Search Results (13,776)

Copernicus, the Earth Observation component of the European Union Space Programme, plays a key role in monitoring planetary health and informing global sustainability agendas. Enhancing its uptake offers a strategic opportunity to translate climate information into actionable knowledge for sustainable institutional governance. This study examines how data visualization, translating complex climate information into context-relevant formats, can strengthen the uptake of Copernicus Climate Change and Atmosphere Monitoring Service by national institutions. Using the Italian initiative for the National Collaboration Programme of the Copernicus Climate Change Service as an empirical setting, we adopt a mixed-method design to bridge expert visualization practices with institutional stakeholders tasked with sustainability transitions. The findings show that users widely recognize the value of Copernicus. Nonetheless, uptake depends largely on how easily visual outputs can be integrated into workflows and decision procedures. By linking uptake to visualization practices, the study reveals a previously underexplored user–expert gap between production and use contexts. We introduce “adaptive translation” as a framework to align scientific integrity with usability through progressive disclosure, defensibility-oriented design, and iterative feedback loops. The results provide context-sensitive guidance for designing “workflow-ready” visual products in similar national institutional settings, enhancing the capacity of institutional actors to design the climate-resilient actions that are essential for a sustainable future. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Soil salinization is projected to intensify under global warming, posing significant constraints on crop growth and agricultural productivity. Although numerous quantitative studies have investigated soil salinization, comprehensive assessments specifically targeting global croplands remain limited. We hypothesize that, within a machine learning framework, combining soil properties, climate variables and anthropogenic management factors can yield global maps of soil salinity in croplands. For this purpose, we use a random forest (RF) model, with irrigation involved, to predict global cropland soil salinity (ECe) at 0.1° resolution for 1981–2010, capturing its spatiotemporal dynamics. The results indicate that the model performs well (R² = 0.63), with soil depth, the aridity index and pH being particularly significant factors. High values of ECe were found across central South America, southwestern Africa, central India, and south-central and northeastern China. The proportion of salinized croplands exhibits a long-term upward trend, averaging 4.88%. Ultimately, this study delivers long-term global cropland salinity maps, offering critical insights for safeguarding food security under climate change. Full article

As an important geographical component at the boundary between land and ocean, the coastline serves as a key indicator reflecting coastal erosion and ecosystem variations. The central Jiangsu coast is a typical muddy coast, and against the backdrop of global climate change and large-scale coastal development, its coastal evolution is complex. In this study, we analyzed the patterns and trends in the evolution of Jiangsu’s coastline using remote sensing imagery from 1984 to 2024. The result indicated that the coastline length in central Jiangsu exhibits a trend of ‘initial continuous decrease followed by recovery growth.’ The coastline peaked at approximately 586.5 km in 1988 and then shortened to a minimum of about 365.2 km in 2015, after which it began to recover. Throughout the study period, the spatiotemporal changes in the coastline displayed a pattern with the Sheyang Estuary as the boundary, characterized by ‘more erosion in the north and more accretion in the south.’ The most severely eroded section was near the Fansheng Estuary, retreating by about 1412.18 m, whereas the most significant sedimentation occurred north of Fangtang Estuary, advancing by about 11,129.78 m toward the sea, with appositional uncertainty (RMSE) of ±15.6 m based on independent validation. The coastline evolution process was classified into seven patterns, with clear differences in spatial distribution and state transitions among them. With the sediment reduction, rising sea levels, and increased frequency of extreme marine storms has heightened the risk of coastal erosion. Therefore, measures such as comprehensive protection, restoration, and management of muddy coasts, scientific promotion of sedimentation, and appropriate reclamation design should be implemented. Full article

Aflatoxins (AFs), toxic secondary metabolites produced by Aspergillus species, represent a major threat to food safety and public health due to their pronounced hepatotoxic, carcinogenic, and mutagenic effects. With increasing global contamination risks driven by climate change and agricultural practices, the development of effective detoxification strategies has become a critical priority. This review provides a comprehensive and mechanistic overview of current aflatoxin (AF) decontamination approaches, focusing on two principal pathways: adsorption and inhibition strategies. Adsorption mechanisms involve the physicochemical sequestration of aflatoxins by inorganic materials, biological adsorbents, and engineered nanocomposites, thereby reducing toxin bioavailability. In contrast, inhibition strategies target fungal growth, toxin biosynthesis pathways, or promote enzymatic and microbial degradation of aflatoxins, offering more specific and potentially sustainable control. We critically analyze the underlying mechanisms, advantages, and limitations of each approach, including issues related to specificity, environmental stability, safety, and interactions with food matrices. Particular emphasis is placed on the toxicological implications of detoxification processes, including the reduction in aflatoxin-induced health risks and the safety of degradation products. Finally, this review highlights the importance of integrating adsorption and inhibition strategies to achieve synergistic decontamination and detoxification effects. Future perspectives on multifunctional materials, biological control systems, and intelligent monitoring technologies are discussed to advance the development of efficient, safe, and sustainable aflatoxin mitigation strategies. Full article

Cotton is a vital cash crop that underpins regional agricultural systems and the global textile supply chain. However, climate change and increasing human activity are reshaping the spatial distribution of areas suitable for cotton cultivation, with the potential impacts being particularly pronounced in arid and semi-arid regions. This study integrated high-resolution cotton distribution data, environmental variables and human activities and employed ensemble model and niche analysis methods to systematically assess cotton suitability in Xinjiang under current and future climate scenarios. The results indicate that the ensemble models demonstrate high predictive performance, with both model types (Model 1: Environmental; Model 2: Environmental and human activity) achieving AUC values exceeding 0.97 and TSS values exceeding 0.84. Under current climatic conditions, suitable cotton-growing areas are primarily distributed on both sides of the Tianshan Mountains, and the inclusion of human activity factors results in a 13.71% reduction in suitable area. Moreover, Future climate change is projected to result in an increase in its suitable range of between 28.25% and 94.10%, with the most significant expansion occurring under the high-emissions scenario. MESS analysis indicates that the newly identified suitable areas in the future bear a high degree of similarity to current environmental conditions, whilst MOD analysis further highlights that temperature and precipitation are the key drivers of environmental variation. Additionally, Xinjiang cotton will retain a high degree of ecological niche under future climatic conditions. These findings provide important scientific evidence for optimizing the spatial distribution of cotton cultivation in Xinjiang and for climate-adaptive agricultural management. Full article

Global climate change poses escalating ecological challenges, with agriculture contributing approximately 30% of anthropogenic greenhouse gas emissions, primarily from nitrous oxide (N₂O) and methane (CH₄). The farmland carbon-nitrogen cycle represents a key nexus for coordinating pollution control and carbon mitigation. This study applies bibliometric methods, including co-occurrence analysis, clustering, and burst detection, to 1286 publications retrieved from the Web of Science Core Collection (1990–2025) and CiteSpace 6.2.R4. Results indicate that China (444 papers, centrality 0.42), the United States (211 papers), and Germany (151 papers) are leading contributors, with major institutions forming a multi-centered international collaboration network. Keyword analysis identified 11 core clusters (modularity Q = 0.82, silhouette S = 0.91), with nitrous oxide emerging as the central theme (frequency 670). The field has evolved through three stages: fundamental emission mechanism studies (1990–2005), agricultural management practices (2006–2015), and integrated mitigation strategies with microbial mechanism exploration (2016–2025). Current frontiers emphasize microbial-mediated carbon-nitrogen cycling and yield-scaled emission assessments bridging theory and practice. Future research should prioritize cross-scale coupling analysis, multi-objective management frameworks, smart agricultural technologies, and policy integration. This study provides a systematic bibliometric mapping of the evolution of synergistic carbon-nitrogen research in agricultural systems, offering a quantitative overview of development trends and research gaps. Full article

In the context of global climate change and industrial low-carbon transition, whether environmental taxes can simultaneously promote environmental and economic benefits by stimulating corporate green investment remains a central issue in academic research. Existing studies have reached mixed conclusions regarding the effects of environmental taxes, emphasizing either the “innovation compensation” effect or the “crowding-out” effect. However, this binary perspective overlooks the internal boundary conditions under which environmental taxes operate, particularly the roles of market competition and firm-level resource endowments. In particular, limited attention has been paid to how competitive market environments shape firms’ responses to environmental regulation. To address this gap, this study develops an integrated analytical framework that combines external market competition with internal firm endowments. Using China’s 2018 Environmental Protection Tax Law as a quasi-natural experiment and a panel dataset of Chinese listed firms from 2009 to 2024, this study employs a Difference-in-Differences (DID) approach to examine the impact of environmental taxation on corporate green investment. The results show that: (1) the environmental protection tax significantly promotes corporate green investment, with substantial heterogeneity across firm size, ownership structure, and regional institutional environments; (2) market competition serves as an important external moderating mechanism, as intensified competition strengthens firms’ incentives to pursue technological differentiation through green investment, thereby generating an “escape-competition effect”; and (3) from an internal perspective, the effectiveness of environmental taxation is also shaped by firm endowments. High investment activity provides the necessary resource buffer to support strategic pivots, whereas rapid revenue growth and high financial slack (excessive cash ratio) generate strategic inertia, thereby attenuating firms’ responsiveness to the tax shock. This study not only provides empirical evidence from China on the mechanisms through which environmental taxes influence corporate green transformation, but also offers important policy implications for improving environmental tax systems in other countries. Full article

Although hantaviruses have traditionally been considered geographically restricted rodent-borne pathogens, globalization, climate change, ecosystem disruption, and environmental contamination may collectively favor novel transmission scenarios and altered epidemiological patterns. The experience gained during the SARS-CoV-2 pandemic showed the importance of environmental determinants, airborne exposure, and host susceptibility factors in emerging viral diseases. In this context, increasing but still indirect evidence suggests that environmental toxicants and the exposome may modulate susceptibility to hantavirus infection and influence disease severity. The proposed mechanisms include oxidative stress, endothelial dysfunction, pulmonary inflammation, and immune dysregulation, rather than direct causal effects of toxicants on infection itself. This article discusses current knowledge regarding interactions among toxic environmental exposures, climate change, and hantavirus disease, with special emphasis on Andes orthohantavirus (ANDV), the principal hantavirus known to exhibit person-to-person transmission. The article integrates recent evidence within the One Health framework and highlights future research priorities linking environmental toxicology, zoonotic disease ecology, and global environmental change. Full article

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

Climate change has intensified global wildfire risks, yet national-scale prediction remains challenging due to the difficulty of consistently monitoring fuel conditions and human ignition factors. This study introduces calendar-based time proxy variables as structural surrogates for these unobservable drivers and integrates them with the Canadian Fire Weather Index (FWI) within a parsimonious framework for seasonally fire-prone regions such as South Korea. Using 15 years of nationwide wildfire records and daily observations from 100 ASOS stations (2011–2025), predictive performance was evaluated across eight models and five feature sets (Time-only, Weather-only, Weather + Time, FWI-only, and FWI + Time). Based on test-set mean AUC, the Time-only feature set reached 0.7374, clearly exceeding the random-classifier baseline (AUC = 0.5) and indicating the independent predictive value of time proxy variables. Furthermore, integrating time proxies with FWI improved performance, with the best model (CatBoost) achieving test AUC = 0.8394 and Recall = 0.6019. Multi-model SHAP analysis revealed complementary contributions of FWI components (53.7% ± 4.7%) and time proxy variables (46.3% ± 4.7%). Overall, the results demonstrate that a simple yet structured input design based on time proxy variables provides meaningful predictive performance for nationwide wildfire early warning systems. Full article

The global methane budget is largely driven by biogenic sources, many of which remain insufficiently characterized. Here, we investigated the community composition and seasonal dynamics of methanogenic and methanotrophic assemblages to elucidate the key contributors to methane cycling and the environmental factors shaping these processes in lake sediments of the Rila Mountains (Bulgaria). Methanogenic communities are primarily composed of Methanothrix, Methanosarcina, Methanobacterium and Methanoregula with summer peaks in Methanothrix and Methanoregula, and cold-season proliferation of Methanobacterium. Methanotrophic communities are dominated by representatives of the Pseudomonadota, including Crenothrix, Methylobacter, and Methylocystis with summer maxima observed for Crenothrix and Methylobacter. Methanosarcina and Methylocystis showed relatively stable abundances throughout the ice-free season. Ordination and correlation analyses revealed that temperature, pH, and carbon (organic and inorganic) concentration and lability emerged as the environmental drivers influencing on microbial communities, with seasonally variable effects on methane-cycling microorganisms. These findings provide a foundation for future research on methane cycling in alpine lake ecosystems of the Rila Mountains and contribute to improving predictions of methane emissions under changing climatic conditions. Full article

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges. Full article

Climate change necessitates a global transition toward green and low-carbon development, underscoring the critical importance of energy efficiency. Buildings account for a substantial portion of urban energy consumption and carbon emissions, with central air-conditioning systems representing the largest energy-consuming component. This study focuses on optimizing equipment selection—including chillers, pumps, and cooling towers—for the refrigeration plant of a commercial complex in Xiamen. Following theoretical optimization, the operational performance of the implemented system was empirically analyzed using long-term monitoring data from 2024 to 2025. The results demonstrate an energy efficiency ratio (EER) of 5.44 in 2024 and 5.28 in 2025, surpassing the Grade I efficiency threshold (5.2) stipulated by the Chinese standard T/CRAAS 1039-2023. Monthly EER values consistently remained above 5.06 throughout the cooling season. Detailed performance analysis of individual equipment further confirmed that actual operational performance of chillers, pumps, and cooling towers closely matched or even exceeded rated performance metrics, with chiller efficiency deviations controlled within 5%. This study integrates optimized equipment selection at the design stage with empirical performance analysis based on actual operation, providing a validated approach for improving the energy efficiency of refrigeration plants in commercial buildings and offering valuable references for the revision of relevant energy efficiency standards. Full article