Search Results (7,927)

The Hermies-Ruyaulcourt site (Pas-de-Calais), investigated within the “Canal Seine-Nord Europe” project, provides an exceptional record of pedosedimentary dynamics throughout the last interglacial-glacial cycle (Eemian–Weichselian). Eight stratigraphic trenches, correlated along 350 m, reveal several pedosedimentary units strongly influenced by local topography. This study combines sedimentological and micromorphological analyses with optically stimulated luminescence (OSL) dating. For OSL ages, a correction of the water content calculation protocol was developed, accounting for long-term moisture variations during burial. Nine OSL ages from humic horizons of the Early Glacial (MIS 5d-5a) and colluvial deposits of the Lower Pleniglacial (MIS 4) represent the first robust chronological dataset for these periods in northern France. Their internal consistency and agreement with existing thermoluminescence ages on burnt flints support their reliability. Moreover, geomorphological analysis highlights intense erosional phases which are interpreted as rapid permafrost destabilisation events linked to the melting of large ice-wedge networks around 60–55 ka and 30 ka (thermokarst erosion gullies). These investigations thus enable the chronology of the loess–palaeosols and the link with associated climatic events to be refined. This leads to a spatio-temporal model describing the evolution of Last Glacial environments in Western Europe, providing a robust reference for studying the Neanderthal occupation of the area. Full article

As global climate change intensifies, research on low-carbon practices has become a critical component of sustainable tourism development. The carbon emission profile of ski tourism differs significantly from other tourism sectors. Ski resorts have a mountainous terrain and typically maintain relatively high levels of vegetation, endowing them with inherent advantages for pioneering low-carbon and sustainable tourism practices. However, the substantial energy demands associated with artificial snowmaking systems and advanced infrastructure pose significant challenges to reducing carbon emissions in ski resort operations. This study gathers first-hand data on sustainable tourism development in the Chongli ski resort—the region that hosted the 2022 Winter Olympics—through field investigations and interviews with key industry stakeholders. It develops a comprehensive framework accounting for carbon emissions in ski resorts by integrating input–output analysis with enterprise-level data, focusing on four core operational sectors: catering, skiing, wholesale and retail, and leasing and business services. Furthermore, this study examines the coupling relationship between carbon emissions and operating revenue. Using correlation and regression analyses, this study identifies the key drivers of carbon emissions across these operational departments within the ski tourism sector. The results indicate that carbon emissions from these four sectors in the Chongli ski resort exhibit periodic fluctuations with an overall upward trend year by year. Nevertheless, progress in low-carbon development is evident, suggesting that the resort is on a trajectory toward achieving peak carbon emissions and eventual carbon neutrality. The inclusion of natural endowments, market-scale effects, festival and special events, and capital investment in ski tourism collectively serve as crucial drivers for low-carbon sustainability in Chongli. Based on these findings, this study proposes targeted recommendations to support low-carbon sustainable development, offering scientific insights for similar Winter Olympics host cities. This study integrates top-down input–output analysis with bottom-up enterprise data, taking Chongli, the host city of the Winter Olympics, as a timely case study. It constructs a four-dimensional low-carbon development model based on the identification of key natural, social, and economic driving factors, and strengthens the reliability of the conclusion by relying on first-hand field research and operator interview data. Our study provides an analysis of methodological innovation, framework integrity, and solid empirical evidence that accounts for micro-scale carbon emissions in ski resorts. Full article

The accelerating impacts of climate change present critical challenges to cultural heritage, particularly in the Mediterranean region where hydroclimatic extremes are intensifying. Future estimates for the Sanctuary of Asklepios at Epidaurus, a UNESCO World Heritage Site, suggest more intense precipitation patterns, increased rainfall intensity and water-induced material degradation. This study aims to identify current and projected climate-related threats to the site and to inform adaptive strategies that safeguard both its physical integrity and its associated heritage values through a value-based approach. Opting for a heritage value-based risk assessment, the study employs a mixed-methods technical approach grounded in the Conceptual Framework for Disaster Risk Reduction of UNISDR and ICCROM’s “ABC Method” for the risk assessment of climatic threats that combines GIS-based hydrological modelling (HAND), field observations and existing material assessments with NARA Grids to link exposure, vulnerability and value loss. Results reveal intensified surface water runoff and localised water inundation threatening key monuments, particularly the Roman Odeion and the central part of the site’s ensemble, while frost-related risks are projected to decline towards 2100. The findings suggest the development of site-specific climate change adaptation that prioritises drainage enhancement, preventive conservation and continuous monitoring to preserve its Outstanding Universal Values under changing climatic conditions. Full article

The cover-management factor (C-factor) plays a pivotal role in soil erosion control and is the most easily influenced by policymakers. Despite the availability of numerous C-factor estimation methods, systematic comparisons of their applicability and associated uncertainties remain limited, particularly for future projections under climate change scenarios. This study systematically evaluates multiple widely used C-factor estimation models and projects potential C-factor changes under future scenarios up to 2070, using 2015 as a baseline. Results reveal substantial spatial variability among models, with the land use/land cover-based model (CLu) showing the strongest correlation with the reference model (r = 0.960) and the lowest error (RMSE = 0.048). Using the CLu model, global average C-factor values are projected to increase across all Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP-RCP) scenarios, rising from 0.077 to 0.079–0.082 by 2070. Statistically significant trends were observed in 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas, identified as hotspot regions (HRs). In these HRs, mean C-factor values are expected to increase by 16.1% and 33.4%, respectively, relative to the 2015 baseline. Economic development analysis revealed distinct trajectories across income categories. Low-income countries (LICs, World Bank classification) exhibited a pronounced dependency on development pathways, with C-factor values decreasing by −50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5 compared to 2015. In contrast, lower-middle-income, upper-middle-income, and high-income countries exhibited consistent C-factor increases across all scenarios. These variations were closely linked to cropland dynamics, with cropland areas in LICs decreasing by 64.6% under SSP1-RCP2.6 but expanding under other scenarios and income categories between 2015 and 2070. These findings highlight the critical importance of sustainable land-use policies, particularly in LICs, which demonstrate the highest magnitude of both improvement and degradation under varying scenarios. This research provides a scientific foundation basis for optimizing soil conservation strategies and land-use planning under future climate and socioeconomic scenarios. Full article

In the context of accelerating global urbanization and sustainable development challenges, impervious surfaces, as a key component of urban land cover, are significantly associated with regional economic development. This study takes Harbin, a typical cold region city, as a research object and constructs a three-level analytical framework of “land surface classification-economic simulation-mechanism analysis.” By innovatively integrating multi-source remote sensing, demographic, and economic data, the research addresses gaps in understanding urban sustainability in cold environments. An enhanced XGBoost algorithm was employed to achieve high-precision classification of ten land surface materials, resulting in a high overall accuracy. Furthermore, a gridded GDP spatialization model developed using high-resolution population data demonstrated superior performance compared to traditional methods. Machine learning-assisted analysis revealed that asphalt and metal surfaces are the most significant impervious materials driving economic output, reflecting the respective influences of transportation infrastructure and industrial agglomeration. Spatial pattern analysis indicates that Harbin’s impervious surfaces exhibit a lower fractal dimension and a distinct grid-like morphology compared to the typical subtropical city of Guangzhou, underscoring urban form adaptations to cold climatic constraints. The strong spatial coupling between gradients of GDP intensity and the attenuation of impervious surface density is quantitatively confirmed. This study provides a quantitative basis and a transferable technical framework for optimizing land use intensity and infrastructure planning in cold cities, thereby offering a scientific foundation for sustainable, intensive land utilization in climate-vulnerable urban systems. Full article

With the increasing volatility and extremity of global climate change, the frequency, intensity, and associated impacts of compound extreme climate events have escalated substantially. To investigate the temporal trends and characteristics of such events, we identified compound extreme climate events in the Huangshui River Basin, located in the northeastern Qinghai–Tibet Plateau, using daily mean temperature and precipitation records from eight meteorological stations. Compound warm–wet, warm–dry, cold–wet, and cold–dry events from 1960 to 2022 were detected based on cumulative distribution functions, and their long-term trends and intensity structures were examined. The results show that: (1) Warm–dry events dominate the basin, with an average annual frequency of 32.84 days per year, occurring frequently across all seasons; cold–dry events rank second (22.38 days per year) and are particularly frequent in winter. (2) Warm–dry events are highly concentrated in the river valley region (e.g., Minhe station), whereas cold–dry and warm–wet events mainly occur in the low-mountain areas (e.g., Huangyuan and Datong). (3) From 1960 to 2022, warm–dry and warm–wet events exhibit a highly significant increasing trend (p < 0.001), cold–dry events show a significant decreasing trend, and cold–wet events display no statistically significant trend. (4) In terms of intensity, all four types of compound events—warm–wet, warm–dry, cold–wet, and cold–dry—are dominated by weak to moderate grades. Overall, the basin is undergoing a compound-risk transition from historically “cold–dry dominated” conditions toward a regime characterized by “warm–dry predominance with emerging warm–wet events.” By identifying compound extreme climate events and analyzing their spatiotemporal variability and intensity characteristics, this study provides scientific support for disaster prevention, daily management, and risk mitigation in climate-sensitive regions. It also offers a useful reference for developing strategies to address compound extreme events induced by climate change and for implementing regional risk-prevention measures. Full article

Urban Heat Islands (UHIs) are urbanized areas that experience significantly higher temperatures than their surroundings, contributing to thermal discomfort, increased air pollution, heightened public health risks, and greater energy demand. In Bhutan, where urban expansion is concentrated within narrow valley systems, the formation and intensification of UHIs present emerging challenges for climate-resilient urban development. Thimphu, in particular, is experiencing rapid urban growth and densification, making it highly susceptible to UHI effects. Therefore, the aim of this study was to evaluate and simulate UHI conditions for Thimphu Thromde. We carried out the simulation using a GIS, multi-temporal Landsat imagery, and an Artificial Neural Network model. Land use and land cover classes were mapped through supervised classification in the GIS, and surface temperatures associated with each class were derived from thermal bands of Landsat data. These temperature values were normalized to identify existing UHI patterns. An Artificial Neural Network (ANN) model was then applied to simulate future UHI distribution under expected land use change scenarios. The results indicate that, by 2031, built-up areas in Thimphu Thromde are expected to increase to 72.82%, while vegetation cover is projected to decline to 23.52%. Correspondingly, both UHI and extreme UHI zones are projected to expand, accounting for approximately 14.26% and 6.08% of the total area, respectively. Existing hotspots, particularly dense residential areas, commercial centers, and major institutional or public spaces, are expected to intensify. In addition, new UHI zones are likely to develop along the urban fringe, where expansion is occurring around the current hotspots. These study findings will be useful for Thimphu Thromde authorities in deciding the mitigation measures and pre-emptive strategies required to reduce UHI effects. Full article

In the context of intensifying global efforts to mitigate climate change, methane emissions from the oil and gas sector have emerged as a critical environmental and regulatory challenge, given methane’s high global warming potential over short timeframes. This study investigates methane emissions from representative extraction and production of oil and gas facilities in Romania, focusing on fugitive emissions from wells and associated processing infrastructure. The research is grounded in the implementation of a comprehensive Leak Detection and Repair (LDAR) program, aligned with OGMP 2.0 standards, and utilizes advanced detection technologies such as Flame Ionization Detectors (FID), Optical Gas Imaging (OGI), and Quantitative Optical Gas Imaging (QOGI). A systematic inventory and screening of thousands of components enabled the precise identification and quantification of methane leaks, providing actionable data for maintenance and emissions management. The findings highlight that, although the proportion of leaking components is relatively low, cumulative emissions are significant, with block valves, connectors, and compressor shaft seals identified as the most frequent sources of major leaks. The study underscores the importance of rigorous preventive and corrective maintenance, rapid leak remediation, and the adoption of modern detection and continuous monitoring technologies. The approach developed offers a robust framework for regulatory compliance and supports the transition from inventory-based to measurement-based emissions reporting, in line with recent European regulations. Ultimately, effective methane management not only fulfills environmental obligations but also delivers economic benefits by reducing product losses and enhancing operational efficiency, contributing to the decarbonization and sustainability objectives of the energy sector. Full article

The System of Rice Intensification which promotes agro-ecological practices like alternate wetting and drying (AWD) to enhance root growth and resource efficiency, relies on the genotypic capacity of rice varieties to undergo physiological adaptation. This study elucidates the molecular basis of such adaptation by investigating the transcriptomic profile of four rice varieties to continuous flooding (CF) and AWD at 50 days after transplanting. Our analysis revealed distinct, organ-specific acclimation strategies. Roots underwent extensive transcriptional reprogramming, underscoring their role as the primary site of plasticity. Under CF, a conserved response involving cell wall reinforcement was accompanied by variety-specific strategies, ranging from sustained growth to enhanced anaerobic metabolism. Under AWD, roots shifted toward water stress management, with varieties employing distinct defensive (e.g., diterpenoid biosynthesis) and metabolic programs. Associated transcription factors (TFs) enriched under CF included Dof and MYB, whereas bZIP, HSF, and WRKY factors predominated under AWD. In leaves, acclimation to AWD involved more targeted adjustments, including modulation of nitric oxide signaling and photoprotective pathways, regulated by TFs such as WRKY, NAC, and HSF. Varieties with robust TF responses, such as IR64 and Hitachi hatamochi, showed comprehensive regulatory shifts, while others exhibited more constrained profiles. Overall, this study provides a molecular framework for understanding variety-specific adaptation to SRI-relevant water management practices and identifies key TFs as promising candidates for breeding climate-resilient rice. Full article

This study aims to investigate the impact of green finance on corporate sector investments and their associated environmental outcomes. The authors collected cross-sectional survey data with a sample of four hundred firms selected from the five green-relevant industries in an emerging economy. The results indicate that, over the last three years, seventy percent of firms have accessed at least one green instrument. Overall, the firms under study indicate that PKR 3.4 million is being allocated to green finance, and PKR 2.7 million is spent on CAPEX. However, each million PKR is associated with a ten percent capital expenditure, which exhibits the highest adoption of the renewable energy sector, while the manufacturing sector has the lowest adoption. Regression results depict that Greenhouse gas reduction is only achievable if expenditure on R&D is ensured for environmental gains. This study indicates a declining incremental impact when green finance exceeds PKR 5.00 million, suggesting that firms’ limitations in utilizing the additional amount may be a factor. Financially constrained firms achieve stronger environmental goals, confirming that strict criteria to finance projects show more responsibility and discipline in executing projects. However, small- and medium-sized firms are confronted with barriers, such as lack of information and transaction costs. The findings of this study highlight the need for a multi-layered regulatory framework, innovation-driven incentives, and fintech integration to fully realize the potential of green finance. The outcome enables financial institutions, sustainability practitioners, and regulators to connect financial markets, national climate, and development goals. Full article

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts. Full article

Lentil (Lens culinaris Medik. subsp. culinaris) is a Mediterranean legume crop of high value due to nutritional quality and adaptability; however, its cultivation is increasingly threatened due to climate uncertainty and reduction in genetic diversity in modern cultivars. The present research study evaluated 31 Greek lentil accessions (twenty-two landraces and nine commercial cultivars of both small and large seed types) in a semi-arid environment of Central Greece, over two cropping seasons, focusing on phenological, morphological, yield, and quality traits. The great diversity observed at the morpho-phenological and qualitative levels implies the high genotypic diversity of these genetic resources. Small-seeded landraces performed better in seed and biological yield, harvest index, and protein content, having greater phenological stability and tolerance to the Mediterranean environments. In particular, the highest seed yield was observed in LAX small-seeded landrace (1930 kg ha⁻¹), followed by TSO (1559 kg ha⁻¹), DIG (1449 kg ha⁻¹), and EGL (1437 kg ha⁻¹) small-seeded landraces. As for the regression analysis, seed yield was positively correlated with days to flowering (TF: r = 0.076, p < 0.01), plant height (PH: r = 0.143, p < 0.05), number of pods per plant (NPP: r = 0.941, p < 0.001), number of seeds per pod (NPP: r = 0.432, p < 0.001), number of branches (NPB: r = 0.234, p < 0.01), biological yield (BY: r = 0.683, p < 0.001), and harvest index (HI: r = 0.650, p < 0.001). Principal component analysis (PCA) distinguished small-seeded landraces associated with adaptive and yield traits from large-seeded cultivars associated with seed size. Greek lentil landraces, especially the small-seeded genotypes (e.g., LAX and DIG), have great potential for use in the development of climate-tolerant and high-yielding lentil varieties adapted for sustainable Mediterranean production. Breeding programs can target the crossing of landraces with large-seeded cultivars (e.g., IKAm and THEm) to develop varieties that combine stress tolerance, adaptation, and high productivity with adaptation to different seed sizes. Subsequent studies on drought tolerance and heat resistance are still important for continued improvement in lentil productivity in a changing climate. Full article

The Colombian Orinoquia was shaped within a tectonic and sedimentary framework linked to the uplift of the Andean cordilleras during the Oligocene–Miocene. This orogenic event generated two tectonic fronts and facilitated extensive fluvial sedimentation across a broad alluvial geosyncline. The present geomorphological configuration reflects the cumulative interaction of tectonic and erosional processes with Quaternary climatic dynamics, which together produced complex landscape assemblages characterized by plains with distinctive drainage patterns. To delineate and characterize geomorphological units, we employed multidimensional imagery and Machine Learning techniques within the Google Earth Engine platform. The classification model integrated dual polarizations of synthetic aperture radar (L-band) with key topographic variables including elevation, slope, aspect, convexity, and roughness. The analysis identified three major physiographic units: (i) the Foothills and the Floodplain, both dominated by fluvial environments; (ii) the High plains and Serranía de La Macarena (Macarena Mountain Range), where denudational processes predominate; and (iii) localized aeolian environments embedded within the Floodplain. These contrasting dynamics have generated a broad spectrum of landforms, ranging from terraces and alluvial fans in the Foothills to hills and other erosional features in La Macarena. The Floodplain, developed over a sedimentary depression, illustrates the combined action of fluvial and aeolian processes, whereas the High plains is characterized by rolling plains and peneplains formed through the uplift and erosion of Tertiary sediments. Such geomorphic heterogeneity underscores the interplay between tectonic activity, climatic forcing, and surface processes in shaping the Orinoquia landscape. The geomorphological classification using Random Forest demonstrated high effectiveness in discriminating units at a regional scale, with accuracy levels supported by confusion matrices and associated Kappa indices. Nevertheless, some degree of classificatory overlap was observed in fluvial environments, likely reflecting their transitional nature and complex sedimentary dynamics. Overall, this methodological approach enhances the objectivity of geomorphological analysis and establishes a replicable framework for assessing landform distribution in tropical sedimentary basins. Full article

Background and Objectives: To evaluate whether regional climatic factors, particularly relative humidity (RH) and temperature, are associated with geographic variation in dry eye disease (DED) prevalence in Argentina. Materials and Methods: A secondary analysis of a nationwide voluntary online cross-sectional survey of DED in Argentina (April 2022; non-probability sample; n = 10,812) was performed. Participants were assigned to one of five standard geopolitical regions. DED prevalence (%) was estimated at regional levels. Monthly mean, maximum, and minimum temperature and RH for April 2022 were obtained from the National Meteorological Service; for each province/region, data corresponded to the principal synoptic station in the provincial capital. Because the survey and climate data were restricted to April, seasonal effects could not be evaluated. Region-level analyses of climate–DED patterns were prespecified as exploratory and descriptive given the small number of regional units (n = 5). Weighted descriptive models were used to compare RH–DED patterns while accounting for regional age and sex distributions. Results: Regional DED prevalence ranged from 37.7% (Northwest) to 49.8% (Cuyo). RH showed a clear inverse pattern with regional DED prevalence, whereas temperature showed no consistent relationship. Weighted descriptive models showed a similar qualitative RH–DED pattern, although residual confounding cannot be excluded. Conclusions: DED prevalence in Argentina shows geographic variability and a descriptive inverse pattern with ambient RH. These ecological findings are hypothesis-generating and must be interpreted cautiously due to sampling bias. Future seasonal, longitudinal, or individual-level studies are needed to confirm and quantify these preliminary signals. Full article

Soil salinization is an escalating global concern threatening agricultural productivity and ecological sustainability, particularly in coastal regions where complex interactions among hydrological, climatic, and anthropogenic factors govern salt accumulation. The vertical differentiation and spatial heterogeneity of salinity drivers remain poorly resolved. We present an integrated modeling framework combining ensemble machine learning and spatial statistics to investigate the depth-specific dynamics of soil salinity in the Yellow River Delta, a vulnerable coastal agroecosystem. Using multi-source environmental predictors and 220 field samples harmonized to 30 m resolution, the hybrid Gray Wolf Optimizer–Random Forest–XGBoost model achieved high predictive accuracy for surface salinity (R² = 0.91, RMSE = 0.03 g/kg, MAE = 0.02 g/kg). Spatial autocorrelation analysis (Global Moran’s I = 0.25, p < 0.01) revealed pronounced clustering of high-salinity hotspots associated with seawater intrusion pathways and capillary rise. The results reveal distinct vertical control mechanisms: vegetation indices and soil water content dominate surface salinity, while total dissolved solids (TDS), pH, and groundwater depth increasingly influence middle and deep layers. By applying SHAP (SHapley Additive Explanations), we quantified nonlinear feature contributions and ranked key predictors across layers, offering mechanistic insights beyond conventional correlation. Our findings highlight the importance of depth-specific monitoring and intervention strategies and demonstrate how explainable machine learning can bridge the gap between black-box prediction and process understanding. This framework offers a generalizable framework that can be adapted to other coastal agroecosystems with similar hydro-environmental conditions. Full article