Significant Spatial Correlation Research Articles

Impact and Mechanism of Digital Inclusive Finance on the Urban–Rural Income Gap of China from a Spatial Econometric Perspective

With the rapid development of digital inclusive finance, whether it can lower the income inequality between urban and rural areas has been the focus of policy makers and researchers. Based on China’s 2011–2018 provincial panel data, this paper employs spatial econometric models and mediating-effect models to examine the impact of digital inclusive finance on the urban–rural income gap and its mechanism. The main findings are as follows: First, globally, the urban–rural income gap and digital inclusive finance each exhibit significant positive spatial correlation; locally, the income gap primarily exhibits spatial dependence in Central and Western China. Meanwhile, digital inclusive finance transitions from varied agglomeration patterns to exclusively H-H patterns in Eastern China. Second, digital inclusive finance notably reduces the urban–rural income gap, primarily attributed to its expanded breadth of coverage and digitization. Third, mechanism analysis indicates that digital inclusive finance can narrow the urban–rural income gap by increasing labor employment, resulting in higher farmer incomes, rather than through individual entrepreneurship and human capital investment. The findings of this study are crucial for improving digital inclusive financial development and adjusting urban–rural income distribution.

Research on Real Estate Batch Evaluation Based on Spatial Econometric Model

This article studies the real estate batch evaluation model in Yuzhong District, Chongqing City. By analyzing 350 real second-hand housing listing data in October 2023 as sample data, a feature price model is established and a spatial econometric model is constructed based on the regression results to seek the most suitable model for batch evaluation of second-hand housing in Yuzhong District, Chongqing City. The results show that there is a significant spatial correlation between sample data, and both the spatial error model and spatial lag model in the spatial econometric model have improved the evaluation results of the traditional feature price model. However, the spatial lag model has the best performance in all indicators. Applying the spatial econometric model to real estate batch evaluation can improve the accuracy and fairness of the evaluation results.

Spatial patterns and driving forces of urban vegetation greenness in China: A case study comprising 289 cities

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Correlations between ecosystem service value and landscape ecological risk and its spatial heterogeneity in Jilin Province, China.

Exploring the correlations between ecosystem service value (ESV) and landscape ecological risk and the driving factors of their spatial variations is crucial for maintaining regional ecological security and promoting sustainable human well-being. We carried out a grid resampling size of 5 km×5 km assessment units of Jilin Pro-vince based on the remote sensing monitoring data of land use in 2000, 2005, 2010, 2015, and 2020. We quantitatively evaluated the landscape ecological risk and ESV, and analyzed their spatial-temporal variations. Employing bivariate spatial autocorrelation analysis and the geographical detector models, we examined the correlation between the landscape ecological risk and ESV and explored the driving factors for their spatial variations. The results showed that ESV in Jilin Province decreased from 385.895 billion yuan to 378.211 billion yuan during 2000-2020. The eastern region was dominated by extremely low risk, medium risk, and low risk areas. In contrast, the western region was mainly composed of extremely high risk and high risk areas. There was a significant negative correlation and spatial negative correlation between landscape ecological risk and ESV in Jilin Province. Human activity and land use type were the important driving factors for spatial differentiation in both landscape ecological risk and ESV. Our findings suggested that scientific land use regulation and appropriate control of human activities are critically needed to optimize Jilin Province's ecological environment.

Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data

Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms.

Spatial differences, distribution dynamics and driving factors of the synergy between marine ecological security and high-quality development in three major marine economic circles of China

IntroductionIn China, the sustainable development of the marine is facing a prominent contradiction between ecological security and economic development, as well as unbalanced and non-coordinated regional development. Exploring the regional differences in the synergy between marine ecological security (MES) and high-quality development of the marine economy (MHQD) and its driving mechanism is the key to optimizing the spatial distribution of marine development, promoting the synergistic and balanced development of the regional marine composite system during the “14th Five-Year Plan” period.MethodsIn this article, we employed the Dagum Gini coefficient decomposition model and Kernel density estimation methods to reveal the evolution of regional differences, sources, and distributional dynamics of synergistic development of the composite system from 2009 to 2020. Meanwhile, spatial econometric analyses were applied to identify the influencing factors and spatial spillover effects.ResultsThe level of synergistic development of composite system has a fluctuating upward trend, with a significant positive spatial correlation and a significant “polarization effect”. Inter-regional differences in the synergistic development of the composite system are the main source of the overall differences. Factors such as innovation-driven, human capital, and opening up to the outside world are important driving factors for the synergistic development of the composite system.DiscussionThe MES-MHQD composite system exhibits a low level of synergistic development, with significant differences in the level of synergy both among the 3MMECs and within each circle. We infer that the expansion trend of overall difference will constrain the exploration of a sustainable development path for China’s coastal regions in the future. It is necessary to optimize the spatial layout of the composite system, establish robust mechanisms for regional cooperation and communication, accurately identify the development bottlenecks of the regional composite systems. This study provides a theoretical basis and decision-making reference for the promotion of the synergistic development of the composite system of the Three Major Marine Economic Circles (3MMECs) in China.

Spatiotemporal evolutionary characteristics and driving forces of carbon emissions in three Chinese urban agglomerations

Assessing carbon emissions in urban agglomerations has emerged as a pivotal step towards achieving carbon neutrality. In this study, panel data from 75 cities within the three largest urban agglomerations in China—the Yangtze River Delta (YRD), Beijing-Tianjin-Hebei (BTH) region, and Pearl River Delta (PRD)—were constructed to analyse the spatiotemporal evolution of carbon emissions from 2005 to 2020. An indicator system was established, and the Spatial Durbin Model (SDM) was used to investigate the driving forces of carbon emissions. The findings revealed a gradual increase in carbon emissions within these agglomerations, with a notable shift in their spatial distribution from metropolitan areas to broader urban agglomerations, exhibiting significant spatial correlation. Additionally, variations in carbon emissions were observed across different development stages. The SDM indicated that several factors, such as economic conditions, industrialization levels, and population size, influenced the carbon emissions of urban agglomerations. Nevertheless, the impact of these driving factors on carbon emissions was not constant and could be altered by other elements, including green technologies and governmental policies, indicating a decoupling effect between economic growth and carbon emissions. The insights from this study can assist policymakers in formulating strategies to curb carbon emissions and foster sustainable development.

Research on the coupling mechanism and influencing factors of digital economy and green technology innovation in Chinese urban agglomerations

This paper examines the coupling coordination degree between digital economy and green technology innovation in 19 urban agglomerations across China from 2011 to 2020. Through the analysis of the coupling coordination degree model, spatial autocorrelation, multi-distance spatial clustering analysis, kernel density analysis and grey correlation model, this study uncovers the mechanism of coupling between digital economy and green technology in Chinese urban agglomerations. Data analysis revealed a significant increase in the coupling coordination between the digital economy and green technology innovation within urban agglomerations. However, there are noticeable spatial imbalances in this trend. Additionally, the multi-distance spatial distance analysis highlights a shift from a random distribution to a clustered distribution of spatial characteristics. The polarization features vary among each urban agglomeration and exhibit a significant positive spatial correlation. Factors such as economic sustainability, creative talent, policy support, digital impetus, and technological support will affect the coupling mechanism of green technology innovation and the digital economy in China's urban agglomerations. Policy recommendations are proposed to foster the development of the digital economy, promote coordinated growth within and beyond urban clusters, and ultimately build a digital ecological civilization that is both green and intelligent.

Study on the spatiotemporal evolution of coupled and coordinated digital economic resilience and efficiency

The coordinated development of resilience and efficiency in the digital economy guarantees a sustainable and efficient growth model. We used panel data from 30 provinces and regions in China from 2013 to 2021 to evaluate the resilience and efficiency levels of the digital economy. We calculated their coordinated development levels using kernel density estimation, spatial autocorrelation, and Markov chain analysis to explore the spatiotemporal evolution characteristics of their coordinated development. The results indicate that (1) the resilience level of the digital economy is on the rise, while efficiency fluctuations are decreasing. Resilience exhibits an “east high–west low” spatial pattern with the “Hu Huanyong Line” as a boundary, while efficiency shows a “collapse in the central and northern regions” trend. (2) The overall level of coordinated development between resilience and efficiency has decreased; however, the differences in coordination between different regions have reduced, gradually forming a spatial pattern of “high in the east, low in the west, low in the south, and high in the north.” (3) A significant positive spatial correlation exists between the development levels of digital economic resilience and efficiency, which continues to strengthen over time. This finding reveals a spatial pattern of high-level concentration centered around Beijing–Tianjin, Jiangsu–Zhejiang–Shanghai, and low-level concentration centered around the central and western regions. (4) Coordinated development shows a “club convergence” effect, indicating stability in maintaining the existing state.

Spatial coupling coordination and interactive response between green finance and green total factor productivity: geographical analysis based on Chinese provinces, 2010-2020.

Based on the panel data of 30 provinces in China from 2010 to 2020, this study uses the coupling coordination degree model (CCDM), spatial autocorrelation analysis, and the panel vector autoregression (PVAR) model to quantitatively examine the dynamic coordination and interactive response of green finance (GF) and green total factor productivity (GTFP). The results show that the coupling coordination degree (CCD) between GF and GTFP during the study period shows an overall increasing trend and significant regional differentiation and remains at the medium level, indicating large room for improvement. The CCDs of provinces fluctuate between the low and high types. The distribution gradually shifts from a "high in the south and low in the north" pattern to a "high in the east and low in the west" configuration. The spatial difference of coupling development continues to expand and presents a significant spatial positive correlation. In addition, GF and GTFP in China exhibit significant self-reinforcing characteristics, but the reinforcing effect gradually decreases. There is only a unidirectional interaction relationship between GF and GTFP, which is GF actively fosters the advancement of GTFP, while the influence of GTFP on GF appears less pronounced. This research provides comprehensive insights into the dynamic interaction between GF and GTFP.

Correlation or Causation: Unraveling the Relationship between PM2.5 Air Pollution and COVID-19 Spread Across the United States

Numerous studies have examined the potential connection between air pollution, particularly PM2.5, and the incidence of COVID-19 cases during the pandemic. While several studies have demonstrated a strong correlation, caution is advised as correlation does not imply causation. To address this concern, our two-year observational study employs a comprehensive approach that utilizes a large sample size and draws on temporal and spatial data across the United States, surpassing the limitations of previous studies restricted to specific locations. Through rigorous correlation and regression analyses, we control for potential confounding factors. Air pollution data, a crucial component of our study, has been sourced from the United States Environmental Protection Agency (EPA). Additionally, COVID-19 case data is extracted from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, providing a robust and widely recognized dataset for our analyses. Notably, a significant spatial correlation exists between COVID-19 cases and population size (r=0.98, p-value <0.01), as confirmed by multivariate regression analysis, suggesting a confounding influence of population. It is crucial to emphasize that correlation does not automatically imply a direct cause-and-effect relationship. Moreover, to minimize the impact of population, we employ rates (COVID-19 cases/population of States), demonstrating that the rate of COVID-19 cases is independent of PM2.5 and population. Additionally, the rate of COVID-19 infection is not correlated with population density, implying the population's influence on infection is more likely due to probability rather than being a direct cause. In summary, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship. In conclusion, while many studies report a correlation between air pollution and COVID-19 cases, the influence of confounding factors like population density necessitates further investigation to establish a definitive causal relationship.

The spatial and temporal situation of China's digital technology innovation and its influencing factors.

Digital technology innovation is the core driving force for the high-quality development of the digital economy, and in-depth exploration of the regional distribution pattern and formation mechanism of digital technology innovation in China is conducive to the rational layout and coordinated development of the inter-provincial digital economy. Based on the Reference Relationship Table of the Classification of Core Industries of Digital Economy and the International Patent Classification (2023), the patent authorization data of digital technology from 2012 to 2022 were obtained, and the spatiotemporal situation of China's digital technology innovation was analyzed by using ArcGIS software, Dagum's Gini coefficient, and Moran's I index, and the spatial Dubin panel model was used to explore the influencing factors of digital technology innovation. It is found that: (1) the scale and vitality of China's digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal-central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China's digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.

Spatiotemporal Evolution Characteristics and Driving Factors of Water-Energy-Food-Carbon System Vulnerability: A Case Study of the Yellow River Basin, China

With the growing influences of anthropogenic activities and climatic change, the problem concerning the vulnerability of the Water-Energy-Food-Carbon (WEFC) system in the Yellow River Basin is prominent. Using the VSD framework, the WEFC system vulnerability evaluation index system was constructed with 60 cities in the Yellow River Basin as the samples, and the WEFC system vulnerability of each city was measured from 2010 to 2019. Kernel density estimation, Theil index, and spatial correlation analysis were employed to investigate spatio-temporal evolution characteristics. Geodetector was utilized to analyze the driving factors of WEFC system vulnerability. The results demonstrate that: (1) The vulnerability of the WEFC system in the Yellow River Basin tends to decrease, with a spatial pattern of “low in the middle and high on both sides”; the vulnerability is largest in the upper and lower reaches, while smallest in the middle reaches. (2) The spatial difference in vulnerability narrows in the middle and lower reaches, while expanding in the upper reaches. The differences among the three major regions mainly originate from within the region, with the upper reaches having the largest difference and contribution; the vulnerability is featured with a significant spatial correlation, with the upper and lower reaches cities mostly displaying a “high-high” agglomeration and the middle reaches mainly showing a “low-low” one. (3) Factors, including the carbon and ecological carrying capacity coefficient, water resource development and utilization rate, and urbanization rate, mainly influence the WEFC system vulnerability; the spatial heterogeneity of core drivers at the regional scale is obvious, with the upper reaches regions being more strongly influenced by factors of the water resources system, while the middle and lower reaches regions are more sensitive to factors concerning industrial pollution of the energy subsystem. The explanatory power of carbon ecological carrying capacity reaches its peak in the middle reaches. The interaction of factors increases the strength of the impact on vulnerability. This study provides decision support and policy suggestions for achieving a balanced and coordinated development of water resource utilization, energy development, food production, and carbon cycle system in the Yellow River Basin. Investigating WEFC system vulnerability to support SDG 11 provided valuable insights and policy strategies for building cities that are inclusive, secure, resource-efficient, and resilient in the face of climate change and disaster risks.

Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China.

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Research on the estimation and spatial pattern of net tourism carbon emissions in the Yellow River Basin from 2009 to 2019.

Based on panel data and remote sensing data of cities in the Yellow River Basin in China from 2009 to 2019, and using the tourism carbon footprint and tourism carbon carrying capacity models, the tourism carbon emissions, tourism carbon carrying capacity, and net tourism carbon of 65 cities in the Yellow River Basin were calculated. The balance and dynamic changes in carbon emissions and carbon fixation of urban tourism in the past ten years were compared. The results show that (1) tourism carbon emissions in the Yellow River Basin are generally on the rise, along with a distribution characteristic of downstream > middle reaches > upstream with obvious characteristics of urban agglomeration centrality within the basin; (2) the carbon carrying capacity of tourism is higher than that of tourism. The growth of carbon emissions is relatively slow, showing a spatial distribution pattern of high in the west and low in the east, which is mainly related to the geographical environment and economic development of the city; (3) the tourism carbon emissions and tourism carbon carrying capacity in the upstream areas can basically maintain a balance, but in the middle and lower reaches of the region, they show a carbon surplus. There is a significant positive spatial correlation in urban net tourism carbon emissions, and the clusters are mainly H-H and L-L.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China.

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Características estruturais e fatores de influência da rede espacial do comércio agrícola: evidências de 15 países do RCEP

ABSTRACT: Based on national agricultural trade panel data of Regional Comprehensive Economic Partnership (RCEP) countries from 2002 to 2020, the structural characteristics of the RCEP national agricultural trade spatial network are reconstructed by the social network analysis method in this paper. Specifically, the characteristics consist of overall characteristics, individual characteristics, core-periphery structure analysis, and block models. Moreover, the influence factors on the agricultural trade network are also examined by the QAP regression model. The conclusions are summarized as follows. Firstly, the agricultural trade in RCEP countries has significant spatial correlation, showing favorable stability and accessibility. Secondly, Australia, China, Thailand, and Vietnam are the central actor’s leading position in the correlation network. While Cambodia, Laos, Myanmar, and other countries are in the marginal position. Thirdly, the network can be divided into four blocks, including the net benefit block, the two-way spillover block, the broker block, and the net spillover block. The spillover effect between the blocks is transitive. And finally, geographical distance, economic and social conditions, resource endowment, and language proximity index significantly impact the RCEP agricultural trade network structure.

Identifying driving factors of ecosystem service trade-offs in mountainous region of southwestern China across geomorphic and climatic types

Mountainous areas have obvious spatial differences in topography and climate, which could further cause heterogeneity of ecosystem service (ES) trade-offs. The characteristics of ES trade-offs had been extensively studied, however, the driving mechanism of which is still unclear in mountainous areas. Focusing on the Hengduan Mountain region (HDM) in southwest China, this study quantified the spatial aggregation effects of ESs and their trade-offs using a spatial autocorrelation analysis, and identified the dominant drives for each ES trade-off across various geomorphological and climatic zones. In terms of the spatial distribution, ESs and their trade-offs have a significant positive spatial correlation, which was mainly manifested as high-high (H-H) and low-low (L-L) value aggregations. The dominant factors for some trade-offs differed across geomorphological and climatic zones. Land use type was the main factor in flat areas for paired water yield (WY) with soil conservation (SC) and carbon storage (C). In plateau (PLA) and mid-subtropical (MS) climate zones, the main factors were land use type and soil types for paired WY and C, respectively. Temperature had a higher contribution for paired C and SC in flat and plateau areas. For the other trade-offs, the dominant factor was constant for different zones. The normalized difference vegetation index (NDVI) and soil type were the main factors for paired net primary productivity (NPP) with SC and C respectively. For trade-offs between habitat quality (HQ) and other ESs, and those among multiple ESs, land use type was the dominant drive, and the influence decreased with the increase of topographic relief. Therefore, attention should be paid to the ecological effects of land use change in future urban planning and development. The study will help guide zoning regional spatial planning and achieve mountain sustainable development.

Correlação espacial: padrão heterogêneo de mortalidade por covid-19 no Brasil

ABSTRACT This ecological study analyzed the spatial correlation between socioeconomic and demographic indicators and deaths due to COVID-19 in Brazil. The independent variables covered population, sex, age, race, literacy, and Gini index, while the dependent variable was COVID-19 mortality. Socioeconomic and demographic data were obtained from the Continuous National Household Sample Survey, and data on COVID-19 cases and deaths were obtained from the Ministry of Health website. The analysis included the calculation of univariate and bivariate Global Moran’s Indices. The results revealed a significant spatial correlation of the independent variables with COVID-19 mortality. A moderate positive correlation stands out for literate individuals aged 20 to 59 years. Therefore, there is an association between socioeconomic factors and COVID-19 mortality, with variations between Brazilian states. This conclusion highlights the need to implement intersectoral measures to ensure universal access to health and allocate resources equitably across states.

Evolution Model, Mechanism, and Performance of Urban Park Green Areas in the Grand Canal of China

Urban park green areas are part of territorial space planning, shouldering the mission of providing residents with high-quality ecological products and public space. Using a combination of several measurement models such as the BCG (Boston Consulting Group) matrix, ESDA (Exploratory Spatial Data Analysis), MLR (Machine Learning Regression), GWR (Geographically Weighted Regression), and GeoDetector, this paper presents an empirical study on the changes in Urban Park Green Areas (UPGAs) in the Grand Canal of China. By quantitatively measuring the spatio–temporal evolution patterns of UPGAs, this study reveals the driving mechanisms behind them and proposes policy recommendations for planning and management based on performance evaluation. The spatio–temporal evolution of UPGAs and their performance in China’s Grand Canal are characterized by significant spatial heterogeneity and correlation, with diversified development patterns such as HH (High-scale–High-growth), HL (High-scale–Low-growth), LH (Low-scale–High-growth), and LL (Low-scale–Low-growth) emerging. The evolution performance is dominated by positive oversupply and positive equilibrium, where undersupply coexists with oversupply. Therefore, this paper recommends the implementation of a zoning strategy in the future spatial planning of ecological green areas, urban parks, and green infrastructure. It is also recommended to design differentiated construction strategies and management policies for each zoning area, while promoting inter-city mutual cooperation in the joint preparation and implementation of integrated symbiosis planning. Furthermore, the spatio–temporal evolution of the UPGAs in the Grand Canal of China is influenced by many factors with very complex dynamic mechanisms, and there are significant differences in the nature, intensity, spatial effects, and interaction effects between different factors. Therefore, in the future management of ecological green areas, urban parks, and green infrastructure, it is necessary to interconnect policies to enhance their synergies in population, aging, industry and economy, and ecological civilization to maximize the policy performance.