Search Results (1,393)

The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments. Full article

Global environmental changes significantly alter ecosystem services (ESs), particularly in fragile regions like the Tibetan Plateau. While methodological advances have improved spatial assessment capabilities, understanding of how multiple drivers interact to shape ecosystem service heterogeneity remains limited to regional scales, especially across complex alpine landscapes. This study aims to clarify whether multi-factor interactions produce nonlinear enhancements in ES explanatory power and how these driver–response relationships vary across heterogeneous terrains. We quantified spatiotemporal patterns of four key ecosystem services—water yield (WY), soil conservation (SC), carbon sequestration (CS), and habitat quality (HQ)—across the southeastern Tibetan Plateau from 2000 to 2020 using multi-source remote sensing data and spatial econometric modeling. Our analysis reveals that SC increased by 0.43 t·hm⁻²·yr⁻¹, CS rose by 1.67 g·m⁻²·yr⁻¹, and HQ improved by 0.09 over this period, while WY decreased by 3.70 mm·yr⁻¹. ES variations are predominantly shaped by potent synergies, where interactive explanatory power consistently surpasses individual drivers. Hydrothermal coupling (precipitation ∩ potential evapotranspiration) reached 0.52 for WY and SC, while climate–vegetation synergy (precipitation ∩ normalized difference vegetation index) achieved 0.76 for CS. Such climate–restoration synergies now fundamentally shape the region’s ESs. Geographically weighted regression (GWR) further revealed distinct spatial dependencies, with southeastern regions experiencing strong negative effects of land use type and elevation on WY, while northwestern areas showed a positive elevation associated with WY but negative effects on SC and HQ. These findings highlight the critical importance of accounting for spatial non-stationarity in driver–ecosystem service relationships when designing conservation strategies for vulnerable alpine ecosystems. Full article

This study investigates the spatial patterns and drivers of carbon emissions across China’s three major urban agglomerations—Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD)—from 2011 to 2020. A sequential analytical framework was employed to examine emission inequality, spatial dependence, dynamic transitions, and multi-scale drivers. Specifically, the Gini and Theil indices were used to quantify and decompose regional disparities. Spatial clustering patterns and heterogeneity were then identified through global and local Moran’s I analysis. Following this, spatial Markov chains modeled state transitions and neighborhood spillover effects. Finally, the Spatial Durbin Model (SDM) was applied to distinguish between the direct and indirect effects of key socioeconomic drivers. The findings reveal that disparities in emissions are largely driven by factors within each region. In BTH, heavy industrial lock-in accounts for 47.1% of the within-group inequality. By contrast, the YRD and PRD show noticeable convergence, achieved through industrial synergy and technological restructuring, respectively. The mechanisms of spatial spillover also differ across regions. In the YRD, emissions exhibit strong clustering tied to geographic proximity, with Moran’s I consistently above 0.6. In BTH, policy linkages play a more central role in shaping emission patterns. Meanwhile, in the PRD, widespread technological diffusion weakens the conventional distance-decay effect. The influence of key drivers varies notably among the urban agglomerations. Economic growth has the strongest scale effect in the PRD, reflected by a coefficient of 0.556. Industrial transformation significantly lowers emissions in the YRD, with a coefficient of −0.115. Technology investment reduces emissions in BTH (−0.124) and the PRD (−0.076), but is associated with a slight rebound in the YRD (0.037). Overall, these results highlight the persistent path dependence and distinct spatial interdependencies of carbon emissions in each region. This underscores the need for tailored mitigation strategies that are coordinated across administrative boundaries. Full article

Pan evaporation (PE) is widely used as an indicator of atmospheric evaporative demand and is relevant to irrigation demand and climate-related hydrological changes. Using daily records from 759 meteorological stations across China during 2002–2018, this study investigated the temporal trends, spatial patterns, and climatic controls of PE across seven major climate zones. Multiple decomposition techniques revealed a dominant annual cycle and a pronounced peak in 2018, while a decreasing interannual trend was observed nationwide. Spatial analyses showed a clear north–south contrast, with the strongest declines occurring in northern China. A random forest (RF) model was employed to quantify the contributions of climatic variables, achieving high predictive performance. RF results indicated that the dominant drivers of PE varied substantially across climate zones: sunshine duration (as a proxy for solar radiation) and air temperature mainly controlled PE in humid regions, while wind speed and relative humidity (RH) exerted stronger influences in arid and semi-arid regions. The widespread decline in northern China is consistent with concurrent changes in wind speed and sunshine duration, together with humidity conditions, which modulate evaporative demand at monthly scales. These findings highlight substantial spatial heterogeneity in PE responses to climate forcing and provide insights for drought assessment and water resource management in a warming climate. Full article

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

The Yellow River Basin (YRB), a typical river system facing the challenge of balancing ecological conservation and economic development, offers valuable insights for global sustainable watershed governance through its forestry green transformation. Based on panel data from nine provinces in the basin from 2005 to 2022, this study constructs an efficiency evaluation indicator system for forestry green development. This system incorporates four inputs (labor, land, capital, and energy), two desirable outputs (economic and ecological benefits), and three undesirable outputs (wastewater, waste gas, and solid waste). By systematically integrating the undesirable outputs-based super-SBM model and the global Malmquist–Luenberger (GML) index, this study provides an assessment from both static and dynamic perspectives. The findings are as follows. (1) Forestry green development efficiency showed fluctuations over the study period, with the basin-wide average remaining below the production frontier. Spatially, it exhibits a pattern of “downstream > upstream > midstream”. (2) The average GML index is 0.984 during the study period, representing an average annual decline in forestry green total factor productivity of 1.6%. The growth dynamics transitioned from a stage dominated solely by technological progress to a dual-driver model involving both technological progress and technical efficiency. (3) The drivers of forestry green total factor productivity growth in the basin show profound regional heterogeneity. The downstream region demonstrates a synergistic dual-driver model of technical efficiency and technological progress, the midstream region is trapped in “dual stagnation” of both technical efficiency and technological progress, and the upstream region differentiates into four distinct pathways: technology-driven yet foundationally weak, efficiency-improving yet technology-lagged, endowment-advantaged yet transformation-constrained, and condition-constrained with efficiency limitations. The assessment framework and empirical findings established in this study can provide empirical evidence and policy insights for basins worldwide to resolve the ecological-development dilemma and promote forestry green transformation. Full article

Investigating vegetation dynamics and their drivers in ecologically vulnerable regions is essential for evaluating ecological restoration outcomes. This study examined the spatiotemporal evolution of the Normalized Difference Vegetation Index (NDVI) and its influencing factors in Fengning county, the Bashang region from 2001 to 2023 using land use transition matrix, trend analysis, and geographical detector methods. Key findings include the following: (1) Land use transition exhibited a clear phased pattern, shifting from cropland-to-grassland conversion (2001–2010) to grassland-to-forest conversion (2010–2023). (2) The annual mean NDVI increased significantly, showing a southeast–northwest spatial gradient consistent with landforms. The long-term trend followed a sequential “degradation–improvement–consolidation” trajectory. (3) Factor detection identified land use type as the primary driver of vegetation spatial heterogeneity (q = 0.297), highlighting the dominant influence of human activities. (4) Interaction detection demonstrated bivariate enhancement for all factor pairs, with the combination of land use type and precipitation yielding the highest explanatory power (q = 0.440). This underscores that vegetation dynamics are predominantly governed by nonlinear interactions between human-driven land use and climate. The research highlights the effectiveness of ecological restoration policies and offers valuable insights for guiding future ecosystem management in ecologically fragile areas under climate change. Full article

Enhancing agricultural green total factor productivity (AGTFP) under ecological and environmental constraints is essential for advancing green agricultural development in the Beijing–Tianjin–Hebei (BTH) region. Using panel data from 13 prefecture-level cities from 2001 to 2022, this study applies a super-efficiency EBM model incorporating undesirable outputs together with the Malmquist–Luenberger index to measure AGTFP. Global and local Moran’s I indices as well as the spatial Durbin model are then employed to examine the temporal evolution, spatial disparities, and spatial interaction effects of AGTFP during 2001–2022. The findings indicate that: (1) From 2001 to 2022, the AGTFP in the BTH region grew at an average annual rate of 7.7%. This trend reflects a growth pattern primarily driven by green technological progress in agriculture, while substantial disparities in AGTFP persist across different subregions. (2) the global Moran’s I values show frequent shifts between positive and negative spatial autocorrelation, suggesting that a stable and effective regional coordination mechanism for green agricultural development has yet to be formed; (3) the determinants of AGTFP exhibit pronounced spatiotemporal heterogeneity, and the fundamental drivers of the region’s green agricultural transition increasingly rely on endogenous growth generated by technological innovation and rural human capital; (4) policy recommendations include strengthening benefit-sharing and policy coordination mechanisms, promoting cross-regional cooperation in agricultural science and technology, and implementing differentiated industrial layouts to support green agricultural development in the BTH region. These results provide valuable insights for promoting coordinated and sustainable green agricultural development across regions. Full article

Immunotherapy has become a promising treatment for gastric cancer. However, its effectiveness varies significantly across subtypes because of heterogeneous immune microenvironments and genomic alterations. Here, we established Immune&Driver molecular subtypes CS1 and CS2 by systematically integrating multi-omics data for immune-related and driver genes. CS1 was linked to a better prognosis, while CS2 represented a poorer prognostic phenotype. CS1 displayed enhanced genomic instability, marked by higher mutation frequency and chromosomal alterations. In contrast, CS2 exhibited higher immune activity, with a higher density of immune cell infiltration and increased expression of chemokines and immune checkpoint genes. Among FDA-approved anti-cancer agents included in a pan-cancer drug sensitivity prediction framework, CS1 was predicted to be more sensitive to conventional chemotherapeutic agents, whereas CS2 was predicted to be more responsive to immune-related agents. In melanoma datasets, a CS2-like transcriptomic pattern was associated with improved response to anti-PD-1 therapy, with the combination of anti-PD-1 and anti-CTLA-4 showing more favorable response patterns compared to anti-PD-1 monotherapy. Additionally, we developed an immunotherapy response prediction model using PCA-based logistic regression according to the transcriptional expression of CS biomarkers. The model was trained in melanoma immunotherapy cohorts and validated across independent melanoma datasets, and it further achieved a higher AUC in an external gastric cancer cohort treated with anti-PD-1 therapy. Collectively, this study highlights immune and genomic heterogeneity in gastric cancer and provides a hypothesis-generating framework for exploring immunotherapy response. Full article

Sexual systems critically influence woody plant evolution and forest functioning, yet their global patterns and environmental drivers remain understudied. Investigating the environmental correlates of sexual systems in woody plants is essential for developing targeted conservation and restoration strategies for forest ecosystems. We analyzed sexual system composition of 3595 woody species from 30 ForestGEO forest plots spanning tropical, subtropical, and temperate zones in the Northern Hemisphere. Species were classified by sexual system (hermaphroditism, monoecy, and dioecy) and growth form (trees and shrubs). Community-level patterns were assessed across climatic zones, and the relative contributions of climatic, spatial, and topographic factors were quantified using multivariate and network-based analyses. We observed the following: (1) Sexual system composition exhibited clear climatic differentiation: dioecious species predominate in tropical forests, while monoecious species increased in dominance toward temperate regions. (2) Climatic variables, particularly temperature and precipitation, accounted for more variation in sexual system composition than spatial or topographic factors, although their relative influence differed among climatic zones. (3) Distinct life-form-specific patterns were detected: sexual systems of trees were more strongly associated with broad-scale climatic gradients, whereas those of shrubs were more closely linked to spatial structure and local environmental heterogeneity. Together, these results demonstrate that climate is a dominant but life-form-dependent driver of sexual system biogeography in woody plants, improving trait-based understanding of forest biodiversity responses to climate change. Full article

Heart failure with preserved ejection fraction (HFpEF) accounts for about half of heart failure cases and is linked to aging, obesity, diabetes, and multimorbidity, yet disease-modifying therapies remain limited. A major barrier is heterogeneity: HFpEF comprises overlapping inflammatory, fibrotic, cardiometabolic, and hemodynamic/vascular endophenotypes embedded within systemic cardiorenal and cardiohepatic cross-talk, which conventional metrics such as left ventricular ejection fraction (LVEF), natriuretic peptides (NPs), and standard imaging capture incompletely. In this narrative review, we synthesize clinical, mechanistic, and trial data to describe HFpEF endophenotypes and their multi-organ interactions; critically appraise why traditional diagnostic and enrollment strategies contributed to neutral outcomes in landmark trials; and survey emerging cardiovascular multi-omics studies. We then outline an integrative systems-biology framework that applies (i) within-layer analyses and cross-layer integration, (ii) network-based driver nomination and biomarker discovery, and (iii) target nomination to link molecular programs with circulating markers and candidate therapies. Finally, we discuss practical challenges in implementing multi-omics HFpEF research and highlight future directions such as artificial intelligence (AI)-enabled multi-omics integration, cross-organ profiling, and biomarker-guided, endotype-enriched platform trials. Collectively, these advances position HFpEF as a proving ground for precision cardiology, in which therapies are matched to molecularly defined disease programs rather than ejection-fraction cutoffs alone. Full article

Environmental, social and governance (ESG) metrics increasingly inform sustainable investment yet suffer from inter-rater heterogeneity and incomplete reporting, limiting their utility for forward-looking allocation. In this study, we developed and validated a two-level stacked-ensemble machine-learning framework to predict total ESG risk scores for S&P 500 firms using a comprehensive feature set comprising pillar sub-scores, controversy measures, firm financials, categorical descriptors and geospatial environmental indicators. Data pre-processing combined median/mean imputation, one-hot encoding, normalization and rigorous feature engineering; models were trained with an 80:20 train–test split and hyperparameters tuned by k-fold cross-validation. The stacked ensemble substantially outperformed single-model baselines (RMSE = 1.006, MAE = 0.664, MAPE = 3.13%, $R^2$ = 0.979, CV_RMSE_Mean = 1.383, CV_R²_Mean = 0.957), with LightGBM and gradient boosting as competitive comparators. Permutation importance and correlation analysis identified environmental and social components as primary drivers (environmental importance = 0.41; social = 0.32), with potential multicollinearity between component and aggregate scores. This study concludes that ensemble-based predictive analytics can produce reliable, actionable ESG estimates to enhance screening and prioritization in sustainable investment, while recommending human review for extreme predictions and further work to harmonize cross-provider score divergence. Full article

The Three Gorges Reservoir Area (TGRA) faces mounting pressures from urbanization and hydrological regulation, threatening the sustainability of its ecosystem services (ESs). The InVEST model, coupled with optimal parameter geographical detector (OPGD) and geographically and temporally weighted regression (GTWR), was employed to assess spatiotemporal changes, trade-offs/synergies, and driving mechanisms of four ESs, water yield (WY), habitat quality (HQ), carbon storage (CS), and soil conservation (SC), from 2000 to 2020. Results revealed that WY and SC increased significantly by 24.54% and 5.75%, respectively, while HQ declined by 3.02% and CS remained relatively stable, with high-value ES zones mainly concentrated in the eastern and northern forest-dominated areas. Regarding interactions, strong synergies existed among HQ, CS, and SC, whereas WY exhibited persistent trade-offs with other services, particularly in the central agricultural-urban transitional zone. Furthermore, landscape diversity increased linearly, driven by forest expansion and urban growth. Mechanistically, land use type (LUT) dominated the spatial distribution of WY, HQ, and CS, while slope primarily controlled SC patterns, with all driver interactions demonstrating enhanced effects. By coupling OPGD with GTWR, this study uniquely elucidates the spatiotemporal instability of ES trade-offs/synergies and the spatial heterogeneity of their driving mechanisms, providing a novel scientific basis for implementing spatially differentiated management strategies in large-scale reservoir-impacted regions. Full article

The imperative for EMC-optimized gate drivers in Gallium Nitride (GaN)-based automotive DC-DC converters stems from the stringent CISPR 25 standards and GaN’s intrinsic high-speed switching characteristics, which paradoxically exacerbate electromagnetic interference (EMI). This review distinguishes itself by proposing a novel frequency-domain classification framework (Zone I: <50 MHz for conducted harmonics; Zone II: >50 MHz for switching noise and ringing), which systematically organizes and assesses gate driving techniques against the triad of fundamental GaN EMC challenges: pronounced capacitance nonlinearity, low threshold voltage, and extreme parasitic sensitivity. Unlike prior surveys that primarily catalog techniques, the analysis elevates the gate driver from a simple switch interface to the central “electromagnetic actuator” of the power stage, explicitly elucidating its pivotal role in mediating the critical trade-offs among switching speed, loss, and EMC performance. A comprehensive evaluation and comparison of advanced techniques—from spread-spectrum modulation for Zone I to adaptive current shaping and resonant topologies for Zone II—are provided, alongside an analysis of their design trade-offs. Furthermore, this review presents a first-of-its-kind, phased implementation roadmap towards holistic EMC compliance, integrating intelligent hybrid control, heterogeneous integration, and system-level co-design. This review bridges the gap between device physics and system engineering, offering structured design methodologies and a clear future direction for achieving electromagnetic integrity in next-generation automotive power electronics. Full article

Participation in running events has expanded worldwide, consolidating itself as a form of active leisure and a driver of social and tourism engagement. Although runners’ motivations have been extensively studied, perceived inclusivity, understood as motivation derived from the event’s promotion of equitable participation across gender, age and functional ability, has rarely been examined as a distinct motivational dimension within structural models. This study analyses the motivational structure of participants in the Elche Half Marathon (Spain) and assesses the incremental contribution of inclusivity to traditional motivational frameworks. Based on a sample of 1053 valid responses, a two-stage psychometric and segmentation approach was applied. Exploratory and confirmatory factor analyses (EFA and CFA) were conducted to compare a four-factor model (sport-related hedonism, competition, socialization and digital socialization) with an extended five-factor model incorporating inclusivity. Subsequently, cluster analyses were performed using factor scores derived from each model. The results show that the inclusion of inclusivity improves model fit and increases explained variance, while also generating a more differentiated segmentation structure. The extended model revealed six motivational profiles, some of which displayed continuity with the classical solution, while others were reconfigured when inclusivity was introduced. Overall, the findings indicate that inclusivity functions as a complementary and context-dependent motivational dimension that refines the understanding of participation heterogeneity in running events. Rather than replacing traditional motives, inclusivity contributes incremental explanatory value and enhances the identification of motivational profiles, offering relevant insights for the design and management of mass-participation sporting events. Full article