini coefficient reveals that the regional differences serve as the primary source of spatial distribution disparities in the digital business environment, and there are four configuration paths to promote a sustainable digital business environment. Moreover, our findings have implications for policy makers, such as increasing investment in digital infrastructure, leveraging the spatial spillover effects of regions with a high level of digital business environment development, and enhancing the diffusion mechanism for optimizing the digital business environment.

The effects of socioeconomic factors on particulate matter concentration in China's: New evidence from spatial econometric model

As a result of rapid industrialization and urbanization, China is now facing a host of environmental problems that have serious health implications. Studies of air pollution's impact on human health are vital in many fields, including epidemiology, environmental science, and the social sciences. To ensure the effective growth of socioeconomic sectors, it is critical to investigate the effect of socioeconomic factors on primary air pollutant particulate matter (PM2.5) and the driving mechanism. We conducted group-wise (i.,e. divide data in 5 different periods, D1 (2002–2006), D2 (2007–2011), D3 (2012–2016), D4 (2017–2021) and D5(2002–2021) spatial autocorrelation and spatial panel regression analyses of PM2.5 emissions using panel data from 34 provincial-level administrative units in China from 2002 to 2021 to understand the factors influencing air pollutant emissions. This study adds to the literature by considering comprehensive features and spatial effects in the panel-data econometric framework of the different areas. The spatial features analysis reveals that pollutant emissions in these regions decreased during the study period, although socioeconomic and natural factors are essential sources of PM2.5. PM2.5 emissions also showed significant positive spatial autocorrelations. Several statistical tests were run to examine the spatial autocorrelation among the regions. The results of a random effect regression model and geometric weighted regression (GWR) revealed that both socioeconomic and natural factors were statistically significant for PM2.5, though to varying degrees depending on region type. Positive and statistically significant results were obtained for China when considering the impacts of urban population, urban green space, economic growth, and economic spending. China has a positive and significant link with the exploitation of energy and natural resources. In light of these findings, we have developed several ideas for addressing air pollution and improving environmental sustainability, such as increasing regional collaboration and reforming the economy.

Geographic Information System Based Economic And Financial Risk Analysis: The Case Of Europe And Central Asia

The study has two purposes. The first purpose is, using the economic and financial risk factors determined by the International Country Risk Guide (ICRG) rating agency, to reclassify the scores that countries get from these factors with the Jenks Natural Breaks (JNB) classification technique and to compare countries by creating their thematic maps according to this classification. The second purpose of our study is to create economic and financial risk maps of countries by using the Geographical Weighted Regression (GWR) method, which is one of the Geographical Information System (GIS) analysis techniques, based on the economic and financial risk factors determined by the ICRG. Before the GWR analysis for the variables included in the research, Moran Index analysis was performed as the main measurement of spatial autocorrelation. As a result of the Moran analysis, it was determined that there was a statistically significant positive autocorrelation between the countries. In other words, it has been seen that the countries examined have spatial dependence on each other in terms of economic and financial risk. According to the results of the GWR analysis, risk maps of the examined countries were created and more dynamic, more meaningful or more sensitive, more specific and visually easier to understand results were revealed. And according to these results, it has been seen that the GWR technique can also be used in the fields of economy and finance. In addition, the study brought a different interdisciplinary perspective by bringing together the fields of economy, finance and geography.

Evaluation and spatial-temporal evolution of ecosystem service value of cascade hydropower project reservoir area in the Jinsha River, China

The Jinsha River basin, full of hydropower resources, is the largest hydropower energy base in China. From 2005 to 2018, four giant cascade hydropower stations (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) were built along the Jinsha River. The reservoir area of four hydropower stations involves 26 counties (districts). The ecological environment of the reservoir area has a close relationship with hydropower projects, and ecosystem service value is an important standard to measure the quality of the ecological environment. Taking the reservoir area formed by four cascade hydropower stations in Jinsha River (Jinsha River Reservoir Area, JRRA) as the research object, the essay analyzed the spatial and temporal pattern of ecosystem service value in the reservoir area in 2005, 2010, 2015, and 2018. The results showed that (1) The ecosystem service value of JRRA reached 94 billion yuan in 2018, and the forestland took the largest proportion of ecosystem service value, accounting for 46.93%, followed by grassland, water area, cropland, and unused land. (2) From 2005 to 2018, the total ecosystem service value in JRRA increased by 3.374 billion yuan, and the spatial pattern of ecosystem service value showed a spatial distribution characteristic of high in the middle and low on both sides, and the spatial distribution had significant positive autocorrelation. (3) Because the area of water increased a lot, the ecosystem service value of JRRA showed a trend of overall increase which mainly occurred in the 3-km buffer zone along the river. The results further proved that the implementation of hydropower projects could improve the ecosystem service function in the reservoir area and provide technical support for the sustainable utilization of land resources and ecological compensation in the reservoir area.

Spatiotemporal Changes of Cultivated Land System Health Based on PSR-VOR Model-A Case Study of the Two Lake Plains, China.

Cultivated land resources are the material basis of sustainable agricultural development. Climate change, food security, land pollution, and other issues highlight the value of sustainable agricultural development, and the health of the cultivated land system has attracted much attention. By constructing "PSR-VOR" cultivated land system health evaluation framework under the 5 km grid scale and using GIS spatial analysis and mathematical statistics to comprehensively evaluate the health status of the cultivated land system in the two lake plains from 2000 to 2019. The major results have shown that: (1) Over the past 20 years, both the highest and average values of the health index of the cultivated land system have gone down, and the health status of the cultivated land system has changed and gotten worse over time. (2) The health status in the two lake plains has been generally good, mainly in Class I and Class II areas. However, the area of cultivated land with general and poor health status has increased rapidly. On the whole, the health level presents the characteristic of gradually decreasing from the northeast to the southwest and southeast. (3) During the study period, the global Moran's I value of the cultivated land system health index in the two lake plains increased from 0.686 to 0.729, with significant spatial positive autocorrelation, and the spatial heterogeneity of the cultivated land system health index gradually increased. As shown by the spatial distribution characteristics of high in the north, low in the south, and decreasing from the middle to the outside, the distribution of the high-value cluster area and the low-value cluster area of the cultivated land system health index in the two lake plains has not changed significantly over the past 20 years. (4) The two lake plains are divided into five areas: a moderate optimization area, a collaborative optimization area, a potential improvement area, a key improvement area, and a priority improvement area. The urgency of regulating the health status from the moderate optimization area to the priority improvement area has gradually increased, and the differentiated utilization and management of cultivated land resources need to be carried out according to local conditions.

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018.

High concentration of surface ozone (O3) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O3 and assess O3 exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O3 concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O3 by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O3 shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O3 (above 160μg/m3) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O3 increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O3 exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O3 pollution, so we need to strengthen their relevant health protection of them.

Impacts of green finance on green innovation: A spatial and nonlinear perspective

This paper provides an in-depth understanding of the mechanisms of green finance on green innovation, focusing on their spatial and non-linear relationships. Using panel data of 30 provinces of China during the period of 2009–2017, a green finance index is constructed, and the spatial Durbin model and panel threshold model are used. The results reveal that: (1) Green finance and green innovation had significant positive autocorrelation , and the development of green finance in provinces of central and western China were still in the lower-level range. (2) The coefficients of the direct effect and indirect effect of green finance on green innovation were significantly positive, which indicated that green finance was conducive to enhancing green innovation in local and adjacent provinces during the research period. (3) Double threshold effects of green finance on green innovation were observed, which demonstrated that the driving effect of green finance was decreased with the raising of intensity of environmental regulation. The implication of these results is that government should make full use of the radiation and trickle-down functions of green finance and green innovation to narrow regional gaps in sustainable development while maintaining a moderate intensity of environmental regulation. • A green finance index of 30 provinces of China during the period of 2009 to 2017 was constructed on the basis of green credit, green securities, green insurance, and green investment. • The heterogeneity of region and time for green finance were analyzed employing the spatial exploratory analysis, which manifested that the spatial evolution of green finance in China presents a gradient decreasing pattern from east to west. • The impact of green finance on green innovation was explored by the spatial Durbin model, which proved that the direct effect and the spatial spillover effect were significantly positive. • Moderate intensity of environmental regulation helps green finance promote green innovation and this value should not exceed a maximum of 0.935.

Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis

Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H–H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were −0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.

Assessing impact of land use change on ecosystem service value in Dasi River Basin of China based on an improved evaluation model.

The aim of this study was to provide a new method for dynamic and continuous assessment of ecosystem service value (ESV) and reveal the impact of land use change on ESV in Dasi River Basin within Jinan's startup area from replacing old growth drivers with new ones. Based on four remote sensing images from 2002 to 2020, four ecological indicators were extracted, and the ecological environmental quality index (EEQI) was obtained through the approach of principal component analysis (PCA). Then, the traditional ESV evaluation method was modified by using the EEQI, grain yield, the biomass factor of cropland ecosystem, and the consumer price index (CPI). Finally, the impact of land use change on ESV was further analyzed based on the improved evaluation model. The result showed that (1) during 2002-2020, the area of forestland, grassland, and built-up land showed an increasing trend. The area of cropland and bare land showed a decreasing trend, and the water body area showed a slightly decreasing trend. (2) The total ESVS overall increased by 2.1759 × 107 yuan; the increased ESVS from air quality regulation, maintain biodiversity, and climate regulation were the main reasons for the increased of total ESVS, with contribution rates of 53.18%, 12.46%, and 11.29% respectively. (3) The sensitivity of ecosystem services to land use change showed a decreasing trend, and the order of elasticity index of different land use types was cropland > water body > forestland > grassland > bare land. The conversion of cropland and bare land to forestland was the main type of ESVs increase, with contribution rates of 18.35% and 10.13%, respectively. The cropland reclamation and built-up land expansion were the most significant land use changes that lead to the decline of ESVS, with contribution rates of 20.14% and 19.03% respectively. (4) The ESV showed a significant positive auto-correlation in terms of spatial distribution. The area of high-high region was mainly distributed in water body, forestland, and its surrounding areas. The area of low-low region was mainly distributed in built-up land and wasteland areas where human disturbance is relatively serious. The high-low and low-high regions were affected by landscape transition process and randomly distributed around the low-low and high-high regions, respectively. This study cannot only put forward a new method for the dynamic continuous evaluation of ESV, but also provide a reference for the rational allocation of land resources in the startup area to realize the balanced development of regional environment and economy.

Spatial effects and influencing factors of urban land intensive use in the Yangtze River Delta under high-quality development

To integrate the concept of high-quality development into urban land use, this paper explores the spatial-temporal pattern characteristics of urban land intensive use (ULIU) in the Yangtze River Delta with a constructed evaluation index system, the comprehensive evaluation model and exploratory spatial data analysis based on the spatial panel data of 41 cities from 2010 to 2020. The paper also analyzes the spatial effects and influencing factors of ULIU with the spatial Durbin model and partial differential method. The results demonstrate that: 1) The ULIU in the Yangtze River Delta generally showed consistent growth from 2010 to 2020. However, there was significant spatial differentiation among different regions, as the land intensive use level was higher in cities along the Yangtze River and coastal cities around Hangzhou Bay, while relatively lower in the mountainous and hilly cities in Zhejiang and southwestern Anhui. 2) The ULIU level showed significant positive global spatial autocorrelations, while its local spatial characteristics mainly included four types dominated by high-high agglomeration and low-low agglomeration from 2010 to 2020. 3) Economic development, industrial structure and infrastructure could remarkably promote the ULIU of both local and neighboring cities. Scientific and technological innovation could significantly enhance the ULIU of local cities; population urbanization and government financial pressure showed significant negative effects on the ULIU of local cities; while all the above three factors had insignificant effects on the ULIU of neighboring cities. These findings are of great significance for revealing the internal mechanism underlying the spatial effect of land intensive use and provide some guidance for improving the level of ULIU in the Yangtze River Delta from the perspective of regional cooperation.

Spatial and Spatio-Temporal Distribution of Human Respiratory Syncytial Virus, Human Parainfluenza Virus, and Human Adenoviruses Cases in Kenya 2007-2013.

Human Respiratory Syncytial Virus (HRSV), Human Parainfluenza Virus (HPIV), and Human Adenovirus (HAdV) epidemics differ in geographical location, time, and virus type. Regions prone to infections can be identified using geographic information systems (GIS) and available methods for detecting spatial and time clusters. We sought to find statistically significant spatial and time clusters of HRSV, HPIV, and HAdV cases in different parts of Kenya. To analyse retrospective data, we used a geographical information system (GIS) and the spatial scan statistic. The information was gathered from surveillance sites and aggregated at the county level in order to identify purely spatial and Spatio-temporal clusters. To detect the presence of spatial autocorrelation, the local Moran's I test was used. To detect the spatial clusters of HRSV, HPIV, and HAdV cases, we performed the purely spatial scan statistic. Furthermore, space-time clusters were identified using space-time scan statistics. Both spatial and space-time analyses were based on the discrete Poisson model with a pre-specified statistical significance levelof p<0.05. The findings showed that HRSV, HPIV, and HAdV cases had significant autocorrelation within the study areas. Furthermore, in the Western region of the country, the three respiratory viruses had local clusters with significant positive autocorrelation (p<0.05). Statistically, the Western region had significant spatial clusters of HRSV, HPIV, and HAdV occurrence. Furthermore, the space-time analysis revealed that the HPIV primary cluster persisted in the Western region from 2007 to 2013. However, primary clusters of HRSV and HAdV were observed in the Coastal region in 2009-11 and 2008-09, respectively. Human respiratory syncytial virus (HRSV), human parainfluenza virus (HPIV), and human adenovirus (HAdV) hotspots (clusters) occurred in Kenya's Western and Coastal regions from 2007 to 2013. The Western region appeared to be more prone to the occurrence of allthree respiratory viruses throughout the study period. Strategic mitigation should focus on these locations to prevent future clusters of HRSV, HPIV, and HAdV infections that could lead to epidemics.