Search Results (510)

The spatial configuration of cultivated land is crucial for modern agricultural production; therefore, research on cultivated land aggregation and spatial connectivity holds significant importance for enhancing agricultural production efficiency and ensuring food security. This study selected Daqing City, China, as the research area and constructed a three-level nested framework of “patch–local–regional” scales. The aggregation degree was calculated through landscape pattern indices and the MSPA model, and connectivity was evaluated using the Omniscape algorithm based on circuit theory to explore the spatiotemporal evolution patterns of cultivated land configuration and analyze their spatial correlations, proposing classified optimization strategies. The results indicate the following: (1) the spatiotemporal distribution characteristics of cultivated land aggregation in Daqing City exhibit a spatial pattern of “high in the north and south, low in the middle,” with an overall declining trend from 2000 to 2020; (2) high-connectivity areas are primarily distributed in Lindian County in the north and Zhaozhou and Zhaoyuan Counties in the south, while low-connectivity areas are concentrated in the central urban area and surrounding regions; (3) the aggregation degree and connectivity demonstrate positive spatial correlation, with the Global Moran’s index increasing from 0.358 in 2000 to 0.413 in 2020; and (4) based on the aggregation degree and connectivity characteristics, the study area can be classified into four types: scattered imbalance–isolated dysfunction, regular imbalance–connected dysfunction, scattered improvement–connected optimization, and regular improvement–connected optimization. This study provides new research perspectives for cultivated land protection. The proposed multi-scale aggregation–connectivity research method and classification system offer important reference value for the efficient utilization and management optimization of cultivated land. Full article

County-level administrative areas serve as fundamental components in China’s territorial spatial governance, and the precision and consistency of their carbon emission reduction policies are directly linked to the efficacy of the “dual-carbon” strategy’s execution. However, the spatiotemporal evolution characteristics, future trends, and driving factors of carbon emissions from territorial spatial function (TSF) utilisation at the county level remain unclear, posing a fundamental theoretical issue that local governments urgently need to address when formulating carbon reduction policies. This study developed a framework to simulate the spatial distribution of carbon emissions resulting from land use at the county level. It simulated the carbon emissions in Qionglai City from 2009 to 2023, analysed the spatial-temporal evolution characteristics and future trends using global Moran’s I, the Getis-Ord $G_i^{*}$ index, and the Hurst index, and employed the Geographically and Temporally Weighted Regression (GTWR) model for analysis. The findings indicated the following: (1) From 2009 to 2023, the city’s total carbon emissions increased from 852,300 tonnes to 1,422,500 tonnes, showing a significant phased trend. Among these, rural production spaces (RPSs) were the primary carbon sources, accounting for over 70% of annual carbon emissions each year. (2) County carbon emissions exhibit a pronounced positive geographical correlation and aggregation distribution, characterised by notable regional heterogeneity. (3) From 2009 to 2023, the city’s regional carbon emissions rose dramatically by 65.69%, while 29.66% of the areas experienced negligible increases; 99% of the regions are projected to maintain the historical growth trend, but this continuity exhibits spatial and temporal variations. (4) Economic growth, industrial structure, and development intensity are the core driving factors of carbon emissions at the county level, with spatial variations in their impact. The research findings not only provide a basis for Qionglai City, China, to formulate precise and sustainable carbon reduction policies (such as developing differentiated carbon emission control measures based on the spatiotemporal heterogeneity of carbon emissions and their driving factors), but also offer insights for similar regions worldwide in controlling carbon emissions and addressing global climate change (for example, by optimizing land spatial function utilisation, reducing carbon sources, and maximizing carbon sink capacity). Full article

In the context of global climate change, achieving a green and low-carbon economic transition is essential for sustainable development. This study constructs a model using data from 30 provinces collected between 2006 and 2020 to investigate how green finance influences China’s carbon productivity and the transmission mechanism mediated by factor-biased technological progress. The findings reveal the following: (1) The Moran’s index test for carbon productivity across Chinese provinces demonstrates significant spatial clustering. (2) Green finance exhibits substantial spillover effects on carbon productivity in surrounding regions. (3) Capital-biased and energy-biased technological progress significantly mediate the relationship between green finance and carbon productivity, indicating that green finance enhances carbon productivity by optimizing the allocation of capital, labor, and energy factors. (4) Regional heterogeneity analysis indicates that capital-technology-biased and energy-factor-technology-biased approaches can significantly enhance carbon productivity in Central and Northeastern China. Notably, energy-factor innovation delivers far greater environmental efficiency gains in these regions than in Eastern and Western China. Full article

This study evaluated ten drought indices focusing on their ability to monitor drought events in Marathwada, a semi-arid region of India. High-resolution gridded monthly total precipitation data for 75 years (1950–2024) from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to evaluate the drought indices. These indices were computed across six timescales: 1, 3, 4, 6, 9, and 12 months. A Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was employed to detect temporal volatility in precipitation, followed by a second-order geospatial autocorrelation eigenfunction eigendecomposition using Global Moran’s Index statistics to geolocate both aggregated and non-aggregated precipitation locations. The performance of drought indices was assessed using non-parametric Spearman’s correlation to identify the strength, direction, and similarity of regional-specific drought events. The temporal lag interdependence between meteorological and agricultural droughts was assessed using a non-parametric Spearman’s cross correlation function (SCCF). The findings revealed that the GARCH model with a skewed Student’s t distribution effectively captured conditional temporal volatility and asymptotic behavior in the precipitation series. The model’s sensitivity enabled the incorporation of temporal fluctuations related to droughts and extreme meteorological events. The Bhalme and Mooley Drought Index (BMDI-6) and Z-Score Index (ZSI-6) were the most applicable indices for drought monitoring. Spearman’s cross-correlation analysis revealed that meteorological droughts influenced agricultural droughts with a time lag of up to 4 months. Full article

Non-grain conversion threatens food security in karst mountainous regions where fragmented terrain and shallow soils create unique agricultural challenges. This study examines Yunnan Province (28% karst coverage) in the Yunnan-Guizhou Plateau, where cultivated land faces distinct pressures from limited soil depth (average < 30 cm in karst areas) and poor water retention capacity. Using multi-source data (2001–2021) and an integrated Dynamic Spatial-Temporal Clustering Model (DSTCM), we quantify non-grain conversion through a clearly defined Non-Grain Conversion Index (NGCI = 0.35 × CPI + 0.25 × LUI + 0.20 × RSI + 0.20 × PSI). Results reveal the NGCI declined from 45.91 to 21.05, indicating a 54% intensification in conversion (lower values = higher conversion intensity). Spatial analysis shows significant clustering (Moran’s I = 0.57, p < 0.001), with karst areas experiencing 23% higher conversion rates than non-karst regions. Key drivers include soil fertility limitations (t = 2.35, p = 0.027), crop type transitions (t = 3.12, p = 0.047), and economic pressures (t = 2.88, p = 0.012). Model predictions (accuracy: 92.51% ± 2.3%) forecast continued intensification with NGCI reaching 9.31 by 2035 under current policies. Spatial distribution mapping reveals concentrated conversion hotspots in southeastern karst regions, with 73% of high-intensity conversion occurring in areas with >30% karst coverage. This research provides critical insights for managing cultivated land in karst landscapes facing unique geological constraints. Full article

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments—geography, ambient population, and street networks—influence criminal through spatial dependence across multiple scales. Despite growing interest in the spatial distribution of crime, few studies have explicitly explored the spatiotemporal dimensions of VAW in Monterrey. This study explores spatio-temporal patterns of VAW in Monterrey, Mexico, based on the analysis of 27,036 georeferenced and verified emergency reports from the 911 system (2019–2022). The study applies kernel density estimation (KDE), the Getis–Ord G_i* statistics, the Local Moran I index, and space–time cube analysis to identify spatial and temporal clusters of VAW. The results show concentrations of incidents during nighttime and weekends, particularly in northern and eastern sectors in Monterrey. The analysis reveals clusters in areas of high socioeconomic vulnerability. VAW in Monterrey follows predictable and cyclical patterns. These insights contribute to the design of tailored public policies and actions to improve women’s health, well-being, and safety in critical zones and timeframes. The findings also enhance international understanding of gender-based spatial violence patterns in the rapidly urbanizing contexts of the Global South. Full article

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience. Full article

As the spatial carrier, the high-quality development of land complements the high-quality development of the economy and society. Imbalanced land use severely restricts regional high-quality development. This study uses panel data from 110 cities at or above the prefecture level in the Yangtze River Economic Belt (YREB) from 2013 to 2022. Based on a conjugate perspective, it comprehensively considers quantitative balance and efficiency coordination to calculate the spatial equilibrium degree of land use. Kernel density estimation and Moran’s I index are employed to reveal the spatiotemporal differentiation characteristics. This study divides land-use spatial equilibrium into different types and proposes differentiated development paths. The findings are as follows: ① In terms of temporal evolution, the spatial equilibrium degree of land use in the YREB exhibits a nonlinear progression, overall trending towards stable convergence. ② In terms of spatial evolution, provincial capital cities and municipalities directly under the central government drive the development of surrounding cities, forming three major urban clusters in the upper, middle, and lower reaches. ③ The spatial clustering characteristics of land-use equilibrium in the YREB are significant, but the degree of agglomeration is continuously weakening. ④ The optimization paths for different types of land-use spatial equilibrium show significant differences, requiring differentiated governance. These findings provide a scientific foundation for optimizing the national spatial pattern of land use, advancing regional balanced development and achieving high-quality development. Full article

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the unique conflict between the preservation of well-known historical and cultural cities and the development of greening. It does this by integrating streetscape big data (2925 sampling points) and point of interest (POI) density data (57,266 records) and using the DeepLab-ResNeSt269 semantic segmentation model in conjunction with spatial statistical techniques (Moran’s Index, Locational Entropy and Theil Index Decomposition) to quantitatively analyze the spatial equity of the green view index (GVI) in Kaifeng City. The results of the study show that (1) The Theil Index reveals that the primary contradiction in Kaifeng City’s distribution pattern—low GVI in the center and high in the periphery—is the micro-street scale difference, suggesting that the spatial imbalance of the GVI is primarily reflected at the micro level rather than the macro urban area difference. (2) The distribution of the GVI in Kaifeng City exhibits a significant spatial polarization phenomenon, with the proportion of low-value area (35.40%) being significantly higher than that of high-value area (25.10%) and the spatial clustering being evident (Moran’s Index 0.3824). Additionally, the ancient city area and the new city area exhibit distinct spatial organization patterns. (3) POI density and GVI had a substantial negative correlation (r = −0.085), suggesting a complicated process of interaction between green space and urban functions. The study reveals that the fairness of green visibility in historical and cultural cities presents the characteristics of differentiated distribution in different spatial scales, which provides a scientific basis for the optimization of greening spatial layouts in historical and cultural cities while preserving the traditional landscape. Full article

Since the 18th National Congress of the Communist Party of China in 2012, green and low-carbon development has become a national strategic priority. This study constructs a 39-indicator evaluation system grounded in the DPSIRM framework, which includes six interlinked subsystems. A key innovation lies in incorporating the Digital Inclusive Finance Index as a driver of green transitions and using Baidu search indices for “environmental protection” and “carbon dioxide” as proxies for public awareness. Using a projection pursuit model optimized by simulated annealing, we assess green low-carbon development across 30 Chinese provinces from 2011 to 2021. Temporal and spatial patterns are analyzed via kernel density estimation and Moran’s I, while Theil Index decomposition quantifies regional disparities. Results: First, substantial variations exist among Chinese provinces in both subsystem performance and integrated green low-carbon development levels, and response subsystems have the greatest influence on the overall development level. Second, over time, the gaps in green, low-carbon development between provinces have become more pronounced. Third, geographically, a distinct east-to-west declining gradient characterizes the regional clustering patterns of green low-carbon development. Fourth, the Theil Index for green, low-carbon development exhibits an overall trend of fluctuating increase, indicating that the overall gap in green, low-carbon development is gradually widening, with within-group disparities as the primary cause. This research enhances understanding of China’s green and low-carbon development, actively promoting global sustainable development and environmental improvement. Full article

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article

In recent years, the frequent occurrence of extreme weather events has prompted increased global attention to greenhouse gas emissions. This study analyzes the spatio-temporal evolution of carbon emission intensity (CEI) across land use types in China’s 30 provinces from 2009 to 2022. Based on the data from China Rural Statistical Yearbook, China City Statistical Yearbook, China Energy Statistical Yearbook, China Natural Resources Statistical Yearbook, and China Statistical Yearbook, this study aims to reveal the spatio-temporal differentiation patterns of CEI, analyze the decoupling status between development mode and carbon emissions, and establish a three-dimensional collaborative emission reduction framework. Firstly, employing the carbon emission factor method, provincial carbon emissions, sinks, and net emissions are calculated, with intensity levels derived from gross domestic product (GDP). Secondly, spatio-temporal trends and inter-provincial disparities are analyzed using the decoupling index. The spatial effects among the provinces are investigated based on Moran’s I index. The results show that while the overall CEI has declined since 2009, significant regional disparities persist, with the southern provinces showing lower carbon emission intensities compared to the northern and western regions. The spatial analysis reveals a strong aggregation effect, with provinces clustering into high-high (HH) and low-low (LL) regions regarding CEI. This study concludes with policy recommendations for emission reduction and climate change mitigation, emphasizing industrial structure adjustment, enhanced regional coordination, and optimized land use planning. Full article

This study systematically evaluates the spatial effects and driving mechanisms of Passenger Throughput (PT) within the Multi-airport System (MAS) of the Yangtze River Delta (YRD) region in China, using data from 22 cities between 2011 and 2019. Initially, the Air Passenger Location Entropy (APLE) index is introduced to quantify the spatial agglomeration within the MAS. Subsequently, both global and local Moran’s I indices are employed to assess the spatial autocorrelation of PT. Finally, Lagrange Multiplier (LM) tests, Wald test and Likelihood Ratio (LR) tests are utilized to select the appropriate spatial econometric model under different spatial weight matrices. Key findings include: (1) Air transport activity within the MAS exhibits a dynamic trend toward intensified spatial agglomeration and enhanced regional equilibrium; (2) APLE with higher value primarily concentrated in the southeastern coastal cities; (3) APLE has a significant positive impact on PT, with a 1% increase in APLE, leading to an average increase of 0.429% in PT; and (4) in cities with a well-developed air transport system, PT is predominantly influenced by APLE (0.915), whereas in cities with less robust air transport infrastructure, PT is more strongly influenced by tertiary industry value added (0.839) and GDP (0.442). These findings underscore the pivotal role of spatial dynamics in shaping PT and emphasize the necessity of spatially informed policy interventions to foster balanced regional development, strengthen system resilience, and advance the sustainable evolution of the MAS. Full article

The historic landscape and the value of the ancient city in the stock era present a diversified and mixed problem; as such, this study explores a quantifiable spatial correlation method for landscape layering characteristics and value space, in order to provide support for the urban renewal paths that integrate historical and contemporary needs. Taking as an example Chaozhou Ancient City, a renowned historical and cultural city in China, this study draws on the theory of historical urban landscape layering and comprehensively uses historical graphic interpretation, GIS spatial quantitative analysis, the single-land-use dynamic degree model, the Analytic Network Process, and the Delphi method to quantitatively analyze and evaluate the landscape layering characteristics and value space of the ancient city. Meanwhile, it explores the relationship between the historical landscape layering characteristics and value space of ancient cities using the spatial autocorrelation model and the coupling coordination modulus model. The key findings are as follows: (1) The high-layer space (66.1%) and high-value space (31.1%) of the historic landscape of Chaozhou Ancient City show significant mismatch and imbalance. Spatially, layer spaces increase from the city center toward the periphery, whereas value spaces decrease from the center outward, demonstrating marked spatial heterogeneity. (2) The layer–value space shows a spatial distribution of agglomeration, with Moran’s I index values of 0.2712 and 0.6437, respectively. The agglomeration degree of the value space is much higher than that of the layer space, and both show significant non-equilibrium and associative coupling. (3) Coupling coordination: basically balanced (D = 0.56) indicates a transition toward a more integrated state, although 48% of the region remains in a state of severe dysfunction, mainly consisting of two types of spaces: “high-layer–high-value” and “low-layer–low-value.” These two dysfunctional types should be prioritized in future conservation and renewal strategies. This study provides a more comprehensive quantitative analysis path for identifying and evaluating the landscape layer–value space of the ancient city, providing visualization tools and decision-making support for the future protection and renewal of Chaozhou Ancient City and the declaration of the World Heritage. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article