Search Results (851)

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

This study investigates changes in Near-Surface Air Temperature (NSAT) over the South African region using weather station data, reanalysis products, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. It is shown that, based on ERA5 reanalysis, the average NSAT increase in the region (45–10° S, 0–50° E) for the period 1940–2023 was 0.11 ± 0.04 °C. Weak multi-decadal changes in NSAT were observed from 1940 to the mid-1970s, followed by a rapid warming trend starting in the mid-1970s. Weather station data generally confirm these results, although they exhibit considerable inter-station variability. An ensemble of 33 CMIP6 models also reproduces these multi-decadal NSAT change characteristics. Specifically, the average model-simulated NSAT values for the region increased by 0.63 ± 0.12 °C between the periods 1940–1969 and 1994–2023. Based on the results of the comparison between weather station observations, reanalysis, and models, we utilize projections of NSAT changes from the analyzed ensemble of 33 CMIP6 models until the end of the 21st century under various Shared Socioeconomic Pathway (SSP) scenarios. These projections indicate that the average NSAT of the South African region will increase between 1994–2023 and 2070–2099 by 0.92 ± 0.36 °C under the SSP1-2.6 scenario, by 1.73 ± 0.44 °C under SSP2-4.5, by 2.52 ± 0.50 °C under SSP3-7.0, and by 3.17 ± 0.68 °C under SSP5-8.5. Between 1994–2023 and 2025–2054, the increase in average NSAT for the studied region, considering inter-model spread, will be 0.49–1.15 °C, depending on the SSP scenario. Furthermore, climate warming in South Africa, both in the next 30 years and by the end of the 21st century, is projected to occur according to all 33 CMIP6 models under all considered SSP scenarios. The main spatial feature of this warming is a more significant increase in NSAT over the landmass of the studied region compared to its surrounding waters, due to the stabilizing role of the ocean. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods do not account for network-based effects. Additionally, network-based research has explored a multitude of data from urban landscapes. However, achieving a comprehensive understanding of urban mobility proves challenging without exhaustive datasets. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city’s socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. In experiments in 12 major U.S. cities, the proposed model achieves considerable explanatory performance and is able to outperform previous conventional machine learning models based on extensive regional-level features. Providing researchers with methods to incorporate network effects in urban modeling, this work also informs stakeholders of wider network-based effects in urban policymaking and planning. Full article

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

Rockfalls, among the most common natural disasters, pose risks such as traffic congestion, casualties, and substantial property damage. Guizhou Province, with China’s fourth-longest highway network, features mountainous terrain prone to frequent rockfall incidents annually. Consequently, assessing highway rockfall risks in Guizhou Province is crucial for safeguarding the lives and travel of residents. This study evaluates highway rockfall risk through three key components: susceptibility, hazard, and vulnerability. Susceptibility was assessed using information content and logistic regression methods, considering factors such as elevation, slope, normalized difference vegetation index (NDVI), aspect, distance from fault, relief amplitude, lithology, and rock weathering index (RWI). Hazard assessment utilized a fuzzy analytic hierarchy process (AHP), focusing on average annual rainfall and daily maximum rainfall. Socioeconomic factors, including GDP, population density, and land use type, were incorporated to gauge vulnerability. Integration of these assessments via a risk matrix yielded comprehensive highway rockfall risk profiles. Results indicate a predominantly high risk across Guizhou Province, with high-risk zones covering 41.19% of the area. Spatially, the western regions exhibit higher risk levels compared to eastern areas. Notably, the Bijie region features over 70% of its highway mileage categorized as high risk or above. Logistic regression identified distance from fault lines as the most negatively correlated factor affecting highway rockfall susceptibility, whereas elevation gradient demonstrated a minimal influence. This research provides valuable insights for decision-makers in formulating highway rockfall prevention and control strategies. Full article

The Gunung Sewu UNESCO Global Geopark (GSUGGp) is one of Indonesia’s 12 UNESCO-designated geoparks. Its presence is expected to enhance rural development by boosting the local economy through tourism. However, there is a lack of statistical evidence quantifying the economic benefits of geopark development, mainly due to the complex, non-linear nature of these impacts and limited village-level economic data available in Indonesia. To address this gap, this study aims to measure how socio-economic and environmental factors contribute to the Village Development Index (VDI) within the GSUGGp area, which includes the districts of Gunung Kidul, Wonogiri, and Pacitan. A machine learning–deep learning approach was employed, utilizing four algorithms grouped into eight models, with hyperparameter tuning and cross-validation, tested on a sample of 92 villages. The analysis revealed insights into how 17 independent variables influence the VDI. The Artificial Neural Network (ANN) algorithm outperformed others, achieving an R-squared of 0.76 and an RMSE of 0.040, surpassing random forest, CART, SVM, and linear models. Economically related factors—considered the foundation of rural development—had the strongest impact on village progress within GSUGGp. Additionally, features related to tourism, especially beach tourism linked to geological landscapes, contributed significantly. These findings are valuable for guiding geopark management and policy decisions, emphasizing the importance of integrated strategies and strong cooperation among local governments at the regency and provincial levels. Full article

The distribution of Itea yunnanensis, a shrub species in the genus Itea of the family Iteaceae, is primarily concentrated in the Hengduan Mountains region of China, where it faces severe threats from global climate change. However, systematic research on the species’ distribution patterns, climatic response mechanisms, and future suitable habitat dynamics remains insufficient. This study aims to assess the spatiotemporal evolution and driving mechanisms of I. yunnanensis-suitable habitats under current and future climate change scenarios to reveal the migration patterns of its distribution centroid and ecological thresholds, and to enhance the reliability and interpretability of predictions through model optimization. For MaxEnt modeling, we utilized 142 georeferenced occurrence records of I. yunnanensis alongside environmental data under current conditions and three future Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP5-8.5). Model parameter optimization (Regularization Multiplier, Feature Combination) was performed using the R (v4.2.1) package ‘ENMeval’. The optimized model (RM = 3.0, FC = QHPT) significantly reduced overfitting risk (ΔAICc = 0) and achieved high prediction accuracy (AUC = 0.968). Under current climate conditions, the total area of potential high-suitability habitats for I. yunnanensis is approximately 94.88 × 10⁴ km², accounting for 9.88% of China’s land area, with core areas located around the Hengduan Mountains. Under future climate change, the suitable habitats show significant divergence, area fluctuation and contraction under the SSP1-2.6 scenario, and continuous expansion under the SSP5-8.5 scenario. Meanwhile, the species’ distribution centroid exhibits an overall trend of northwestward migration. This study not only provides key spatial decision-making support for the in situ and ex situ conservation of I. yunnanensis, but also offers an important methodological reference for the adaptive research on other ecologically vulnerable species facing climate change through its optimized modeling framework. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

The rapid proliferation of electric vehicles necessitates accurate forecasting of charging pile capacity for urban power system planning, yet existing methods for medium- to long-term prediction lack effective mechanisms to capture complex multi-factor relationships. To address this gap, a hybrid cointegration–BiLSTM framework is proposed for medium- to long-term load forecasting. Cointegration theory is leveraged to identify long-term equilibrium relationships between EV charging capacity and socioeconomic factors, effectively mitigating spurious regression risks. The extracted cointegration features and error correction terms are integrated into a bidirectional LSTM network to capture complex temporal dependencies. Validation using data from 14 cities in Hunan Province demonstrated that cointegration analysis surpassed linear correlation methods in feature preprocessing effectiveness, while the proposed model achieved enhanced forecasting accuracy relative to conventional temporal convolutional networks, support vector machines, and gated recurrent units. Furthermore, a 49% reduction in MAE and RMSE was observed when ECT-enhanced features were adopted instead of unenhanced groups, confirming the critical role of comprehensive feature engineering. Compared with the GRU baseline, the BiLSTM model yielded a 26% decrease in MAE and a 24% decrease in RMSE. The robustness of the model was confirmed through five-fold cross-validation, with ECT-enhanced features yielding optimal results. This approach provides a scientifically grounded framework for EV charging infrastructure planning, with potential extensions to photovoltaic capacity forecasting. Full article

European integration aims to achieve spatially sustainable development across the member states. However, the success of socio-economic integration is conditioned by structural features of the economies, which, hitherto, appear highly diversified across the EU countries. The paper focuses on the structural conditions of the process of income inequality convergence. It aims to identify differences in the convergence regarding the structural conditions of the economies. To fulfil the research tasks the paper classifies the 27 European member states according to their sectional employment structures using the Ward method. It then tests the appearance of beta convergence using FE panel models for the specified clusters of economies. It also considers structural change, measured by the NAV (norm of absolute value), as a determinant of income inequality convergence. The main research period covers 2009–2021. The findings of the paper confirm that income inequality convergence occurs within the groups of economies specified by different structural conditions. Importantly, the clustering according to the similarity of the employment structure overlaps with the division along the lines of the ‘new’ and ‘old’ member states, which proves the importance of historically shaped institutions for development. However, the observed convergence does not lead to improved social cohesion. Social policy, especially in the ‘new’ member states, is not able to offset the growth in market income inequality additionally stimulated by the structural changes. It can be concluded that an urgent need to design new solutions for social policy concerning structural transformation in employment in the EU emerges. Full article

Background/Objectives: Breast cancer (BC) is the leading cause of cancer incidence and mortality among women and the recent identification of a resident mammary microbiota has highlighted its potential role in breast carcinogenesis. Given that environmental and socioeconomic factors influence both BC prevalence and tumor-associated bacterial composition, this study aimed to evaluate the compositional and functional features of the mammary microbiota in cancerous (oncobiome) and adjacent healthy BC tissues from patients living in urban and rural areas. Methods: Microbiota composition in both the oncobiome and adjacent healthy BC tissues was analyzed using 16S rRNA sequencing. Results: Significant variations in breast oncobiome composition were observed among BC patients from urban and rural areas. A statistically significant β dispersion among breast oncobiome of patients from urban or rural areas was highlighted. Specifically, the genera Selenomonas, Centipeda, Leptotrichia, Neisseria and Porphyromonas were found exclusively in BC tissues of patients from rural areas. Additionally, bacteria from the Neisseriaceae, Porphyromonadaceae, and Selenomonadaceae families, as well as the Selenomonas genus, were significantly enriched in the oncobiome of rural BC patients. Furthermore, the results of the PICRUSt2 (phylogenetic investigation of communities by reconstruction of unobserved states) revealed a significant increase in phospholipid biosynthesis pathways in breast oncobiome of patients from rural areas compared to those from urban areas. Conclusions: This study provides evidence of distinct compositional and functional differences in the breast oncobiome between BC patients from rural and urban areas. These findings suggest that environmental factors influence local microbiome composition, potentially contributing to BC development and/or progression. Full article

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems. Full article