Search Results (7,788)

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

Indigenous Fire Stewardship (IFS) has long been practiced by Indigenous Peoples to care for the land, reduce wildfire risk, and maintain ecological and cultural values. In British Columbia, Yunesit’in, a member of the Tsilhqot’in Nation, has revitalized their IFS practices following the 2017 Hanceville Fire. As climate policy increasingly supports nature-based solutions, carbon credit programs are emerging as a potential funding source for IFS. This study used grounded theory with interviews to understand Yunesit’in IFS practitioners’ and community leaders’ perspectives on carbon credits. Using the concept of “cultural signatures,” we identified core values shaping community engagement in carbon markets. While most interviewees (7/10) were initially unfamiliar with carbon credits, many saw their potential to support long-term program goals after learning more. Three cultural signatures emerged from the analysis: (1) a sense of stewardship responsibility, (2) the importance of a community-grounded program, and (3) the revitalization of Indigenous knowledge and land-based practices. Interviewees expressed concern that carbon credits might shift the program’s focus away from land and culture toward technical goals that exclude community participation. We conclude that building awareness about carbon and carbon credits among Indigenous Peoples, and supporting engagement processes that reflect cultural signatures in carbon frameworks, are both critical. Full article

Tackling climate change in the public–private partnership (PPP) infrastructure sector requires radical transformation of projects to make them resilient against climate risks and free from excessive carbon emissions. Types of PPP infrastructure such as transport, power plants, hospitals, schools and residential buildings experience more than 30% of global climate change risks. Therefore, this study aims to examine the interrelationships between the climate risk management strategies in PPP infrastructure projects. The first step in conducting this research was to identify the strategies through a comprehensive literature review. The second step was data collection from 147 PPP stakeholders with a questionnaire. The third step was analysing the interrelationships between the strategies using a partial least square–structural equation model approach. The findings include green procurement, defined climate-resilient contract award criteria, the identification of climate-conscious projects and feasible contract management strategies. The results provide understanding of actionable measures to counter climate risks and they encourage PPP stakeholders to develop and promote climate-friendly strategies to mitigate climate crises in the PPP sector. The results also serve as foundational information for future studies to investigate climate change risk management strategies in PPP research. Full article

Vineyard cover crops deliver well-documented ecosystem services, yet consistent establishment, especially of perennial grasses and legumes, remains a primary barrier to adoption. This review reframes “companion (nurse) cropping” not as a new crop class but as a acilitative establishment strategy within the broader cover-/service-crop literature. We (i) position our contribution relative to recent syntheses, (ii) synthesize evidence on companion crops practices that reduce cover cropping early failure risk, and (iii) propose a testable research agenda. A focused scoping review of peer-reviewed and extension literature indexed in Web of Science and Google Scholar was conducted using search terms encompassing cover/service crops and nurse/companion/facilitation in viticulture systems. Across climates, fast-establishing cereals (Avena sativa, Hordeum vulgare, Secale cereale, × Triticosecale Wittmack) and short-cycle legumes (Vicia sativa, Pisum sativum, Trifolium incarnatum) can reliably “nurse” slower perennials and legumes by providing early groundcover, weeds control, and microclimate buffering when sown at reduced rates (≈25–50% of monoculture) and terminated on time to limit vine competition. Evidence gaps persist for in-row applications, water-use penalties under drought, and long-term effects on yield and grape composition. Companion cropping is argued to be a design principle in vineyard cover-crop programs rather than a separate category. A decision framework and research agenda are presented to quantify establishment reliability, resource trade-offs, and wine-relevant outcomes, and it is recommended that future decision tools make the companion-phase logic explicit to de-risk adoption and align with regional guidelines. Full article

To explore the coupling between agricultural farming models and surface water environmental in central China’s irrigation districts, this study focuses on the Four Lakes Basin within Jianghan Plain, a key grain-producing and ecological protection area. Integrating remote sensing images, statistical yearbooks, and on-site monitoring data, the study analyzed the phased characteristics of the basin’s agricultural pattern transformation, the changes in non-point source nitrogen and phosphorus loads, and the responses of water quality in main canals and Honghu Lake to agricultural adjustments during the period 2010~2023. The results showed that the basin underwent a significant transformation in agricultural patterns from 2016 to 2023: the area of rice-crayfish increased by 14%, while the areas of dryland crops and freshwater aquaculture decreased by 11% and 4%, respectively. Correspondingly, the non-point source nitrogen and phosphorus loads in the Four Lakes Basin decreased by 11~13%, and the nitrogen and phosphorus concentrations in main canals decreased slightly by approximately 2 mg/L and 0.04 mg/L, respectively; however, the water quality of Honghu Lake continued to deteriorate, with nitrogen and phosphorus concentrations increasing by approximately 0.46 mg/L and 0.06 mg/L, respectively. This indicated that the adjustment of agricultural farming models was beneficial to improving the water quality of main canals, but it did not bring about a substantial improvement in the sustainable development of Honghu Lake. This may be related to various factors that undermine the sustainability of the lake’s aquatic ecological environment, such as climate change, natural disasters, internal nutrient release from sediments, and the decline in water environment carrying capacity. Therefore, to advance sustainability in this basin and similar irrigation districts, future efforts should continue optimizing agricultural models to reduce nitrogen/phosphorus inputs, while further mitigating internal nutrient release and climate disaster risks, restoring aquatic vegetation, and enhancing water environment carrying capacity. Full article

Dams are currently confronted with severe risks from frequent extreme climates and expanding aging deterioration, with traditional mitigation measures struggling to balance efficient prevention/control and long-term management. As an innovative solution, fiber-reinforced polymer (FRP) composites support improved dam safety governance. To address the lack of systematic integration in existing dam-related studies, this paper promotes the development of an FRP in the dam field by comprehensively analyzing and summarizing the material properties, interfacial bonding properties of the FRP, as well as the flexural and compressive characteristics of FRP bar–concrete members and FRP sheet–concrete members while systematically organizing their practical engineering application cases. It also explores the FRP’s potential in hydraulic structures and suggests its wider application therein based on the FRP’s superior properties. Full article

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

Climate risk, as a significant factor affecting human sustainable development, has emerged as a focal topic of concern for governments and all sectors of society. Using a dataset from China’s Shanghai and Shenzhen A-share markets spanning 2007 to 2019, this study empirically examines how climate risk influences corporate financialization. The empirical results show that heightened climate risk significantly reduces the level of corporate financialization, a finding that remains robust across multiple tests. Further heterogeneity analyses indicate that the suppressive effect of climate risk is particularly evident among state-owned enterprises, firms operating in intensely competitive industries, and those located in regions subject to more stringent environmental policies. Mechanism analysis suggests that climate risk inhibits corporate financialization primarily by intensifying firms’ financing constraints while simultaneously stimulating their innovation capacity. These findings imply that corporate financialization in China is largely driven by profit-maximizing behaviors rooted in “investment substitution” and “real-sector intermediation” motives. Collectively, this research enhances understanding of the channels through which climate risk impacts corporate financial behavior and offers valuable empirical insights for policymakers aiming to optimize climate regulations and redirect financial resources toward productive real-sector activities. Full article

Climate change poses a growing threat to the mental health of children and adolescents. This narrative review synthesizes global, interdisciplinary research on the psychological impacts of climate disruption during critical developmental stages, with attention to marginalized populations. We explore three key pathways of harm: direct exposure to environmental disasters, chronic disruption of ecological and social systems, and existential distress such as eco-anxiety. Drawing on eco-social theory and developmental psychopathology, the review highlights how these impacts are shaped by age, geography, identity, and systemic inequities. It identifies both risk and protective factors, emphasizing the importance of caregiving relationships, cultural practices, education, and youth climate engagement. While activism can foster resilience and purpose, it may also incur emotional burdens that require clinical and policy attention. We argue that child and adolescent mental health must be recognized as central to climate justice and adaptation, and we offer urgent recommendations for integrated action across sectors. Full article

Climate change is increasingly altering ecosystems around the world and threatening biodiversity, especially species with narrow distribution ranges and a dependency on dedicated conservation practices. In Saskatchewan, Canada, the ecological significance of the genus sedge (Carex L.) from the Cyperaceae family is well recognized, yet spatially explicit forecasts of its habitats under future climate scenarios remain absent, creating a major obstacle to forward-looking conservation strategies. This study assesses the current and future habitat suitability of six sedges, including three nationally at-risk species (C. assiniboinensis, C. saximontana, C. tetanica) and three provincially rare species (C. glacialis, C. granularis, C. supina subsp. spaniocarpa). We applied the MaxEnt algorithm to model the distributions of those Carex species of conservation concern using 20 environmental predictors (19 bioclimatic variables and elevation) under baseline climate (1970–2000) and projected future scenarios for the 2030s and 2050s using SSP245 and SSP585 emission pathways. We optimized and validated models with the ENMeval package to enhance predictive reliability. Model accuracy was high (AUC = 0.88–0.99) and the results revealed a diversity of species responses: C. assiniboinensis and C. tetanica are projected to expand their suitable habitat, while C. saximontana is expected to lose high suitability areas. The distributions of C. glacialis and C. supina subsp. spaniocarpa remain restricted and relatively stable across scenarios. C. granularis is projected to have dynamic range shifts, particularly under the high-emission SSP585 scenario. Temperature-related variables were consistently the most influential predictors. These results provide critical insights into the potential impacts of climate change on Carex species of conservation concern in Canada and offer valuable guidance for prioritizing adaptive conservation planning and proactive habitat management. The diversity of species responses emphasizes the necessity of tailored conservation approaches rather than a one-size-fits-all strategy. Full article

Microgrids are becoming an innovative piece of modern energy systems as they provide locally sourced and resilient energy opportunities and enable efficient energy sourcing. However, microgrid operations can be greatly affected by sudden environmental changes, deviating demand, and unexpected outages. In particular, extreme climatic events expose the vulnerability of microgrid infrastructure and resilience, often leading to increased risk of system-wide outages. Thus, successful microgrid operation relies on timely and accurate outage predictions. This research proposes a data-driven machine learning framework for the optimized operation of a microgrid and predictive outage detection using a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture that reflects inherent temporal modeling methods. A time-aware embedding and masking strategy is employed to handle categorical and sparse temporal features, while mutual information-based feature selection ensures only the most relevant and interpretable inputs are retained for prediction. Moreover, the model addresses the challenges of experiencing rapid power fluctuations by looking at long-term learning dependency aspects within historical and real-time data observation streams. Two datasets are utilized: a locally developed real-time dataset collected from a 5 MW microgrid of Maple Cement Factory in Mianwali and a 15-year national power outage dataset obtained from Kaggle. Both datasets went through intensive preprocessing, normalization, and tokenization to transform raw readings into machine-readable sequences. The suggested approach attained an accuracy of 86.52% on the real-time dataset and 84.19% on the Kaggle dataset, outperforming conventional models in detecting sequential outage patterns. It also achieved a precision of 86%, a recall of 86.20%, and an F1-score of 86.12%, surpassing the performance of other models such as CNN, XGBoost, SVM, and various static classifiers. In contrast to these traditional approaches, the RNN-LSTM’s ability to leverage temporal context makes it a more effective and intelligent choice for real-time outage prediction and microgrid optimization. Full article

The Brazil nut production chain, which is reliant on Amazonian environmental conditions, is significantly affected by climate change, particularly extreme droughts, which decrease production and compromise sanitary quality. This study evaluated the influence of severe drought on aflatoxin concentrations and sequence toxigenic fungi in Brazil nuts harvested during the 2023 off-season. Aflatoxins were quantified using high-performance liquid chromatography, while fungal sequencing involved DNA extraction, PCR, and sequencing analysis. Findings indicated that all Brazil nut samples collected during extreme drought contained detectable aflatoxins, with 10% exceeding the legal threshold of 10 µg/kg. Phylogenetic analysis identified four isolates as Penicillium citrinum. Additional morphological and sequencing analyses identified Aspergillus species from the Circumdati and Flavi sections, although one isolate could not be taxonomically classified. These results demonstrate the aflatoxin production by fungi in Brazil nuts in an unprecedented way under drought conditions. Furthermore, the diversity of fungal species during drought underscores the risk of contamination, emphasizing the necessity for monitoring future harvests to improve management and ensure product safety. Full article

In recent years, agriculture has increasingly faced shocks related to climate change, pathogen outbreaks, and geopolitical instability, highlighting the need for sustainable regeneration strategies. This study develops an innovative Multi-Criteria Decision-Making (MCDM) framework that integrates the Delphi method, the Analytic Network Process (ANP), and the Aggregated Decision-Making (ADAM) method—the first application of this combination in the context of agricultural regeneration. The framework was applied to the Apulia region (Italy), heavily affected by the Xylella fastidiosa epidemic, and evaluated alternative crops across 30 economic, environmental, and socio-cultural sub-criteria. Results indicate that carob, walnut, and pistachio outperform other options by combining strong economic viability, climate resilience, and cultural compatibility. To mitigate the risks of monoculture, crop diversification strategies based on high-ranked alternatives are recommended. Sensitivity analysis confirmed the robustness of results, and the framework demonstrates high scalability, offering a transparent tool for policymakers in regions facing similar agricultural crises. Full article

Cave systems are a kind of natural laboratory for interdisciplinary research on karstogenesis in the context of global changes. In this study, we investigate the concentration of ²²²Rn at 65 points in 37 representative caves of Bulgarian karst through continuous monitoring with passive and active detectors with a duration of 1 to 13 years. The concentration changes strongly both in the long term and seasonally, with values from 0.1 to 13 kBq m⁻³. These variations are analyzed from different perspectives (location and morphological features of the cave system, cave climate, ventilation regime, etc.). The seasonal change in the direction and intensity of ventilation is a leading factor determining the gas composition of the cave atmosphere during the year. Parallel measurements of ²²²Rn and CO₂ concentrations in the cave air show that both gases have a similar seasonal fluctuation. Cases of coincidences of an anomalous increase in the concentration of ²²²Rn with manifestations of seismic activity and micro-displacements along tectonic cracks in the caves have also been registered. The dependencies between the ²²²Rn concentration in the caves and in the soil above them are also discussed, as well as the possible connections between global trends in climate change and trends in ²²²Rn emissions. Special attention is paid to the risks of radiation exposure in show caves. A calculation procedure has been developed to achieve the realistic assessment of the effective dose of cave guides. It is based on information about the annual course of the ²²²Rn concentration in the respective cave and the time schedule of the guides’ stay in it. The calculation showed that the effective dose may exceed the permitted limits, and it is thus necessary to control it. Full article