Search Results (2,601)

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Temperature fluctuations caused by climate change and global warming pose a threat to fish. The burbot (lota lota) population is particularly sensitive to increased water temperature, but the systematic impacts of high-temperature exposure on their liver and intestinal health remain unclear. In January of 2025, we collected wild adult burbot individuals from the Ussuri River (water temperature: about 2 °C), China. The burbot were exposed to 2 °C, 7 °C, 12 °C, 17 °C, and 22 °C environments for 96 h; then, the liver and intestinal contents were subsequently collected for histopathology observation, immunohistochemistry, biochemical index assessment, and transcriptome/16S rDNA sequencing analysis. There was obvious liver damage including hepatocyte necrosis, fat vacuoles, and cellular peripheral nuclei. Superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) activities were elevated and subsequently decreased. Additionally, the malondialdehyde (MDA) level significantly increased with increasing temperature. These results indicate that 7 °C (heat stress temperature), 12 °C (tipping point for normal physiological metabolism status), 17 °C (tipping point for individual deaths), and 22 °C (thermal limit) are critical temperatures in terms of the physiological response of burbot during their breeding period. In the hepatic transcriptome profiling, 6538 differentially expressed genes (DEGs) were identified, while KEGG enrichment analysis showed that high-temperature stress could affect normal liver function by regulating energy metabolism, immune, and apoptosis-related pathways. Microbiomics also revealed that acute heat stress could change the intestinal microbe community structure. Additionally, correlation analysis suggested potential regulatory relationships between intestinal microbe taxa and immune/apoptosis-related DEGs in the liver. This study revealed the potential impact of environmental water temperature changes in cold habitats in winter on the physiological adaptability of burbot during the breeding period and provides new insights for the ecological protection of burbot in the context of global climate change and habitat warming. Full article

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups—representing seven cultivars and different ripening or size stages—were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton’s Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Tornado outbreaks can cause substantial damage, injuries, and fatalities, highlighting the need to understand their characteristics for assessing present and future risks. However, global climate models (GCMs) lack the resolution to explicitly simulate tornado outbreaks. As an alternative, researchers examine large-scale atmospheric ingredients that approximate tornado-conducive environments. Building on this approach, we tested whether patterns of covariability between WMAXSHEAR and 500-hPa geopotential height anomalies, previously identified in ERA5 reanalysis, could approximate major U.S. May tornado outbreaks in a GCM. We developed a proxy-based methodology by systematically testing pairs of thresholds for both variables to identify the combination that best reproduced the leading pattern selected for analysis. These thresholds were then applied to simulations from the high-resolution MPI-ESM1.2-HR model to assess its ability to reproduce the original pattern. Results show that the model closely mirrored the observed tornado outbreak pattern, as indicated by a low normalized root mean square error, high spatial correlation, and similar distributions. This study demonstrates a replicable approach for approximating tornado outbreak patterns, applied here to the leading pattern, within a GCM, providing a foundation for future research on how such environments might evolve in a warming climate. Full article

Solid biomass (agro-residue) is the most important source of renewable energy. The accelerating impacts of climate change and global population growth contribute to air pollution through the use of fossil fuels. These processes increase the demand for energy. The European Union has adopted a climate action plan to address the above challenges. The main aim of this study was to assess the economic performance of the producers of biomass for energy generation in Poland. The detailed objectives were to determine land resources in the studied agricultural farms and to determine the value of fixed and current assets in the analyzed farms. We used questionnaires as the main method to collect data. Purposive sampling was used to choose the farms. We conducted various tests to analyze the revenues from biomass sales and their normality, such as the Dornik–Hansen test, the Shapiro–Wilk test, the Liliefors test, and the Jargue–Berra statistical test. Moreover, we conducted regression analysis to find factors that are the basis for the economic performance (incomes) of farms that sell biomass. Results: This study demonstrated that biomass sales had a minor impact on the performance of agricultural farms, but they enabled farmers to maintain their position on the market. The economic analysis was carried out on a representative group of Polish agricultural farms, taking into account fixed and current assets, land use, production structure, and employment. The findings indicate that a higher income from biomass sales was generally associated with better economic results per farm and per employee, although not always per hectare of land. This suggests that capital intensity and strategic resource management play a crucial role in the profitability of bioenergy-oriented agricultural production. Conclusions: We concluded that biomass sales had a negligible influence on farm income. But a small income from biomass sales could affect a farm’s economic viability. Full article

The Inner Mongolia Plateau (IMP), situated in the arid and semi-arid ecological transition zone of northern China, is particularly vulnerable to both climate change and human activities. Understanding the spatiotemporal vegetation dynamics and their driving forces is essential for regional ecological management. This study employs Sen’s slope estimation, BFAST analysis, residual trend method and Geodetector to analyze the spatial patterns of Normalized Difference Vegetation Index (NDVI) variability and distinguish between climatic and anthropogenic influences. Key findings include the following: (1) From 1982 to 2022, vegetation cover across the IMP exhibited a significant greening trend. Zonal analysis showed that this spatial heterogeneity was strongly regulated by regional hydrothermal conditions, with varied responses across land cover types and pronounced recovery observed in high-altitude areas. (2) In the western arid regions, vegetation trends were unstable, often marked by interruptions and reversals, contrasting with the sustained greening observed in the eastern zones. (3) Vegetation growth was primarily temperature-driven in the eastern forested areas, precipitation-driven in the central grasslands, and severely limited in the western deserts due to warming-induced drought. (4) Human activities exerted dual effects: significant positive residual trends were observed in the Hetao Plain and southern Horqin Sandy Land, while widespread negative residuals emerged across the southern deserts and central grasslands. (5) Vegetation change was driven by climate and human factors, with recovery mainly due to climate improvement and degradation linked to their combined impact. These findings highlight the interactive mechanisms of climate change and human disturbance in regulating terrestrial vegetation dynamics, offering insights for sustainable development and ecosystem education in climate-sensitive systems. Full article

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

Stem radius growth (GRO), tree water deficit (TWD), and maximum daily shrinkage (MDS) were monitored throughout 2023 in a semi-arid Mediterranean forest stand in Burdur, Türkiye, where Pinus nigra subsp. pallasiana (Lamb.) Holmboe and Pinus brutia Ten. naturally co-occur. These indicators, derived from electronic band dendrometers, were analyzed in relation to key climatic variables. Results indicated that P. brutia had a longer growth period, while P. nigra exhibited a higher average daily increment under the environmental conditions of 2023 at the study site. Annual stem growth was nearly equal for both species. Based on dendrometer observations, P. brutia exhibited lower normalized TWD and higher normalized MDS values under varying vapor pressure deficit (VPD) and soil water potential (SWP) conditions. A linear mixed-effects model further confirmed that P. brutia consistently maintained lower TWD than P. nigra across a wide climatic range, suggesting a comparatively lower degree of drought-induced water stress. GRO was most influenced by air temperature and VPD, and negatively by SWP. TWD was strongly affected by both VPD and SWP, while MDS was primarily linked to minimum air temperature and VPD. Moreover, MDS in P. brutia appeared more sensitive to climate variability compared to P. nigra. Although drought limited stem growth in both species during the study year, the lower TWD and higher MDS observed in P. brutia may indicate distinct physiological strategies for coping with drought. These findings offer preliminary insights into interspecific differences in water regulation under the particular climatic conditions observed during the study year in this semi-arid Mediterranean ecosystem. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

This article investigates the institutional and informational foundations of water governance in Southern Europe amid escalating climate stress. Focusing on Portugal, Spain, Italy, and Greece, it develops a multi-level analytical framework to explore how information asymmetries and governance fragmentation undermine coordinated responses to water scarcity. Integrating theories of information economics, polycentric governance, and critical institutionalism, this study applies a stylized economic model and comparative institutional analysis to assess how agents—such as farmers, utilities, regulators, and civil society—respond to varying incentives, data access, and coordination structures. Using secondary data, normalized indicators, and scenario-based simulations, the model identifies three key structural parameters—institutional friction (θ_i), information cost (β_i), and incentive strength (α_i)—as levers for governance reform. The simulations are stylized and not empirically calibrated, serving as heuristic tools rather than predictive forecasts. The results show that isolated interventions yield limited improvements, while combined reforms significantly enhance both equity and effectiveness. Climate stress simulations further reveal stark differences in institutional resilience, with Greece and Italy showing systemic fragility and Portugal emerging as comparatively robust. This study contributes a flexible, policy-relevant tool for diagnosing governance capacity and informing reform strategies while also underscoring the need for integrated, equity-oriented approaches to adaptive water governance. Full article

Ravine forests are key to conserving biodiversity and maintaining ecosystem processes in fragmented landscapes. Here, we evaluated the phenology of plant species in the Los Nogales Nature Sanctuary (Lo Barnechea, Chile) using Sentinel-2 images (2019–2024) and the Alos Palsar DEM (12.5 m). We calculated the Normalized Difference Vegetation Index (NDVI), the Topographic Position Index (TPI), and Diurnal Anisotropic Heat (DAH) to assess vegetation dynamics across different topographic and thermal gradients. Generalized Additive Models (GAM) revealed that tree species exhibited more stable, regular seasonal NDVI trajectories, while shrubs showed moderate fluctuations, and herbaceous species displayed high interannual variability, likely reflecting sensitivity to climatic events. Spatial analysis indicated that trees predominated on steep slopes and higher elevations, herbs were concentrated in low-lying, moisture-retaining areas, and shrubs were more common in areas with higher thermal load. These findings highlight the significant role of terrain and temperature in shaping plant phenology and distribution, underscoring the utility of remote sensing and topographic indices for monitoring ecological processes in complex mountainous environments. Full article