Search Results (1,340)

Background: Severe fever with thrombocytopenia syndrome (SFTS) has become an increasing public health threat in China, with Yantai City representing a major endemic focus. A fine-scale, long-term epidemiological analysis integrating human case data with vector surveillance is essential for understanding local transmission dynamics. Methods: We conducted a retrospective analysis using 12-year (2013–2024) county-level SFTS surveillance data from Yantai City. Temporal trends were analyzed by Joinpoint regression. Concurrent field surveillance of Haemaphysalis longicornis (2019–2024) was used to quantify local SFTSV infection rates in ticks. Associations between SFTS incidence and environmental/livestock factors were evaluated using Spearman’s correlation and multivariable negative binomial regression. Results: A total of 1964 SFTS cases were reported. The annual incidence rate increased from 0.65 to 5.12 per 100,000 population, with an average annual percentage change (AAPC) of 13.56% 2013–2024, showing the most substantial rise among the elderly. Marked spatial heterogeneity was observed, with county-level mean incidence ranging from 0.30 to 5.23 per 100,000. The SFTSV infection rate in ticks surged from 0.54% in 2019 to 3.24% in 2024, and showed a strong positive correlation with human incidence both seasonally (ρ = 0.998) and across counties (ρ = 0.79), a pattern likely driven by shared environmental factors. Multivariable analysis identified grassland coverage (adjusted IRR [aIRR] = 1.21), woodland coverage (aIRR = 2.31), goat density (aIRR = 1.49), and tick infection rate (aIRR = 1.65) as independent risk factors, while urban land was protective (aIRR = 0.83). The overall case fatality rate was 8.86%, showing a declining trend, but was significantly higher in males (10.90%) than in females (7.04%), particularly among the elderly. Conclusions: SFTS incidence in Yantai increased significantly over the past decade, characterized by a heightened burden on the elderly and strong spatiotemporal clustering. Risk is independently mediated by ecological interfaces, notably woodland/grassland habitats and goat rearing. These findings delineate high-risk areas and populations, offering crucial insights for developing targeted public health strategies. Full article

Understory shrub–grass coverage is a key indicator of forest ecosystem structure and function, and its accurate retrieval via remote sensing is essential for regional ecological assessments. To address the critical limitation in existing multi-angle remote sensing inversion methods: high-resolution images lack angular information while multi-angle datasets suffer from low spatial resolution, making it difficult to achieve large-scale and fine-grained inversion of understory shrub–grass coverage. Here, we developed an inversion method for estimating understory shrub–grass coverage by integrating multi-angle Moderate Resolution Imaging Spectroradiometer data with high-resolution Sentinel-2 imagery to produce 10 m resolution coverage maps; we then used this method to analyze spatiotemporal changes in Changting County from 2016 to 2025. The results demonstrated that the method achieved high accuracy (R² = 0.8418, RMSE = 0.07), meeting the requirements for quantitative understory shrub–grass coverage estimation. Understory shrub–grass coverage exhibited a concentric decreasing pattern from the surrounding mountainous areas toward the central plain, with high-coverage zones concentrated primarily in the west, southwest, and south. Over the 2016–2025 period, understory shrub–grass coverage displayed a fluctuating upward trend: approximately 60% of the study area showed improvement, while 37.73% experienced slight degradation. The change persistence was dominated by positive trends, with an area proportion of 54.14%, which was close to that of the anti-persistent type (44.87%). This study provides technical support for the high-resolution inversion of understory vegetation structure. Full article

Amid the dual pressures of ecological conservation and livelihood sustainability on the Qinghai–Tibet Plateau, investigating the economic effects of herders’ adaptation strategies holds practical relevance. Focusing on grass-based livestock husbandry, this study examines 327 pastoral households in Xinghai County, Qinghai Province, using endogenous switching regression models to empirically analyze the determinants, economic effects, and underlying mechanisms of herders’ production transformation. The main contribution is providing new empirical evidence for understanding herders’ adaptive strategies and informing policy design. The findings reveal that: (1) Transformation decisions are rational choices shaped by household resource endowments. Households with more labor and larger pasture areas are more likely to transform, while non-pastoral employment partially substitutes for such transformation. (2) Production transformation significantly increases herders’ pastoral income. Under the counterfactual framework, the income enhancement effect amounts to 21,509.08 Yuan for the transformed group and 741.30 Yuan for the non-transformed group. Income growth in the transformed group mainly stems from specialized livestock production, whereas the non-transformed group relies more on gradual improvements and policy compensation. (3) Production transformation promotes large-scale breeding without affecting livestock mortality rates. Efficiency gains from transformation are significant only for the transformed group; forcing non-transformers to adopt transformation under current endowments may lead to efficiency losses. These findings suggest that the government should prioritize supporting herders with both the capacity and willingness to transform, address barriers faced by vulnerable groups, and emphasize productivity enhancement and moderate-scale operations to facilitate sustainable income growth. Full article

Understanding the intricate interrelationships among ecosystem services (ESs) is fundamental to advancing sustainable ecological management. This study focuses on the Taihu Basin and examines five representative ESs, including water yield (WY), carbon sequestration (CS), soil retention (SR), habitat quality (HQ), and crop production (CP), for the years 2000, 2010, and 2020. Spatial distribution characteristics and spatiotemporal dynamics were quantified through the combined application of the InVEST model, a food production model, and ArcGIS. Spearman correlation analysis and K-means clustering were then applied to characterize trade-offs and synergies among ESs and to delineate ecosystem service bundles at multiple spatial scales, including 1 km × 1 km grids, 10 km × 10 km grids, and the county level, while GeoDetector was used to identify the associated driving mechanisms. The results indicated that (1) between 2000 and 2020, the spatial distribution pattern of the ESs in the Taihu Basin underwent significant changes, with WY and SR increasing by 48.97% and 51.89%, respectively, while HQ, CS, and CP decreased by 17.2%, 15.5%, and 47.6%. (2) From an overall perspective of trade-offs and synergies, the interactions among ESs shifted from trade-offs (r < 0) to synergies (r > 0) as the scale increased. From the perspective of the spatial characteristics of trade-offs and synergies, the intensity of these interactions varied significantly with increasing scale, but the trend remained relatively stable. (3) The Taihu Basin can be categorized into six ES bundles (ESBs). ESB 1, ESB 3, ESB 4, and ESB 5 have relatively stable ES structures, whereas ESBs 2 and 6 display significant variations. (4) The primary factors influencing ESs vary significantly across different spatial scales, with land use/land cover (LULC) and the proportions of arable land, forestland, and buildings exhibiting strong explanatory power. This highlights the critical role of coupled natural and anthropogenic processes in shaping the spatial patterns of ESs. This study considers the spatiotemporal variation and scale dependence of ecosystem services, providing management recommendations tailored to different regions and spatial scales, and offering a scientific basis for regional ecological planning and watershed governance. Full article

Invasive alien plants (IAPs) pose a severe and escalating threat to biodiversity and ecosystem services in China. However, a systematic nationwide assessment that identifies invasion hotspots, quantifies their overlap with protected area networks, and pinpoints critical conservation gaps is still lacking. This hinders the development of spatially targeted management strategies. To address this, we developed an integrated analytical framework coupling the Maximum Entropy (MaxEnt) model with the InVEST habitat quality model. Using a high-resolution, county-level distribution database of 293 IAPs, we mapped potential species richness and habitat degradation across China. The geo-detector model was further employed to identify the primary environmental factors and their interactions. Spatial overlay analysis was conducted to delineate core invasion habitats (areas of high invasion suitability and high degradation) and assess their coverage within China’s national nature reserves. Nighttime light intensity (DMSP, 34.39%), annual precipitation (Bio12, 14.16%), and mean diurnal range (Bio2, 11.82%) were the factors with the highest contribution in the model, highlighting the statistical interaction between anthropogenic pressure and climatic conditions. The core invasion habitat spanned 20.10 × 10⁴ km², predominantly (66.04%) concentrated in high-intensity human disturbance zones. Notably, only 11.18% of this core habitat falls within existing national nature reserves, revealing a vast conservation gap of 17.85 × 10⁴ km². Our results indicate a profound spatial mismatch between invasion hotspots and the current protected area network in China. We prioritize southeastern coastal urban agglomerations-characterized by high anthropogenic pressure (DMSP), high precipitation (Bio12), and low diurnal temperature range (Bio2)-for immediate monitoring and intervention. This integrated assessment provides a national-scale, spatially explicit prediction of invasion risk for 293 plant species in China, and offers an evidence-based decision-support tool for optimizing invasive species management and biodiversity conservation. Full article

This study explored the spatial relationship between frequent physical distress (FPD) and socioeconomic as well as health-related factors across the contiguous United States. FPD, defined as having 14 or more physically unhealthy days within the past month, serves as an important measure of overall population health. While many studies have examined the causes of mental distress, research on the geographic variation and social context of physical distress remains limited. Using data from 2673 U.S. counties, this study analyzed how socioeconomic conditions and health indicators relate to FPD at both national and regional levels. Ordinary Least Squares (OLS) multivariate regression model was first used to assess general associations, followed by Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) to identify spatially varying and scale-dependent relationships. Comparing the GWR and MGWR results revealed that several predictors of FPD operate at different spatial scales, reflecting local heterogeneity in health outcomes. Counties in the southeastern United States, particularly those with higher levels of socioeconomic disadvantage and poorer health conditions, showed elevated FPD rates. These findings highlight the importance of accounting for spatial context when addressing physical distress and suggest that locally tailored public health strategies may be more effective than uniform national approaches. Full article

Against the macro-background of balancing development and food security strategies, China has implemented a land-use regulation system centered on farmland protection. However, the economic impacts of such regulation lack sufficient quantitative evaluation. Using farmland retention targets at the county-level in the administrative region and combining them with relevant data, this study employs an Intensity Difference-in-Differences (Intensity DID) approach to examine how land-use regulation affects county-level economic growth and convergence. The findings reveal a U-shaped relationship between land-use regulation and county-level economic growth, suggesting that, at the current stage, the intensity of land-use regulation generally promotes economic growth. Heterogeneity analysis further indicates that county economies in major grain production areas (MGPAs) and main grain-producing counties (MGPCs) experience stronger negative constraints related to the policy, while MGPCs in non-major grain production areas (non-MGPAs) are most sensitive to land-use regulation. China’s county economies exhibit convergence; however, land-use regulation may reduce the growth rate of counties that were underdeveloped in the base period, thereby widening inter-county development disparities. This divergence is manifested in the lack of convergence between the clubs of MGPCs and non-MGPCs. Mechanism analysis suggests that differences in industrial structure, capital investment, and fiscal expenditure constitute the key focal points for addressing the issue. Policy implications indicate that China should strengthen land-use regulation on the premise of rationally determining the functions and scale of various land types, continue to advance market-oriented reforms of land factors, improve the vertical and horizontal interest compensation mechanism for MGPAs, and stimulate the endogenous development momentum of these regions. Full article

This study aims to address gaps in understanding healthcare accessibility inequality in rural China, where traditional distance-based assessments and urban-centric biases are insufficient. By integrating real-time travel data from Amap and the two-step floating catchment area (2SFCA) method, we conducted a high-resolution (1 km grid) analysis across transportation modes, administrative scales, and time-sensitive populations. Results reveal that driving enables more stable, equitable access (characterized by higher supply–demand ratios and lower variability) than public transport, which distorts ratios due to limited coverage. Accessibility disparities are most pronounced at the county scale, with eastern rural counties (e.g., Yangtze River Delta) showing far higher accessibility (log₁₀(A-value) > 5.0) than remote western counties (log₁₀(A-value) < 1.5). High time-sensitive populations (urgent care) face extreme accessibility gaps, with only 15% of counties providing optimal access. In contrast, low time-sensitive groups benefit from extended travel time thresholds, achieving 62% coverage of optimal access. Targeted interventions—investing in rural high-tier hospitals, enhancing transit frequency, and county-specific policies—are needed to advance health equity. The findings of this study provide the first nationwide high-resolution healthcare accessibility map for rural China, improve assessment accuracy via real-time data, and identify county-level gaps—offering data-driven insights for targeted policies to advance health equity and support rural revitalization. Full article

Carbon emissions (CEs) are a primary driver of global climate change, particularly pronounced in China’s Yangtze River Delta (YRD) region, where rapid economic development and urbanization have led to a substantial increase in CEs. At fine spatial scales (e.g., county level) or in regions with limited statistical data, traditional methods for CE accounting are constrained by data gaps and inconsistencies, which hinders the accurate characterization of regional disparities. Therefore, this study proposes a CE spatial downscaling method based on nighttime light (NTL) data. By integrating remote sensing data with the IPCC emission inventory model, energy consumption-related carbon emissions (ECCEs) across the YRD region from 2000 to 2020 were quantified. Through global spatial autocorrelation analysis and standard deviation ellipse (SDE) analysis, the spatial distribution characteristics and temporal variation trends of ECCEs were revealed. Results indicate that total CEs increased significantly over the study period. CE hotspots were concentrated in the Hangzhou Bay area and the Shanghai–Nanjing corridor, while coldspots were identified in southwestern Anhui and Zhejiang. From 2010, the CE centroid shifted toward the southwest or northwest, and the regional CE distribution evolved from a point pattern to a band-shaped pattern. These findings offer a novel approach for CE monitoring and can provide scientific support for low-carbon development policies and precise emission reduction strategies in data-scarce regions of developing countries. Full article

Peer relationships are closely associated with the academic performance of adolescent students. This paper develops an integrated framework taking the peer main effect as the starting point to systematically incorporate the demonstration effect, the within-class network effect, and the cross-class average effect. Using comprehensive network and survey data from high school students in a typical county in western China, this paper employs a network-based identification strategy to reveal robust positive peer effects. The mechanism test shows that the demonstration effect can play a moderating role in peer effects on academic performance, with the network effect being heterogeneous across students and the average effect indicative of a potential role in providing diverse academic information. These findings provide empirical insights into the multifaceted nature of peer dynamics, offering actionable evidence for designing targeted interventions to improve educational outcomes in underdeveloped regions. Full article

Addressing meso-scale sensing voids and resource misallocations, this study constructs an integrated “Performance Sensing–Bottleneck Diagnosis–Configuration Identification” framework to evaluate the spatiotemporal evolution of resilience across Xi’an’s districts (2018–2023). This research operationalizes a diagnostic-driven analytical pipeline coupling multi-source parameters with the CRITIC method to complement static stock accounting with dynamic performance sensing. This logic integrates Dagum Gini decomposition to pinpoint spatiotemporal bottlenecks and fuzzy-set QCA (fsQCA) to uncover driving pathways, utilizing an “Obstacle–Correlation” matrix to provide an objective basis for antecedent selection. The results show the following: (1) A “V-shaped” spatiotemporal trajectory and 2020 “resilience inversion” (dipping to 0.364) highlight the sensitivity of dynamic performance sensing in exposing latent vulnerabilities. (2) Persistent “center-periphery” gradients exist, with administrative siphoning driving 66.7% of inequality; diagnosis identifies distinct spatiotemporal pathologies: rigid spatial constraints in urban cores versus service imbalances in expansion zones. (3) Three equifinal pathways and an “asymmetric cancellation” effect prove that resilience hinges on configurational fit rather than linear stacking, where extreme single-dimension shortfalls neutralize collective gains. By bridging situational pathologies and governance pathways, this framework provides a robust empirical basis for the refined allocation of resources in complex environments. Full article

Aeroponic cultivation can enhance resource-use efficiency, yet field-scale evidence for closed-loop water recycling remains limited. This study assessed a multi-tier aeroponic system for Pak choi, Brassica rapa subsp. chinensis, integrated with a recovery, filtration, ultraviolet sterilization, and recirculation module under practical operating conditions in Yunlin County, Taiwan. System performance was quantified using water consumption under recycling and non-recycling configurations, electricity use, crop growth, yield, and resource-use efficiencies. Closed-loop operation reduced external freshwater input from 27,000 L to 7000 L, corresponding to a 74% reduction, and decreased water use from 2.8 to 0.95 L plant⁻¹. Electricity consumption over the cultivation cycle was 68.9 kWh, equivalent to 2.46 kWh day⁻¹. With a planting density of 44 plants m⁻², yield reached 2657.6 g m⁻² and exceeded the soil reference benchmark of 1644 g m⁻² used for contextual comparison. Water-use efficiency was 63.8 g L⁻¹, and nutrient-use efficiency was 35.4 g mL⁻¹ of fertilizer stock added. Nutrient solution pH remained stable between 6.69 and 6.99, while electrical conductivity was adjusted by growth stage. The findings indicate that field-deployed closed-loop aeroponics can markedly reduce freshwater demand while sustaining high productivity, and they identify transplant acclimation and improved pH control as priorities for enhancing survival and consistency. Full article

With the intensification of climate change, deepening energy transition, and increasing social vulnerability, extreme power outage events pose escalating challenges to the governance capacity of modern power systems. Existing evaluation frameworks primarily focus on engineering reliability and economic loss estimation, lacking systematic quantification of the governance complexity arising from multidimensional interacting pressures behind outage events. This creates a blind spot in both theoretical research and governance practice, hindering differentiated resilience decision-making. To address this gap, this study develops a four-dimensional evaluation framework of power outage governance complexity encompassing event attributes, external environment, internal system, and social impacts. Based on county-level outage data and multi-source auxiliary data in the United States from 2015 to 2024 and employing the XGBoost–SHAP interpretable machine learning approach, we construct the Power Outage Complexity Index (POCI) for all U.S. counties and systematically analyze its spatiotemporal evolution and core driving factors. The results show that outage governance complexity in the U.S. power grid exhibits a significant upward trend during 2015–2024, with an average annual growth rate of 1.84%. Spatially, significant positive autocorrelation is observed, and 146 high-complexity hotspot counties are identified, mainly clustered along the East and West Coasts, the Gulf Coast, and the Southwest. Driver analysis reveals that social impact and event attribute dimensions together account for nearly 90% of the variance in complexity, with cumulative outage exposure burden, outage frequency, and large-scale event ratio being the most critical drivers. Theoretically, this study extends power resilience research from an engineering-physical paradigm to a socio-technical governance paradigm and provides a reproducible methodological framework for assessing governance complexity in critical infrastructure systems. Practically, the POCI can serve as a governance diagnostic tool for the power industry and regulators, supporting resilience investment prioritization, emergency resource optimization, and differentiated governance strategy formulation. It also provides empirical evidence for safeguarding energy security in highly vulnerable communities and promoting energy resilience equity. Full article

The transition from lower to upper secondary education is a critical developmental stage, requiring decisions with long-term academic and professional consequences. Addressing a gap in evidence that often treats counselling, family educational capital, and place of residence separately, this study examines how these factors jointly relate to students’ high school track/profile choice and their intention to pursue higher education in the Romanian educational transition. Using a standardized questionnaire, we conducted a cross-sectional survey of 1392 lower secondary students (aged 13–14) from Brașov County, Romania, to map preferred tracks, influencing factors, perceptions of high school, and the values framing decision-making. High school track/profile choice emerged as a central “decision node”, strongly associated with participation in counselling p < 0.001; Cramer’s V = 0.678) and significantly related to parents’ educational level and university intentions. Substantial urban–rural differences were observed in track/profile choice (p < 0.001; V = 0.442), with urban students selecting the “real” track more frequently (≈68%) than rural students (≈37%). University intention was high overall, with a small but significant urban–rural difference (≈89.7% vs. ≈86.9%; p = 0.028; V = 0.072). Findings support integrating counselling into coherent adolescent career development models and expanding services to reduce contextual disparities through stronger school–family–community partnerships. This evidence is relevant for education policy and practice by supporting the scaling of school-based career guidance and targeted measures to reduce rural–urban disparities. Full article

Law enforcement officers experience substantial occupational stressors that increase vulnerability to food addiction and cardiovascular disease (CVD), which may be compounded by adverse local environments. This study examined associations among county-level environmental factors, food addiction, and cardiometabolic profiles among North Carolina law enforcement officers. Participants included 330 officers (mean age = 37.98 years; mean BMI = 30.53 kg/m²) who completed the Yale Food Addiction Scale 2.0 and underwent assessments of anthropometrics, blood pressure, blood lipids, and glucose. County-level Food Environment Index (FEI) scores and counts of fast-food restaurants, recreation and fitness facilities, and crime events were obtained from public data sources. Comparative analyses evaluated differences by county type and region, and BMI- and sex-adjusted regression models assessed associations among environmental factors, food addiction symptoms, and cardiometabolic profiles. Rural counties had significantly poorer FEI scores than suburban and urban counties, and rural officers reported the highest food addiction symptoms. Lower FEI scores were significantly associated with greater food addiction symptoms, which were, in turn, associated with higher adiposity and lower triglyceride levels. The findings support associations between food addiction and CVD risk, while underscoring potential influences of food environments on food addiction, warranting further investigation using more precise and up-to-date measures. Full article