Search Results (3,278)

At present, the decarbonization of the public transport sector plays a key role in international and regional policies. Among the various energy vectors being considered for future clean bus fleets, green hydrogen and electricity are gaining significant attention thanks to their minimal carbon footprint. However, a comprehensive Life Cycle Assessment (LCA) is essential to compare the most viable solutions for public mobility, accounting for variations in weather conditions, geographic locations, and time horizons. Therefore, the present work compares the life cycle environmental impact of different powertrain configurations for urban buses. In particular, a series hybrid architecture featuring two possible hydrogen-fueled Auxiliary Power Units (APUs) is considered: an H2-Internal Combustion Engine (ICE) and a Fuel Cell (FC). Furthermore, a Battery Electric Vehicle (BEV) is considered for the same application. The global warming potential of these powertrains is assessed in comparison to both conventional and hybrid diesel over a typical urban mission profile and in a wide range of external ambient conditions. Given that cabin and battery conditioning significantly influence energy consumption, their impact varies considerably between powertrain options. A sensitivity analysis of the BEV battery size is conducted, considering the effect of battery preconditioning strategies as well. Furthermore, to evaluate the potential of hydrogen and electricity in achieving cleaner public mobility throughout Europe, this study examines the effect of different grid carbon intensities on overall emissions, based also on a seasonal variability and future projections. Finally, the present study demonstrates the strong dependence of the carbon footprint of various technologies on both current and future scenarios, identifying a range of boundary conditions suitable for each analysed powertrain option. Full article

Citrus tristeza virus (CTV) is an important pathogen threatening the global citrus industry, but its evolution and transmission mechanism in wild citrus has not been clarified. Most of the existing studies are based on CTV-specific gene fragments, lacking genome-wide analysis. There is especially a lack of understanding of CTV transmission dynamics in wild citrus, which needs further investigation. In this study, wild citrus samples from three provinces of China were collected, virus genome data were obtained by high-throughput sequencing (HTS) technology and combined with public database data, and Bayesian phylogeographic inference was used to analyze virus composition characteristics in wild citrus, as well as the population genetic structure, temporal dynamic evolution, and spatial transmission mode of CTV. The results showed that Yunnan wild citrus samples contained the most abundant virus components, including CTV, Citrus Exocortis Viroid (CEVd), Citrus associated Ampelovirus 1 (CaAV-1), and Citrus Virus B (CiVB), while Jiangxi and Hunan samples only contained CTV and CEVd, with all samples showing mixed infection. Phylogenetic analysis showed that nine wild citrus CTV isolates were scattered in different evolutionary clades, and only 9.27% of genetic variation existed between the populations, while 90.72% of genetic variation existed within the populations, indicating little effect of geographic isolation on gene flow. The time to the most recent common ancestor (tMRCA) of CTV was estimated at 1360 CE, with subsequent divergence into two lineages, with population size stabilizing after a rapid increase in 1980–1990. Asia has been identified as the central source of CTV’s global spread, with key migration events including Asia to North America (1746), Asia to Oceania (1829), and Asia to South America (1965), coinciding with global maritime trade and the expansion of the citrus industry. Full article

Myriophyllum spicatum is a globally distributed aquatic plant capable of sexual and clonal reproduction. Despite its ecological importance and biochemical potential, studies on its genetic and clonal structure in freshwater systems throughout South Korea remain limited. We investigated the genetic and clonal diversity of M. spicatum using 30 newly developed microsatellite markers across 120 individuals from six freshwater systems in South Korea. Overall, 148 alleles were identified, with an average polymorphism information content value of 0.530. Clonal diversity differed among populations, with the genotypes to individuals (G/N) ratio ranging from 0.200 to 1.000. Bottlenecks and clonal dominance were observed in riverine populations. High genetic differentiation (mean F_ST = 0.556) indicated limited gene flow, and STRUCTURE analysis revealed six distinct genetic clusters. No significant correlation was found between genetic and geographic distance, suggesting possible seed dispersal by waterfowl, particularly between adjacent populations. Genetic structure was shaped by habitat type, disturbance intensity, and reproductive strategy. Stable reservoir habitats favored sexual reproduction and higher genetic diversity, whereas disturbed river systems showed clonal dominance and reduced variation. These findings provide essential genetic insights for conservation planning and sustainable management of aquatic plant resources. Full article

Floods are among the most devastating natural disasters; predicting their depth and extent remains a global challenge. Machine Learning (ML) models have demonstrated improved accuracy over traditional probabilistic flood mapping approaches. While previous studies have developed ML-based models for specific local regions, this study aims to establish a methodology for estimating flood depth on a global scale using ML algorithms and freely available datasets—a challenging yet critical task. To support model generalization, 45 catchments from diverse geographic regions were selected based on elevation, land use, land cover, and soil type variations. The datasets were meticulously preprocessed, ensuring normality, eliminating outliers, and scaling. These preprocessed data were then split into subgroups: 75% for training and 25% for testing, with six additional unseen catchments from the USA reserved for validation. A sensitivity analysis was performed across several ML models (ANN, CNN, RNN, LSTM, Random Forest, XGBoost), leading to the selection of the Random Forest (RF) algorithm for both flood inundation classification and flood depth regression models. Three regression models were assessed for flood depth prediction. The pixel-based regression model achieved an R² of 91% for training and 69% for testing. Introducing a pixel clustering regression model improved the testing R² to 75%, with an overall validation (for unseen catchments) R² of 64%. The catchment-based clustering regression model yielded the most robust performance, with an R² of 83% for testing and 82% for validation. The developed ML model demonstrates breakthrough computational efficiency, generating complete flood depth predictions in just 6 min—a 225× speed improvement (90–95% time reduction) over conventional HEC-RAS 6.3 simulations. This rapid processing enables the practical implementation of flood early warning systems. Despite the dramatic speed gains, the solution maintains high predictive accuracy, evidenced by statistically robust 95% confidence intervals and strong spatial agreement with HEC-RAS benchmark maps. These findings highlight the critical role of the spatial variability of dependencies in enhancing model accuracy, representing a meaningful approach forward in scalable modeling frameworks with potential for global generalization of flood depth. Full article

Environmental innovation represents a pivotal pathway toward achieving energy efficiency improvements, carbon footprint reduction, and ecological sustainability enhancement. The research investigates Chinese manufacturing enterprises listed on domestic stock exchanges throughout 2011–2023. The analytical framework utilizes count-based regression methodologies to explore how R&D investment intensity influences eco-innovation capabilities. Results demonstrate curvilinear associations linking R&D expenditure levels with both substantive and strategic environmental innovation achievements across industrial firms. This outcome successfully passed the turning-point test. Environmental oversight and financial incentives produce divergent moderating influences on innovation trajectories. Regulatory frameworks generate restrictive impacts through narrowing optimal investment ranges and dampening peak innovation outputs, whereas fiscal support mechanisms foster expansive effects via broadening resource availability and amplifying achievement levels. Cross-sectional examination uncovers substantial variations among ownership categories and geographical locations. State-owned enterprises demonstrate significantly lower optimal R&D intensity thresholds. Private firms require substantially elevated thresholds for optimal performance. Inland territories manifest unbalanced innovation dynamics. Coastal areas exhibit symmetric innovation patterns. The research enriches empirical knowledge in eco-innovation studies while offering context-specific strategic insights. The findings establish theoretical foundations and practical guidance for policy architects designing integrated environmental management systems that enhance innovation capabilities. Full article

This study employs Geographic Information Systems (GIS) combined with multivariate statistical techniques to evaluate soil contamination at two landfill sites in Al-Kharj, Saudi Arabia. A total of 32 soil samples were collected and analyzed for heavy metals and metalloids (HMs) using a range of contamination indices and established soil quality standards. GIS mapping revealed that the Al-Kharj landfill 1 (Kj1) experienced a steady area expansion from 2014 through 2025, while landfill Kj2 expanded from 2014 until 2022, after which its area contracted following the construction of additional facilities. The average values of HMs observed were as follows: Fe (9909 mg/kg), Al (6709 mg/kg), Mn (155.9 mg/kg), Zn (36.4 mg/kg), Cr (24.1 mg/kg), V (22.2 mg/kg), Ni (19.5 mg/kg), Cu (8.20 mg/kg), Pb (7.91 mg/kg), Co (4.32 mg/kg), and As (2.29 mg/kg). Notably, Kj2 exhibited overall higher HM concentrations than Kj1, with particularly elevated levels of Cr, Ni, and Pb. Although most HMs remained within internationally accepted safety limits, only three samples (9.4% of the total) exceeded the WHO threshold for Pb (>30 mg/kg). An analysis using contamination and enrichment factors pointed to increased concentrations of Pb, Zn, and Cr, suggesting localized anthropogenic contributions. Additionally, all samples recorded an ecological risk index (Eri) below 40, and the levels of As, Cr, and Pb consistently stayed under their respective effects range-low (ERL) thresholds, indicating minimal contamination risks. The variations in HM contamination between the sites are likely attributable to differences in the sources of metal inputs and removal processes. These findings highlight the need for continuous monitoring and localized remediation strategies to ensure environmental safety and sustainable landfill management. Full article

Sustainability within the rice and wheat supply chain is integral to attaining the UN’s Sustainable Development Goals (SDGs), as they are the two most consumed grains as food. Rice and wheat cultivation significantly impacts the environment, with the agricultural sector employing 27% of the global workforce and contributing 4% to the world’s GDP, thereby affecting social and economic sustainability. Developing a sustainability index for the wheat and rice supply chain is a complex endeavor, as it depends on various factors such as the location of growers, farming methods, the target audience, and the stakeholders involved. This index must be derived from an optimal selection of indicators to avoid information overload while covering all essential sustainability aspects. There are different methods, such as life cycle assessment, energy analysis, ecological footprint, and carbon footprint, being used to assess sustainability, with indicator-based assessment emerging as a comprehensive approach. This study utilised the Triple Bottom Line (TBL) to identify optimal sustainability indicators in the wheat and rice supply chain. A systematic literature review was initially conducted, followed by an expert opinion survey to determine the required indicators. The literature review unveiled a wide array of indicators used across studies, often contingent on each study’s specific objectives. While some consistency existed in environmental indicators, discussions on social and economic dimensions within the wheat and rice supply chain were limited. Analysis of the expert opinion survey revealed a consensus on most selected indicators, albeit with variations based on experts’ geographical locations. The final set of optimal indicators identified can serve as a foundation for developing a sustainability index, implementing a sustainability information management system, and formulating policy initiatives in the rice and wheat supply chain. Full article

Rapid urbanization intensifies disturbances to the ecological environment, underscoring the urgent need for effective strategies to guide regional development towards sustainability. Functional zoning offers a promising approach to address this challenge. However, in eco-fragile regions, functional zoning has often failed to incorporate the spatially explicit coupling coordination degree (CCD) between ecological environment and urbanization level. Taking Inner Mongolia as a case study, this study evaluated the spatial coordination between these two systems by leveraging geographic big data. Functional zones were then delineated using the K-means clustering method, incorporating the geospatial relationships between ecological environment and urbanization level. Results revealed significant geospatial heterogeneity in both ecological environment and urbanization level. Ecological environment generally declined from east to west, while urbanization was generally low throughout the region. Substantial variations in CCD were observed, with the global Moran’s I value confirming a significant spatial clustering pattern. Based on the findings above, five functional zones were identified, with the urbanization promotion zone as the dominant one. This study provides a valuable reference for regional pattern optimization and sustainable development of social-ecological systems. Full article

Climate change poses significant threats to urban tree health and survival worldwide. This study evaluates climate suitability risks for 12 common tree species in Wuhan, a Chinese metropolis facing escalating climate challenges. We analyzed risk dynamics and interspecific variations across three periods, the baseline (1981–2022), near future (2023–2050), and distant future (2051–2100), quantifying climate risk as differences between local climate conditions and species’ climatic niches. We further examined how species’ geographic distribution and functional traits influence these climate risks. The results revealed significant warming trends in Wuhan during the baseline period (p < 0.05), with projected increases in temperature and precipitation under future scenarios (p < 0.05). The most prominent risk factors included the precipitation of the driest month (PDM), annual mean temperature (AMT), and maximum temperature of the warmest month (MTWM), indicating intensifying drought–heat stress in this region. Among the studied species, Cedrus deodara (Roxb.) G. Don, Platanus acerifolia (Aiton) Willd., Metasequoia glyptostroboides Hu & W.C.Cheng, and Ginkgo biloba L. faced significantly higher hydrothermal risks (p < 0.05), whereas Koelreuteria bipinnata Franch. and Osmanthus fragrans (Thunb.) Lour. exhibited lower current risks but notable future risk increases (p < 0.05). Regarding the factors driving these interspecific variation patterns, the latitude of species’ distribution centroids showed significant negative correlations with the risk values of the minimum temperature of the coldest month (MTCM) (p < 0.05). Among functional traits, the wood density (WD) and xylem vulnerability threshold (P50) were negatively correlated with precipitation-related risks (p < 0.05), while the leaf dry matter content (LDMC) and specific leaf area (SLA) were positively associated with temperature-related risks (p < 0.05). These findings provide scientific foundations for developing climate-adaptive species selection and management strategies that enhance urban forest resilience under climate change in central China. Full article

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, and a global quantile machine learning model (QML) without spatial weighting served as the benchmark. GWQML achieved R² values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. This study represents one of the first applications of geographically weighted kernel functions in a quantile machine learning framework for daily soil moisture prediction. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes. Full article

Red industrial heritage is a crucial component of global socialist industrial civilization, embodying both industrial memory and revolutionary spirit. However, its preservation faces significant challenges, including insufficient policy attention, homogenized revitalization models, and a lack of systematic research. This study uses Hebei Province, a key region where modern industry and revolutionary history intersect, as a case study. By employing Geographic Information System (GIS) spatial analysis and historical geography, the research explores the spatiotemporal patterns and underlying factors that influence the distribution of red industrial heritage. The findings reveal: (1) the spatial distribution is irregular, exhibiting concentration, with high density in the central and southern parts of Hebei, while the northern and eastern areas are more dispersed; (2) The spatiotemporal evolution aligns with significant historical events; (3) The distribution pattern is shaped by multiple factors, with the dynamics of modern Chinese warfare and historical policies serving as the primary driving forces, interacting with natural geographical factors. This study enhances our comprehension of the significance of red industrial heritage and, based on its spatiotemporal variations, proposes a tiered, sustainable preservation strategy. It provides valuable insights into the preservation of socialist industrial heritage both in China and globally. Full article

Centipedes (Chilopoda, Myriapoda) are crucial soil predators, yet their vulnerability to climate and land use change remains unexplored. We assess the impact of these drivers on Greek centipedes, identify current and future biodiversity hotspots, and evaluate the effectiveness of the Natura 2000 Network of protected areas for their conservation. We used an updated species occurrence database of Greek centipedes, derived from literature reviews and museum collections, and evaluated database completeness and geographic sampling biases. Species Distribution Models were employed to predict future distribution shifts under climate and land use change scenarios. Biodiversity hotspots were identified based on species richness (SR) and corrected-weighted endemism (CWE) metrics. We overlapped SR and CWE metrics against the Natura 2000 Network to assess its effectiveness. We found that sampling effort is highly heterogeneous across Greece. All species are projected to experience range contractions, particularly in the 2080s, with variation across scenarios and taxa. Current biodiversity hotspots are concentrated in the south Aegean islands and mainland mountain ranges, where areas of persistent high biodiversity are also projected to occur. The Natura 2000 Network currently covers 52% of SR and 44% of CWE hotspots, with projected decreases in SR coverage but increases in CWE coverage. Our work highlights the vulnerability of Greek centipedes to climate and land use change and reveals conservation shortfalls within protected areas. We identify priority areas for future field surveys, based on sampling bias and survey completeness assessments, and highlight the need for further research into mechanisms driving centipede responses to global change. Full article

This study utilized the North American PhenoCam network to evaluate phenological characteristics and their relationships with geographic and climatic factors across deciduous broadleaf (n = 39) and evergreen needleleaf (n = 13) forests over the past decade. Using high temporal resolution near-surface imagery, key phenological indicators including the start, end, and length of growing season were derived and analyzed using linear regression and structural equation modeling. The results revealed substantial spatial variation; the evergreen needleleaf sites exhibited earlier starts to the growing season (112 vs. 130 Julian date), later ends to the growing season (286 vs. 264 Julian date), and longer lengths for the growing season (172 vs. 131 days) compared with the deciduous broadleaf sites. Latitude was significantly related to the start of the growing season and the length of the growing season at the deciduous broadleaf sites (R² = 0.28–0.41, p < 0.01), while these relationships were weaker at the evergreen needleleaf sites, and elevation had mixed effects. The mean annual temperature strongly influenced the phenology for both forest types (R² = 0.18–0.76, p < 0.01), whereas longitude, distance to the coast, and precipitation had negligible effects. Temporal trends in the phenological indicators were sporadic across both the deciduous broadleaf and evergreen needleleaf sites. Structural equation modeling revealed distinct causal pathways for each forest type, highlighting complex interactions among the geographical and climatic variables. At the deciduous broadleaf sites, geographical factors (latitude, elevation, and distance to the nearest coast) predominated the mean annual temperature, which in turn significantly affected phenological development (χ² = 2.171, p = 0.975). At the evergreen needleleaf sites, geographical variables had more complex effects on the climatic factors, start of the growing season, and end of the growing season, with the end of the growing season emerging as the primary determinant of growing season length (χ² = 0.486, p = 0.784). The PhenoCam network provides valuable fine-scale phenological dynamics, offering great insights for forest management, biodiversity conservation, and understanding carbon cycling under climate change. Full article

Achieving decoupling between tourism economic growth and tourism carbon emissions is of paramount importance. This study innovatively integrates the geographically weighted regression (GWR) model—a tool for analyzing spatial heterogeneity—into the Tapio decoupling framework to examine the dynamic decoupling relationship between tourism growth and carbon emissions. It further investigates the driving factors behind decoupling evolution, their interactions, and precisely characterizes the mechanisms, directions, pathways, and intensities of these drivers. Key findings reveal an M-shaped fluctuation trend in tourism carbon emissions within the study area, with significant variations in emission shares across different tourism sectors and transportation modes. Spatially, carbon emissions exhibit heterogeneity and negative autocorrelation, where inter-regional disparities diminish while intra-regional disparities intensify. The tourism economic system in the Yangtze River Economic Belt (YREB) transitioned through weak decoupling, expansive negative decoupling, and strong decoupling states, eventually stabilizing at weak decoupling. Regional decoupling states varied markedly, suggesting that some areas require exploration of new low-carbon development paradigms. For sustainable tourism development, policy-makers should prioritize the decoupling relationship between tourism emissions and economic growth. Region-specific policies must be formulated to facilitate low-carbon transitions, promote industrial upgrading, and enhance inter-regional collaboration—ultimately advancing sustainable tourism under carbon neutrality goals. Full article

Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations in cover crop outcomes across Nebraska, focusing on three critical management factors: seeding rate, termination timing, and termination-to-corn planting intervals. Using Decision Support System for Agrotechnology Transfer (DSSAT) simulations, we evaluated the effects of these practices on cover crop biomass, growth stages, and subsequent corn yield across seven sites. The results revealed that corn yield remained resilient across all sites, with no statistically significant differences (p > 0.05) across termination timings, seeding rates, or termination-to-planting intervals. A CC seeding rate analysis showed that biomass tended to increase with higher seeding densities, particularly from 200 to 250 plants m⁻², but the gains diminished beyond that, and few pairwise comparisons reached statistical significance. Termination timing had a significant effect on biomass and growth stages, with delayed termination resulting in greater biomass accumulation and advanced phenological development (e.g., Zadoks > 45), which may complicate termination efficacy. Increasing termination-to-planting intervals led to reduced biomass due to shorter growing periods, though these reductions were not associated with significant corn yield penalties. This study highlights the importance of tailoring CC management strategies to local environmental conditions and agronomic objectives. By addressing these site-specific factors, the findings offer actionable insights for farmers and land managers to optimize both ecological benefits and productivity in Nebraska’s no-till systems. Full article