Search Results (1,209)

Multiple factors such as global climate warming and environmental degradation have increased natural disaster frequencies, threatening the safety of citizens’ lives and properties seriously. Existing literature primarily focuses on livelihood diversification, resilience, and vulnerability in flood-prone areas, with limited research systematically examining farmers’ livelihood risks and management strategies across multiple dimensions. To address this gap, this study advances the understanding of multidimensional livelihood risks by systematically identifying the key risk perceptions and management strategy choices of farmers, thereby providing empirical evidence essential for designing targeted interventions and sustainable adaptation policies in flood-prone regions. Specifically, this study employs an unordered multinomial logistic model to examine farmers’ risk management strategy choices, drawing on a field survey of 540 farmers from floodplain areas in Sichuan Province, China. The analysis systematically covers four livelihood risk dimensions (health, environmental, financial, social) and five management strategies (expansion, adjustment-oriented, contraction, aid-oriented, dependency-based). The results indicate that: (1) The most significant livelihood risk is environmental, and the most commonly selected risk management strategy is adjustment-oriented management; (2) When farmers face health risks, they tend to adopt dependency-based management strategy; in dealing with financial and social risks, farmers perceive no significant difference in the selection of the five management strategies. Accordingly, targeted strategies are proposed: insurance and information for environmental risks, medical security for health, employment training for social, and income diversification for financial risks. Full article

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

Background/Objectives: Cross-border meteorological disaster medical rescue policies in the Guangdong–Hong Kong–Macao Greater Bay Area face challenges in coordination, completeness, and effectiveness. Existing policy systems lack systematic quantitative evaluation. This study aims to assess the current policy landscape and provide evidence-based recommendations for optimizing cross-border medical rescue policy supply and enhancing regional emergency coordination. Methods: We reviewed policy documents on cross-border meteorological disaster medical rescue issued from 2005 to 2025 and used a combination of text mining and the PMC index model to quantitatively analyze and evaluate selected policy texts. The PMC scoring criteria (0–10 scale) define scores ≥ 7 as “excellent” and 5–6.99 as “good”. Results: Policy word frequency analysis showed that “emergency,” “disaster,” “meteorology,” and “management,” were core high-frequency words; semantic network clustering revealed five major thematic modules: monitoring and early warning, emergency rescue, medical treatment, material support, cross-border coordination. The PMC indices of the 26 policies ranged from 5.65 to 9.42, with an average score of 6.95, which corresponds to the “good” level. Policy 14 scored 9.42, reaching the “perfect” level; eight policies received an “excellent” rating, indicating generally high policy quality. From a dimensional perspective, X9 (policy evaluation), X1 (Nature of policy), and X8 (policy guarantee) scored relatively high, while X4 (policy type) and X2 (policy timeliness) scored relatively low. Conclusions: The overall performance of the cross-border meteorological disaster medical rescue policy system is good, with relatively sound policy transparency and institutional guarantees. However, the policy system has the following shortcomings: insufficient cross-border coordination mechanisms, shallow integration of medical rescue professional content into comprehensive policies, and an emphasis on short-term emergency response with inadequate medium- and long-term strategic planning. It is recommended to strengthen medium- and long-term top-level strategic planning, enhance the functional allocation of health departments in meteorological disaster emergency plans, and establish a cross-regional joint policy evaluation and dynamic revision mechanism. Full article

The management of European mountain landscapes is increasingly threatened by rural abandonment and escalating environmental risks. This study investigates an innovative Stewardship–Renewable Energy Communities model for the Central Apennines, exploring how post-seismic public reconstruction can serve as a financial engine for territorial maintenance. Utilizing Open Data Sisma administrative records and Photovoltaic Geographical Information System irradiation metrics, this research assesses the solar potential of 18 municipalities within the Sibillini seismic crater. To ensure a reliable baseline, a Building Suitability Coefficient was introduced as a conservative proxy for the public reconstruction sector. Results indicate that the implementation of a distributed network of 6.5 MWp across 325 public nodes, with a specific yield of 1390 kWh/kWp on the entire area, could generate 9 GWh/year. This translates to approximately EUR 1.08 million in annual revenue from energy incentives and sharing. This economic surplus provides a Stewardship Capacity sufficient to fund the active maintenance of 789.77 hectares per year through Nature-Based Solutions, based on a regional rate of 1200 EUR/ha. The novelty of this study lies in bridging post-disaster energy policy with landscape resilience, demonstrating that distributed rooftop solar portfolios represent a non-invasive, self-funding mechanism. By leveraging the reconstructed public stock, mountain territories can transition from passive neglect to active, energy-backed stewardship, offering a reproducible template for high-value cultural landscapes. Full article

Rainfall-induced landslides are destructive natural hazards that require timely detection and early warning to protect lives and infrastructure. This study presents the development and deployment of an IoT-based, cost-effective, real-time monitoring and early warning system that integrates surface and subsurface sensors to detect slope instability and issue timely warnings for disaster prevention. The monitoring system integrates tilt sensors, volumetric water content sensors, a MEMS-based inclinometer, a rain gauge, and a video camera, all linked to a web-based platform. Field results demonstrated that the tilt sensors effectively detected surface displacement, the volumetric water content sensors responded rapidly to rainfall infiltration, and the MEMS-based inclinometer captured subsurface displacement during rainfall events. Detailed analysis was conducted using multisource monitoring datasets collected during three specific rainfall events. An early warning method for landslides was proposed by combining the tilt rate, horizontal displacement rate derived from the MEMS-based inclinometer, and saturation index. Accordingly, critical threshold values for different warning levels were established based on tilt rate (T_r), displacement rate (D_r), and saturation index (S_i). This study provides a robust strategy and guidelines for early warning systems, enabling generation of warning alarms and demonstrating immense potential to reduce the impacts of rainfall-induced shallow landslides and enhance risk management. Full article

Named entity recognition in natural disaster emergencies is a critical foundational task for emergency management. However, existing methods face challenges including complex entity types, frequent emergence of new terminology, model knowledge obsolescence, and poor adaptability to dynamic knowledge updates, resulting in limited accuracy and generalization in real-world disaster scenarios. To address these issues, this paper proposes a named entity recognition method for natural disaster emergencies based on instruction tuning and knowledge graph retrieval-augmented generation. We first construct a dedicated instruction-tuning dataset, EM-InstructNER, and a domain-specific knowledge graph, EmergencyKG, tailored to natural disasters. Then, LoRA is employed for parameter-efficient fine-tuning of the Qwen2-7B-Instruct base model, while KG-based RAG dynamically retrieves subgraphs from the knowledge graph to generate semantically enriched augmented prompts, providing external structured knowledge support for generative NER. Experimental results demonstrate that the proposed method achieves a macro F1 score of 0.9205 on the EM-InstructNER test set, representing a 36.6% relative improvement over the best-performing zero-shot baseline (DeepSeek-R1:14B), while remaining competitive with strong supervised sequence labeling approaches (e.g., BERT + CRF). The framework provides knowledge graph update flexibility and significantly reduces training computational cost and GPU memory consumption through LoRA-based parameter-efficient fine-tuning. Cross-domain evaluation on the public CLUENER2020 benchmark further demonstrates its generalization capability. Full article

Climate change has become a major macroeconomic challenge with profound implications for the real economy. This study examines the influence of perceived climate-related risks, proxied by news-based indices capturing media attention to global warming, natural disasters, US climate policy, and international climate summits, on US business activity across short- and long-term horizons. The methodological framework first employs principal component analysis to condense multiple explanatory variables into a single composite factor. A Fourier autoregressive distributed lag model is then adopted to estimate the effects of these forward-looking informational proxies over time. The results reveal marked heterogeneity across perceived climate-related risks and temporal horizons. Global warming news intensity constitutes a persistent impediment, exerting stronger and more durable effects on business activity. Natural disaster media coverage generates sharp short-term deterioration, although its influence fades over longer horizons. News-based transition-risk proxies exhibit a mixed pattern. US climate policy media coverage consistently dampens business conditions, whereas international climate summit coverage plays a comparatively modest role. Our findings underscore that a one-size-fits-all strategy is ineffective. Climate risk management should differentiate between persistent and transitory forces, recognizing that perceived risks may operate through expectations, uncertainty, and sentiment rather than realized damages or enacted policies alone. Full article

The increasing penetration of distributed energy resources and the growing vulnerability of centralized power systems to natural hazards, terrorist attacks, acts of sabotage, technical incidents, and operational uncertainties have intensified the need for resilient and secure energy infrastructures. Microgrids have emerged as a promising solution to enhance energy security by enabling the localized generation, autonomous operation, and flexible integration of renewable energy sources. However, their effective deployment introduces complex risks related to technical, economic, and operational uncertainties. This paper presents a comprehensive framework for risk management in microgrids within modern power systems, aiming to improve the overall security and resilience of Romania’s power system. The study systematically identifies and evaluates the main risk scenarios affecting the power system: natural disasters, terrorist attacks, acts of sabotage, and technical incidents. In addition, to achieve an in-depth analysis, the paper also discusses the SWOT and PESTEL analyses of the Romanian power system, as well as its resilience. A multi-level risk assessment methodology is proposed, combining probabilistic analysis with severity (impact) analysis. The proposed approach is validated through case studies based on risk scenario assessments, demonstrating its effectiveness in improving microgrid performance under diverse disturbance conditions. The results highlight the critical role of proactive risk management in supporting energy security objectives, while ensuring stable and resilient operation of the Romanian power system. This research contributes to the development of adaptive and sustainable power systems, capable of addressing future challenges in an increasingly decentralized energy landscape, and can be adapted to any modern power system worldwide. Full article

With the rapid growth of tourism in Dayi County over the past decade, this study develops a meteorological disaster risk assessment framework for major tourist attractions in this region. Drawing upon daily precipitation and temperature records from 25 meteorological stations (2014–2023) alongside multi-source geospatial data, we evaluate six primary attractions: Xiling Snow Mountain, Huashuiwan, Anren Ancient Town, Xinchang Ancient Town, Tianfu Huaxigu Valley, and Shujiu Cultural Park. The evaluation model integrates four core dimensions: hazard, environmental sensitivity, asset vulnerability, and disaster mitigation capacity. Indicator weights are determined through the Analytic Hierarchy Process, and GIS-based spatial analysis is employed for risk zonation. Additionally, the 45-year ChinaMet dataset provides independent validation for the long-term stability of the hazard assessment. Results reveal a distinct west-low, east-high composite risk gradient. High-altitude mountainous regions in the west exhibit a lower overall risk. Despite frequent extreme weather events, extensive vegetation coverage and low visitor density effectively buffer the negative impacts of physical hazards. Conversely, tourist attractions on the eastern plains fall within high-risk zones. Concentrated visitor populations, dense built environments, and low-lying terrain collectively amplify exposure to severe rainstorms and extreme heatwaves. These findings demonstrate that meteorological disaster risk in tourism destinations fundamentally arises from the deep coupling of natural and human systems. Thus, this study provides a scientific basis for implementing differentiated disaster prevention, mitigation, and localized emergency management strategies. Full article

Natural and human-made disasters can severely impair terrestrial communication infrastructures and disrupt emergency response coordination in modern smart cities. To address these challenges, this paper introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme, an adaptive, secure, and energy-efficient drone-assisted solution for post-disaster network recovery and emergency response. WAY-CR integrates three main components: First, a novel WAY-based metaheuristic optimizer incorporates the concept of Yo-Yo Motion into the conventional Weighted Average Algorithm (WAA), improving the balance between exploration and exploitation during CH selection and clustering. Second, a secure communication model combines the Paillier Homomorphic Cryptosystem (PHC) with a trust evaluation model to provide end-to-end security and authenticity, ensuring that only authenticated and trustworthy drones participate in communication and routing. Third, a Trust-Aware Boltzmann Path Selection method introduces probabilistic decision-making into routing, allowing adaptive selection of secure and energy-efficient routing paths. WAY-CR formulates a multi-objective optimization model that minimizes communication cost and energy consumption while maximizing trust, link stability, and coverage. Stage 1 addresses secure intra-Ground Control Station (GCS) clustering, authentication, and trust management, whereas Stage 2 restores inter-GCS connectivity through a Secure Relay Discovery and Verification procedure based on Boltzmann Path Selection. An adaptive maintenance mechanism further supports dynamic reconfiguration in response to CH failures, mobility, or trust degradation, thereby preserving stable network performance under disaster-induced disruptions. Extensive simulation results show that WAY-CR outperforms state-of-the-art Flying Ad Hoc Network (FANET) baselines in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery, highlighting its potential as a resilient, scalable, and secure solution for post-disaster smart-city environments. Full article

As the coupling and interaction among subsystems in integrated energy systems (IESs) increase, these systems are becoming increasingly vulnerable to failures under extreme weather events and natural disasters, which threatens overall operational security and stability. This paper reviews recent studies on the resilience-oriented management of IESs, with a focus on the characterization and assessment of energy system resilience covering various types of resilience-challenging extreme events, the related quantification metrics, methods, and resilience enhancement applications. Based on the reviewed studies, this paper attempts to figure out how internal and external adversities impact the systems, and identifies effective methods to detect, assess, and quantify system vulnerabilities and weak links under extreme events scenarios. Rather than confining the analysis to single-carrier systems, this study bridges cross-disciplinary perspectives to construct a resilience-oriented conceptual framework specifically designed for IESs, centering on the core logical, analytical, and technical strategies for both characterizing and advancing IESs’ resilience. Also, this review tries to reveal research opportunities to address significant gaps in the existing literature. Findings from the review can inform future research and help to develop scalable and effective ways to enhance IESs’ resilience. 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

Land trusteeship is an innovative agricultural management model that connects smallholder farmers with modern agriculture. It promotes large-scale agricultural operations, but still faces the impacts of conventional natural disasters. Although agricultural disaster insurance serves as a critical mechanism for farmers to mitigate these natural risks, its risk-mitigation potential remains underutilized due to the persistent challenge of low insurance participation rates. This study develops a decision-making model for farmers’ purchase of agricultural disaster insurance under land trusteeship, drawing on protection motivation theory, market failure theory, and quasi-public goods theory. Using structural equation modeling, we empirically analyze survey data from 319 land-trusteed farmers to uncover the mechanisms and pathways influencing their insurance purchase decisions. The results indicate that: (1) Vulnerability and severity are positively associated with protection motivation through perceived response efficacy and self-efficacy, and protection motivation is directly associated with purchase decisions; (2) Government support has both direct and indirect effects on purchase behavior; and (3) Individual and household characteristics are significantly associated with purchase decisions, with pure farmers, Type I part-time farmers, and farmers with larger landholdings tending to purchase agricultural disaster insurance more often. Full article

Throughout history, wildfires have been prominent natural disasters that cause pollution, environmental damage and loss of lives. Local firefighting agencies and disaster response initiatives have typically managed to contain fires and limit damage to controllable levels. However, in recent times, due to climate change and human population growth, wildfire occurrences are becoming less predictable and result in greater cost and damage. Solutions employing new technologies and a more operations-oriented analysis, through system-of-systems (SoS), could be a promising way to combat further wildfire devastation. Designing new aircraft and strategies that can be used in human transport and firefighting is one of the goals of the COLOSSUS project. To enable international innovation, especially amongst young researchers, a student competition called the X-Challenge was released. This paper will deal with the overview of the challenge, breaking down its objectives, constraints, research contributions and outcomes. Following this paper, four different student teams will present their solutions, including innovative aircraft designs and SoS analysis methods. The knowledge gained, and successes and failures from the challenge, alongside outlook and recommendations for future challenges and SoS exploration, will be discussed in this paper. Full article

Disaster risk reduction and management are essential for sustainable development in territories highly exposed and vulnerable to natural hazards. Recent disasters in the Canary Islands have highlighted the importance of proactive preparedness and systemic approaches to risk management, emphasizing the need to better understand existing barriers to disaster risk reduction (DRR). This study develops an analysis of risk governance within the current planning instruments in the Canary Islands, the island of Tenerife, and the municipality of Candelaria. The research examines the integration of DRR across strategic, territorial, urban, and emergency planning at the regional, insular, and municipal levels. The findings identify key challenges and opportunities for integrating DRR within existing planning frameworks, highlighting both the potential and the limitations of current instruments as cross-cutting tools for building more resilient territories. While Tenerife has a relatively solid administrative and planning structure that could support a more systemic vision of risk, sectoral fragmentation and coordination gaps remain. Overall, the study contributes to the ongoing discussion on advancing risk governance from a systemic perspective at the local level. The challenges identified delineate the boundaries and directions for improvement, offering a valuable contribution to the existing body of knowledge. Full article