Search Results (667)

The uneven spatial distribution of data elements poses challenges to regional equity and sustainable development. To unmask spatial dynamics obscured by traditional macro-divisions, this study evaluates data element development across China’s Eight Comprehensive Economic Regions from 2013 to 2022. Using the entropy weight method, Dagum Gini coefficient, Kernel Density Estimation, and spatial autocorrelation models, the results indicate that while the overall development index exhibits a sustained upward trend, inter-regional differences remain the dominant source of spatial inequality. This disparity is primarily driven by the persistent gap between advanced coastal and lagging inland regions. Notably, spatial trajectories diverge significantly: the Eastern Coastal region exhibits coordinated integration, whereas severe internal polarization appears in the Middle Reaches of the Yellow River and the Southwest. Furthermore, the spatial spillover of data elements remains bounded by physical geography. By highlighting these meso-level structural fault lines, this study provides precise empirical evidence for formulating targeted, basin-specific interventions to bridge the digital divide. Full article

Storm-driven turbidity is a major water-quality concern in urban watersheds, reflecting the mobilization and transport of fine sediment during runoff events. This study examines how seasonal storm characteristics influence turbidity and associated sediment transport responses in the Middle Fork of Beargrass Creek, Louisville, Kentucky, over a two-year period. Forty-one erosive storm events were identified and characterized using high-resolution rainfall data to capture storm magnitude and structure. Study objectives were to: (1) quantify event-scale turbidity responses to erosive storms, (2) compare upstream and downstream turbidity behavior to assess spatial variability, (3) evaluate seasonal variation in these relationships, and (4) assess implications for sediment-focused best management practice (BMP) design. Event-based regression models related downstream turbidity to lagged upstream turbidity and downstream erosivity. Turbidity ratios and turbidity–discharge hysteresis characterized spatial and temporal sediment transport dynamics. Results showed that winter and spring storms exhibited longer durations, stronger upstream–downstream turbidity coupling, and more stable lag relationships, indicating integrated sediment transport. Short-duration, high-intensity summer storms produced elevated turbidity ratios, pronounced clockwise hysteresis, and greater model sensitivity, consistent with localized sediment mobilization. Findings support seasonally adaptive BMP strategies, with volume-reduction approaches most effective during winter–spring and source control measures critical during summer-fall. Full article

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

Accurate prediction of vibration responses in hydraulic structures during flood discharge is essential for ensuring safe and stable operation. This study develops a hybrid deep learning model that combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a Channel Attention (CA) mechanism, optimized through Bayesian Optimization (BO), to predict dam gantry crane beam displacements. Time-lagged Pearson correlation and Maximum Information Coefficient (MIC) are applied to select the informative input features. The CNN-BiLSTM-CA model captures both spatial patterns and temporal dependencies in vibration signals. BO tunes model hyperparameters, while Partial Dependence (PD) analysis provides insight into how these parameters affect prediction accuracy. The model is validated using vibration data from an arch dam in Southwest China during flood discharge. Results show that CNN parameters have a greater impact on prediction accuracy than BiLSTM parameters, underscoring the importance of spatial feature extraction. Ablation studies confirm each component’s contribution. Compared with existing methods, the proposed model achieves superior accuracy with a Root Mean Square Error (RMSE) of 5.49, Mean Absolute Error (MAE) of 4.34, and correlation coefficient (R) of 99.42%. This framework provides a reliable and interpretable tool for predicting structural vibrations in hydraulic engineering under complex discharge conditions. Full article

Efficiently converting natural capital into economic assets is a critical challenge in urban–rural transformation, yet the interactive mechanism between institutional land reform and physical spatial restructuring remains underexplored. While traditional frameworks emphasize institutional design, this study identifies a “Spatial-First” mechanism where physical reconfiguration serves as a spatial mediator to catalyze property rights breakthroughs. Using an entropy-weighted coupling coordination model, we analyzed policy dynamics in Nanhai District, China, a unique “dual-pilot” zone, from 2020 to 2024. The results indicate a nonlinear leap in the Coupling Coordination Degree (D) from 0.100 to 0.978. We interpret this surge as a policy-driven shock during the intensive pilot phase, where substantive spatial integration (0.719) effectively bypassed high transaction costs inherent in collective tenure, outpacing institutional progress (0.281). However, an Ecological Lag was observed; the disproportionately low weighting of the ecological carrier index (7.09%) suggests that current gains are primarily driven by green industrialization rather than the expansion of absolute ecological stock. This study concludes that while spatial tools can effectively unlock natural capital value in the short term, long-term sustainability necessitates a strategic shift from administrative-led economic efficiency to market-based ecological restoration. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Hydrogen energy is pivotal to the global energy transition, and the development of high-efficiency, safe hydrogen storage technologies constitutes a prerequisite for its large-scale commercialization. Kinetic bottlenecks including slow reactions, delayed front propagation, and marked spatial heterogeneity driven by strong thermal–mass transfer coupling restrict the engineering application of solid-state metal hydrides. However, the current research mainly focusing on overall performance lacks a systematic understanding of the spatiotemporal evolution mechanisms and their intrinsic links to internal structural control. In this work, a 3D multiphysics model of a LaNi₅-based reactor is developed to systematically elucidate spatiotemporal evolution patterns, facilitating the proposal of a structural decoupling framework based on synergistic thermal–mass resistance reconfiguration. Both absorption and desorption show distinct three-stage evolution, shifting from kinetic dominance to transfer limitation: absorption causes core self-inhibition via heat-hydrogen supply mismatch, leading to much lower core than surface storage capacity; desorption results in significant inner-layer lag due to endothermic cooling-driven pressure drops. Thermal–mass coupling-induced inverted spatiotemporal evolution is identified as the root cause of spatial heterogeneity. Quantitative comparison of straight-pipe, spiral-tube, and honeycomb structures reveals that internal architectures achieve effective thermal–mass decoupling through expanded heat-exchange areas, reconstructed diffusion pathways, and optimized heat source distribution. Notably, the honeycomb structure with a parallel micro-unit network achieves 89.1% and 86.6% reductions in absorption and desorption times, respectively, showing superior dynamic performance and field uniformity. This study provides a theoretical basis for the mechanism-driven design and synergistic performance optimization of high-efficiency solid-state hydrogen storage reactors. Full article

Patterns of footfall counts in urban environments show regularity at various spatial and temporal scales. In this work, we study a lightweight hierarchical approach in which forecasts use four lagged higher-level aggregates as predictors trained with simple CPU-only models. For a fair comparison, the baseline is expanded to use a horizon-matched lag window, so that the variants have access to the same maximum lookback in time. The study uses hourly pedestrian counts from 13 sensors on two shopping streets in Newcastle upon Tyne, aggregated across spatial and temporal levels. Combined spatial and temporal aggregate predictors reduced forecast error by adding information from higher aggregation levels without changing the base learner. The best-performing configuration was SHTH+CP, which combines spatial and temporal parent features with a spatio-temporal cross-parent, and yielded an average pooled 4.3% improvement in RMSE and 3.5% in MAE, with the largest gains at 12 h directional counts, where RMSE decreased by 6.7% and MAE by 11.4%. Full article

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Against Women (CAW), the Multidimensional Poverty Index (MPI), and drought severity, using data from NFHS-5, the National Crime Records Bureau, NITI Aayog’s MPI reports, and the Drought Atlas of India. Spatial autocorrelation and Spatial regression models were applied alongside machine learning approaches and SHAP-based Explainable AI (XAI) interpretation. Childhood stunting exhibited significant spatial clustering (Moran’s I = 0.520, p < 0.001), with hotspots in northern, central, and eastern India. Higher stunting was associated with higher birth order, low maternal BMI, child anaemia, and MPI, and negative associations with iodised salt usage, electricity access, and timely postnatal care. A significant spatial lag parameter (ρ = 0.348) indicated substantial spillover effects. Machine learning models consistently identified MPI, drought severity, and CAW as key predictors. The integrated spatial and machine learning framework identifies key correlates and spatial dependencies of childhood stunting, highlighting the need for region-specific, multisectoral interventions. Full article

This study explores the coupling relationship between wetland ecotourism and resource–environmental carrying capacity in the Dongting Lake region. By constructing a comprehensive index system and utilizing a coupling coordination degree model, we analyzed the temporal and spatial evolution characteristics across 24 districts and counties from 2014 to 2022. The results indicate the following: (1) The quality of both ecotourism and environmental carrying capacity has steadily improved, though significant regional disparities remain. (2) The coupling coordination degree exhibits a “high in the center, low in the periphery” spatial pattern, showing a positive correlation between ecotourism levels and environmental capacity. (3) The region comprises three development types: balanced coordination, well-matched, and lagging. These findings provide a scientific basis for optimizing ecotourism pathways and achieving high-quality regional sustainable development. Full article

A multi-dimensional quantitative assessment of high-quality development (HQD) in China’s livestock industry and the identification of its main constraints are essential to understanding its current stage and future direction. Guided by global sustainability targets and the United Nations’ Sustainable Development Goals (SDGs), an evaluation system was constructed by this study. This system integrates five key aspects: product safety, output efficiency, resource conservation, environmental friendliness, and regulatory effectiveness. Using provincial panel data from China for 2013–2022, this research applies the entropy-weighted TOPSIS method, kernel density estimation (KDE), and an obstacle degree model for analysis, the goal is to support food security and foster environmentally sustainable growth. The findings indicate the following: (1) Notable inter-provincial disparities exist in the HQD of China’s livestock industry, revealing a spatial pattern of “leading in the east, stable in the center, and lagging in the west.” (2) The nationwide evolution exhibits a “convergence followed by divergence” pattern: from 2013 to 2017, the primary peak of the KDE rose and its width narrowed; from 2018 to 2022, the primary peak declined and its width widened, indicating that inter-provincial disparities first narrowed and then expanded. At the regional level, the development pattern is characterized by eastern polarization, central stability, and western lock-in. (3) Obstacle factor analysis identifies product safety and environmental friendliness as the principal constraints on HQD in the livestock industry. Addressing these bottlenecks is crucial for ensuring the supply of livestock products (SDG 2: Zero Hunger), promoting resource conservation and green production (SDG 12: Responsible Consumption and Production), and alleviating the ecological and environmental pressures of the livestock industry (SDG 15: Protection of Terrestrial Ecosystems). The challenges related to resources, the environment, and quality safety confronting China’s livestock industry are common among developing countries. Consequently, the evaluation framework established in this study can offer methodological references for relevant nations. Full article

Tipping points of vegetation transitions represent the thresholds beyond which ecosystems can no longer maintain their stable states. Approaching these critical points may result in declined resilience or irreversible vegetation transitions. Detecting and predicting tipping points remains notably challenging, yet it is essential for guiding the preservation and restoration of terrestrial ecosystems. In this study, lag-1 temporal autocorrelation (AC1) derived from the Kernel Normalized Difference Vegetation Index (kNDVI) was utilized as an early warning signal to monitor resilience dynamics. We developed a new tipping-point detection method by combining land-cover changes, time series segmentations and temporal–spatial filters. We revealed a widespread resilience decline in China, with the dominant transition type as shrub encroachment. Then, two machine learning models coupled with temporal cross-validation were employed to predict the probabilities of abrupt shifts in the near future. The results showed that Random Forest models (accuracy > 70%) demonstrated robustness across lead times. High probabilities of transitions in 2024 were concentrated along the 400 mm annual isohyet, mainly affected by decreased water availability, lower soil acidity and degraded vegetation functions. Our study provides an effective methodology to pinpoint hotspots of vegetation vulnerability and to support the conservation of ecosystems for a sustainable future. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

The integration of intangible cultural heritage (ICH) and tourism development (TD) is regarded as a crucial national strategy for China’s sustainable development, as their synergistic relationship is considered pivotal for regional progress. A coupling coordination evaluation system was constructed. Kernel density estimation, entropy method, coupling coordination degree (CCD) and relative development degree (RDD) models, and a tobit model were employed to examine the spatiotemporal characteristics and influencing factors of ICH–TD integration in Sichuan Province. Key findings are as follows: (1) Sichuan is endowed with abundant ICH resources characterized by high heritage value and diverse typologies. However, the distribution is skewed toward traditional skills, exhibiting notable regional disparities. ICH demonstrates a “single-core, belt-shaped and multi-cluster” pattern, which is centered on Chengdu, extends along a north–south high-density belt, and forms several secondary high-density clusters. (2) Temporally, the CCD demonstrates a sustained upward trend, whereas the RDD transitions from ICH-lagged to TD-lagged. Spatially, the number of high coordinated cities increases annually, expanding radially from regional centers, while central-eastern regions consistently outperform the west. (3) Regarding influencing factors, comprehensive economic strength, distribution of industrial structure, overall level of urbanization, and transportation accessibility exert significant positive effects on the CCD, with comprehensive economic strength demonstrating the strongest influence. This study contributes to the theoretical understanding of ICH–TD synergy and provides policy-relevant guidance for integration. Full article

Drought–flood abrupt alternation (DFAA) events have emerged as a critical type of compound climate extreme under ongoing climate change, posing increasing risks to water resources and ecosystems in Southwest China. This study investigated the spatiotemporal evolution of DFAA events during the historical period (1995–2024) and the future period (2025–2064), as well as their relationships with evapotranspiration. Daily precipitation was simulated using a CMIP6 multi-model ensemble mean (MME) combined with Delta downscaling, while station observations were used to identify DFAA events and evapotranspiration. Model performance was evaluated using Taylor diagrams and simulation relative bias. The results showed that the downscaled MME substantially improved the simulation of precipitation, evapotranspiration, and cumulative DFAA event occurrences, with relative bias in most regions controlled within ±3%. Compared with the historical period, both drought-to-flood (DTF) and flood-to-drought (FTD) events showed overall increases during 2025–2064. Specifically, under the four SSP scenarios, DTF events increased by 165, 133, 180, and 140 occurrences, respectively, while FTD events increased by 130, 147, 114, and 79 occurrences, respectively. The regional mean trends of DTF events during the near-term period were −0.21, 0.16, −0.45, and 1.24 times·5a⁻¹, whereas the corresponding trends of FTD events were 1.82, 1.17, 0.05, and −1.03 times·5a⁻¹ under the four scenarios. Spatial analyses revealed pronounced regional heterogeneity, with enhanced signals mainly concentrated in eastern Sichuan, Chongqing, and parts of Guizhou. Lagged correlation analyses further indicated significant monthly lag effects between DFAA events and evapotranspiration during the flood season; DTF events generally showed positive correlations with subsequent evapotranspiration, whereas FTD events exhibited predominantly negative correlations. Overall, this study clarifies the future spatiotemporal evolution of DFAA events in Southwest China and highlights the important role of land–atmosphere hydrothermal processes in regulating compound drought–flood extremes. Full article