Search Results (10,495)

Urban forests and green spaces are increasingly promoted as Nature-Based Solutions (NbS) to mitigate climate risks, enhance human well-being, and support resilient and sustainable cities. Focusing on the theme of optimizing urban green space ecosystem services to foster resilient and sustainable cities, this study systematically analyzes 861 relevant publications indexed in the Web of Science Core Collection from 2005 to 2025. Using bibliometric analysis and scientific knowledge mapping methods, the research examines publication characteristics, spatial distribution patterns, collaboration networks, knowledge bases, research hotspots, and thematic evolution trajectories. The results reveal a rapid upward trend in this field over the past two decades, with the gradual formation of a multidisciplinary knowledge system centered on environmental science and urban research. China, the United States, and several European countries have emerged as key nodes in global knowledge production and collaboration networks. Keyword co-occurrence and cluster analyses indicate that research themes are mainly concentrated in four clusters: (1) ecological foundations and green process orientation, (2) nature-based solutions and blue–green infrastructure configuration, (3) social needs and environmental justice, and (4) macro-level policies and the sustainable development agenda. Overall, the field has evolved from a focus on ecological processes and individual service functions toward a comprehensive transition emphasizing climate resilience, human well-being, and multi-actor governance. Based on these findings, this study constructs a knowledge ecosystem framework encompassing knowledge base, knowledge structure, research hotspots, frontier trends, and future pathways. It further identifies prospective research directions, including climate change adaptation, integrated planning of blue–green infrastructure, refined monitoring driven by remote sensing and spatial big data, and the embedding of urban green space ecosystem services into the Sustainable Development Goals and multi-level governance systems. These insights provide data support and decision-making references for deepening theoretical understanding of Urban Green Space Ecosystem Services (UGSES), improving urban green infrastructure planning, and enhancing urban resilience governance capacity. Full article

Understanding residents’ perceptions of ecosystem services (ES) and ecosystem disservices (EDS) is crucial for protected areas governance. This study, conducted in China’s Three-River-Source National Park (TNP), employed participatory rural appraisal and household questionnaires to examine local cognitive patterns of ES and EDS, along with their socio-spatial heterogeneity and perceived synergies and trade-offs among them. The key findings are as follows: (1) Cultural services received the highest scores, followed by regulating services, whereas provisioning services, especially food provisioning, were rated as relatively inadequate. Safety threats were considered the most severe EDS. Overall, a Matthew Effect emerged: services with high current perception scores showed an improving trend, while those with low scores deteriorated. (2) Spatially, ES/EDS evaluation scores exhibited a “core zone < general control zone < peripheral zone” gradient. Socio-demographic and economic factors also influenced residents’ perceptions; women and the elderly were especially more concerned about food and energy supply shortages and safety issues. (3) The relationships among the various ES and EDS are primarily synergistic rather than trade-offs. Specifically, gains in regulating services were associated with enhanced cultural services, while declines in provisioning services and intensified safety threats coincided with the deterioration of material EDS. These findings offer a scientific basis for managing protected areas in high-altitude, ecologically fragile regions and provide practical insights for balancing ecological conservation with community development. Full article

The “Anthropocene” has witnessed unprecedented challenges to the sustainability of human development. Resolving the contradiction between humans and land and achieving coordinated development has become a pressing issue across many disciplines in the era of ecological civilization. This study adopts an ecological perspective to investigate the symbiotic relationship between humans and land in the “Five Poles” urban agglomerations of the Yellow River Basin. In this framework, ecosystem service value and human well-being are employed to quantify “human” and “land,” respectively. The Lotka–Volterra model is then applied as a structural analogy to quantify the dynamic interactions within this symbiotic relationship, treating ecosystem service value and human well-being as two interdependent systems with feedback mechanisms. For the “Five Poles” urban agglomerations in the Yellow River Basin, the ecosystem service and human well-being pressures, along with the symbiosis indices for the period 2011–2020, were calculated and categorized. The results were first subjected to a visual analysis to describe the spatial patterns. Subsequently, spatial autocorrelation analysis was employed to quantitatively investigate the clustering and heterogeneity of these patterns, thereby systematically elucidating the spatial characteristics of human–land symbiosis in the Yellow River Basin. The findings indicate that: (1) the human–land relationship in the Yellow River Basin has evolved from partial interaction to mutualism, reflecting improved coordination within the regional human–land system. (2) The evaluation of this relationship improved between 2011–2015 and 2016–2020. (3) High-evaluation areas have shifted from east to west, exhibiting distinct agglomeration characteristics. Full article

This study is situated at the critical stage of comprehensive implementation of China’s territorial spatial planning system, addressing the strategic need for planning evaluation and optimization. We innovatively construct a Computable General Equilibrium Model for China’s Territorial Spatial Planning (CTSPM-CHN) that integrates dual factors of construction land costs and energy consumption costs. Through designing two policy scenarios of rigid constraints and structural optimization, we systematically simulate and evaluate the dynamic impacts of different territorial spatial governance strategies on macroeconomic indicators, residents’ welfare, and carbon emissions, revealing the multidimensional effects and operational mechanisms of territorial spatial planning policies. The findings demonstrate the following: First, strict implementation of land use scale control from the National Territorial Planning Outline (2016–2030) could reduce carbon emission growth rate by 12.3% but would decrease annual GDP growth rate by 0.8%, reflecting the trade-off between environmental benefits and economic growth. Second, industrial land structure optimization generates significant synergistic effects, with simulation results showing that by 2035, total GDP under this scenario would increase by 4.8% compared to the rigid constraint scenario, while carbon emission intensity per unit GDP would decrease by 18.6%, confirming the crucial role of structural optimization in promoting high-quality development. Third, manufacturing land adjustment exhibits policy thresholds: moderate reduction could lower carbon emission peak by 9.5% without affecting economic stability, but excessive cuts would lead to a 2.3 percentage point decline in industrial added value. Based on systematic multi-scenario analysis, this study proposes optimized pathways for territorial spatial governance: the planning system should transition from scale control to a structural optimization paradigm, establishing a flexible governance mechanism incorporating anticipatory constraint indicators; simultaneously advance efficiency improvement in key sector land allocation and energy structure decarbonization, constructing a coordinated “space–energy” governance framework. These findings provide quantitative decision-making support for improving territorial spatial governance systems and advancing ecological civilization construction. 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

Graphene oxide (GO) is a carbon-based nanomaterial explored for agricultural and forestry uses, but plant responses are strongly subject to both the dose and the route of exposure. We summarized recent studies with defined graphene oxide (GO) exposures by seed priming, foliar delivery, and root or soil exposure, while comparing annual crops with woody forest plants. Mechanistic progress points to a shared physicochemical basis: surface oxygen groups and sheet geometry reshape water and ion microenvironments at the soil–seed and soil–rhizosphere interfaces, and many reported shifts in antioxidant enzymes and hormone pathways likely represent downstream stress responses. In crops, low-to-moderate doses most consistently improve germination, root architecture, and tolerance to salinity or drought stress, whereas high doses or prolonged root exposure can cause root surface coating, oxidative injury, and photosynthetic inhibition. In forest plants, evidence remains limited and often relies on seedlings or tissue culture. For forest plants with long life cycles, processes such as soil persistence, aging, and multi-seasonal carry-over become key factors, especially in nurseries and restoration substrates. The available data indicate predominant root retention with generally limited root-to-shoot translocation, so residues in edible and medicinal organs remain insufficiently quantified under realistic-use patterns. This review provides a scenario-based framework for crop- and forestry-specific safe-dose windows and proposes standardized endpoints for long-term fate and ecological risk assessment. Full article

This study investigates the influence of varying cold stratification durations (0–165 days) on the germination performance and early seedling development of Rudbeckia fulgida. Seeds were divided into 11 groups at 15-day intervals, using a total of 1320 seeds. For each stratification duration, an equivalent number of seeds stored at room temperature served as non-stratified controls. Results demonstrated a clear and significant increase in germination percentage with longer stratification periods (Kruskal–Wallis, H = 57.03, p < 0.001), with the highest germination observed at 135 and 165 days (96.7%). In contrast, seeds kept at room temperature exhibited low and inconsistent germination. Strong positive correlations were detected between stratification duration and both germination percentage (r = 0.914) and post-stratification seed weight (r = 0.419). Furthermore, a Response Surface Methodology (RSM) model was developed to predict germination behavior, achieving an exceptionally high 99% predictive accuracy. The RSM analysis confirmed that cold stratification duration is the dominant factor shaping germination responses in Rudbeckia fulgida Aiton. Overall, the study demonstrates that cold stratification is essential for breaking seed dormancy in R. fulgida, substantially improving propagation efficiency and offering valuable insights for nursery production, landscape practices, and restoration ecology. Full article

Environmental sustainability has emerged as a global imperative in the context of accelerating climate change, rapid industrialization, and increasing ecological stress. Ecological quality is necessary for countries to pursue because of its overall benefits to the entire ecosystem. Therefore, due to the significant role that the United Arab Emirates (UAE) plays in the global environment, this research examines the role of Green Innovation (GI), Financial Globalization (FG), Economic Growth (GDP), and Foreign Direct Investment (FDI) in influencing Environmental Quality (EQ) in the UAE from 1991–2022. The UAE is well known for these economic indices. Furthermore, this study employed the innovative Quantile Augmented Dickey–Fuller (QADF) test, Wavelet Quantile Regression (WQR), Wavelet Quantile Correlation (WQC), and Quantile-on-Quantile Granger Causality (QQGC). WQR is able to identify connections between series over a range of quantiles and periods. WQC evaluates the co-movement between variables at different quantile levels and across several scales. The QQGC captures the causal effect of the regressors on EQ. These methods are quite advanced compared to other traditional econometric methods. Based on the outcome of the WQR and WQC methods, evidence shows that GI contributes to EQ across all quantiles in the short, medium, and long term, while FG, GDP, and FDI reduces EQ across all quantiles in the short, medium, and long term. The QQGC results also affirm causality among the variables, implying that GI, FG, GDP, and FDI can predict EQ across all quantiles. This research recommends that the UAE should improve on its environmental policies both domestically and internationally by making them more stringent, and continue to promote clean energy investments. 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

China’s Green Finance Reform and Innovation Pilot Zones (GFRIPZ) policy has emerged as a central instrument for promoting sustainable urban development and strengthening Urban Ecological and Environmental Resilience (UEER). However, systematic evidence on its actual effectiveness remains scarce. This study applies a difference-in-differences (DID) model to panel data for 279 Chinese cities from 2011 to 2022 to identify the causal impact of the GFRIPZ policy on UEER and to examine its transmission mechanisms and heterogeneity. Specifically, we incorporate green innovation efficiency and environmental regulation intensity to test the technological and regulatory channels through which green finance operates. The empirical results show that: (1) the GFRIPZ policy significantly improves UEER, and this finding is robust across a range of alternative specifications and robustness checks. (2) Green innovation efficiency and environmental regulation intensity serve as key mechanisms through which the policy enhances UEER. (3) The policy effect is stronger in eastern cities, megacities, small cities, and non-resource-based cities, while it is relatively weaker in central and western cities, medium-sized cities, and resource-based cities. These findings provide additional empirical evidence to inform the refinement and further advancement of the GFRIPZ policy and offer evidence-based implications for urban green development strategies. Full article

Pollen identification is critical for melissopalynology (honey authentication), ecological monitoring, and allergen tracking, yet manual microscopic analysis remains labor-intensive, subjective, and error-prone when multiple grains overlap in realistic samples. Existing automated approaches often fail to address multi-grain scenarios or lack systematic comparison across classical and deep learning paradigms, limiting their practical deployment. This study proposes a subinstance-based classification framework combining YOLOv12n object detection for grain isolation, independent classification via classical machine learning (ML), convolutional neural networks (CNNs), or Vision Transformers (ViTs), and majority voting aggregation. Five classical classifiers with systematic feature selection, three CNN architectures (ResNet50, EfficientNet-B0, ConvNeXt-Tiny), and three ViT variants (ViT-B/16, ViT-B/32, ViT-L/16) are evaluated on four datasets (full images vs. isolated grains; raw vs. CLAHE-preprocessed) for four berry pollen species (Ribes nigrum, Ribes uva-crispa, Lonicera caerulea, and Amelanchier alnifolia). Stratified image-level splits ensure no data leakage, and explainable AI techniques (SHAP, Grad-CAM++, and gradient saliency) validate biological interpretability across all paradigms. Results demonstrate that grain isolation substantially improves classical ML performance (F1 from 0.83 to 0.91 on full images to 0.96–0.99 on isolated grains, +8–13 percentage points), while deep learning excels on both levels (CNNs: F1 = 1.000 on full images with CLAHE; ViTs: F1 = 0.99). At the instance level, all paradigms converge to near-perfect discrimination (F1 ≥ 0.96), indicating sufficient capture of morphological information. Majority voting aggregation provides +3–5% gains for classical methods but only +0.3–4.8% for deep models already near saturation. Explainable AI analysis confirms that models rely on biologically meaningful cues: blue channel moments and texture features for classical ML (SHAP), grain boundaries and exine ornamentation for CNNs (Grad-CAM++), and distributed attention across grain structures for ViTs (gradient saliency). Qualitative validation on 211 mixed-pollen images confirms robust generalization to realistic multi-species samples. The proposed framework (YOLOv12n + SVC/ResNet50 + majority voting) is practical for deployment in honey authentication, ecological surveys, and fine-grained biological image analysis. Full article

Background/Objectives: Despite large-scale measles and rubella (MR) vaccination campaigns in West Africa, measles outbreaks persist, raising concerns about campaign effectiveness, coverage, and underlying determinants. This study assesses the impact of MR follow-up campaigns in 12 of 17 West African countries (2024–2025) and examines the factors contributing to post-campaign outbreaks. The main objective of this study is to evaluate the impact of MR campaigns on measles transmission, identify the characteristics of post-campaign outbreaks, and propose strategies to improve campaign effectiveness and accelerate progress toward measles elimination in West Africa. Methods: We conducted a cross-sectional and ecological analytical study to examine spatial and temporal variations based on measles surveillance data from 2024 to 2025, post-campaign coverage surveys (PCCS), district-level outbreak reports, and administrative coverage reports. Trends in measles cases before and after the MMR campaigns were assessed, along with demographic characteristics and spatial analyses of confirmed cases. Results: In 2024, 70.5% (12/17) of countries conducted measles vaccination campaigns, but measles outbreaks increased in 2025 (64 districts in 2024 versus 383 in 2025). Children under five remained the most affected (54%), with 85% of cases being either unvaccinated (57%) or of unknown status (28%). Administrative coverage exceeded 95% in most countries, but measles PCCS revealed gaps, with only Senegal (93%) and Guinea-Bissau (94%) achieving high verified coverage. No country achieved 95% national MPCC. Conclusions: Suboptimal campaign quality, gaps in immunity beyond target age groups, and unreliable administrative data contributed to the persistence of outbreaks. Recommendations include extending Measles vaccination campaigns to older children (5–14 years), improving preparedness by drawing on experiences from other programs such as polio, standardizing PCCS data survey and analysis methodologies across all countries, and integrating Measles vaccination campaigns with other services such as nutrition. Full article

Analyzing the composition and structure of the gut bacterial community in Antheraea pernyi is essential for improving its economic traits, as well as for understanding gut bacteria–host interactions in lepidopteran insects. This study utilized the Illumina MiSeq PE 300 platform to conduct 16S rRNA gene sequencing for a comparative analysis of gut bacterial community in laboratory-reared and field-released (spring and autumn) Antheraea pernyi larvae of the same strain. The study revealed the specific effects of rearing environment and seasonal variation on the structural and functional dynamics of the larval gut bacterial communities. The composition of the dominant gut bacteria varied significantly with rearing environment and season. Laboratory-reared and spring field-released groups exhibited similar bacterial community structures, whereas the autumn field-released group showed a significant trend toward specialization, characterized by enrichment of specific bacterial taxa. Linear discriminant analysis effect size identified statistically significant biomarkers across samples. Taxonomic analysis revealed that Actinomycetota, Actinobacteria, Mycobacteriales, Dietziaceae, and Dietzia were characteristic of the gut bacteria profile in spring field-released, Lactobacillales, Enterococcaceae, and Enterococcus were enriched in the autumn field-released group, and the laboratory-reared group exhibited a relative dominance of Alphaproteobacteria. Functional prediction indicated that gut bacterial community structure likely influences its metabolic potential, which may suggest an adaptive response of the Antheraea pernyi to distinct ecological environments. This study provides important insights into the highly complex nature of insect-microbe interactions. Full article

Land consolidation requires reliable and objective land valuation to ensure transparency and fairness in the reallocation process. This study introduces a data-driven method for assessing agricultural site productivity based on vegetation indices derived from multispectral imagery, supported by Sentinel satellite data and validated using handheld chlorophyll meter measurements. Site productivity, defined as the land’s ability to generate yield and biological value, is determined by natural and environmental factors that directly influence economic worth. Vegetation indices (NDVI, SAVI) obtained from UAV imagery showed a strong correlation with chlorophyll content, confirming the reliability of this non-invasive assessment. The analysis, conducted in Poland and Slovakia, demonstrated the method’s applicability under two different land consolidation systems: a market-based model in Poland and an ecologically oriented approach in Slovakia. The proposed framework proved easy to implement and provided consistent results even without the use of ground control points. By reducing fieldwork time and costs while improving valuation accuracy, this method enhances the objectivity and transparency of land consolidation procedures. The findings confirm the potential of vegetation indices to support data-driven and environmentally informed land valuation across diverse consolidation contexts. Full article

Environmental element monitoring is essential for assessing environmental quality, identifying pollution sources, evaluating ecological risks, and understanding long-term contamination trends. Modern monitoring campaigns routinely generate large volumes of complex data that require advanced analytical strategies. This study applied chemometric techniques to analyze elements and BVOCs (biogenic volatile organic compounds) measured from Posidonia oceanica and related environmental matrices (seawater, sediment, and rhizomes) during three sampling campaigns in the Tremiti Islands (Italy). Twenty-two trace elements were quantified, and BVOC profiles were obtained from the leaf samples. The dataset was analyzed using a combination of univariate visualizations, unsupervised and supervised multivariate techniques, and multi-way methods. PCA (Principal Component Analysis) and PLS-DA (Partial Least Squares-Discriminant Analysis) revealed distinct spatial (leaf section) and temporal (sampling period) trends, supported by consistent elemental markers. A low-level data fusion approach integrating BVOC and element data improved group discrimination and interpretability. PARAFAC (PARAllel FACtor analysis) applied to a three-way array successfully separated background trends from meaningful compositional changes, uncovering latent structures across chemical, spatial, and temporal dimensions. This work illustrates the usefulness of chemometrics in environmental monitoring and the effectiveness of combining multivariate tools and data fusion to improve the interpretability of complex environmental datasets. The methodology used in this study is fully generalizable and applicable to other environmental multi-way datasets. Full article