Current disaster risk management (DRM) frameworks promote risk-informed decision making, resilience building, and multi-stakeholder collaboration, illustrating their continued relevance and dominance in addressing disaster risks. However, they often lack comprehensive ethical guidance for decision making under uncertainty, leading to systemic failures in foresight and inequitable outcomes. To bridge this gap, this article introduces a prudence-based DRM framework rooted in foresight, moral reflection, and ethical responsibility as essential principles for decision making. Drawing from classical philosophy, particularly Aristotle’s phronēsis and Aquinas’s prudentia, prudence is described as a practical virtue that links knowledge with the moral goal of the common good. Through a narrative and scoping review of interdisciplinary literature, three interconnected dimensions are identified: the main types of prudence (political, purificatory, and perfect), the fundamental elements that inform prudent judgment (memory, intelligence, and foresight), and the supporting qualities that uphold moral action (including docility, caution, and cleverness). These interconnected dimensions foster anticipatory, equitable, and inclusive risk governance that prioritizes long-term resilience over reactive, short-sighted actions. The proposed prudence framework redefines DRM as an ethical and political act that combines technical expertise with moral wisdom, ensuring that decisions are both scientifically robust and socially legitimate. By embedding prudence into disaster risk governance, this approach provides a normative foundation for risk-informed, fair, and sustainable decision making, helping institutions manage uncertainty with foresight, compassion, and integrity.

Developing an early warning system requires four key components: risk knowledge, hazard detection (including monitoring and forecasting), dissemination (involving decision making and warning issuance), and response (including action implementation). Early warning system (EWS) provides an integrated system to facilitate timely responses to hazards. To assess the effectiveness of an EWS, a systems-based approach that holistically captures its critical components is required. Therefore, this study used a system-based modeling tool, an agent-based model (ABM), to examine the factors influencing evacuation response in a flooding scenario. The model was tested for an area nestled within the Ba catchment in Fiji. Surveys, interviews, and previous literature underpin the development of the model. Evacuation response was examined across key social and physical factors, with the dissemination of warning information kept as the central focus. The findings indicate that timely warnings, coupled with training, substantially improve response outcomes. However, factors such as belief in the warning and flood velocity can undermine outcomes even when warnings are issued promptly. This study underscores the critical need to assess the effectiveness of EWS holistically by accounting for a range of factors, extending beyond forecast development and dissemination.

The COVID-19 pandemic has tested the limits of healthcare facilities around the world, disrupting the provision of essential preventive and curative services. Despite the growing literature on pandemic lessons, a prominent research gap emerges in understanding the unfolding of the COVID-19 pandemic from the perspective of key disaster components and disaster risk management (DRM). This study aimed to examine the nuanced interplay between pandemic impacts, vulnerabilities, and adaptation options, and how this unfolded across the DRM cycle (preparedness, response, and recovery). The methodological framework relies on an Impact Chain and forensic analysis, which provide an analytical perspective that is currently missing from the literature. The case study focused on the COVID-19 pandemic in the context of the hospital network in Bucharest in 2020–2022. This approach provides a basis for pinpointing shortcomings in mitigation specific to this case study and for formulating three key lessons for improved pandemic DRM. A key finding is that the DRM phases are distorted in long-lasting, wave pattern-based hazards like the COVID-19 pandemic, resulting in a new, intertwined DRM model. The model highlights that preparedness measures are scarcely implemented due to the wave pattern of the hazard, leading to inconsistent efforts to mitigate vulnerability. The lessons draw attention to prioritizing the resolution of deeply rooted vulnerabilities that stem from the underfunding of the Romanian medical system, designing far-reaching adaptation options, and troubleshooting them prior to implementation. These findings provide entry points for developing more proactive, scientifically-grounded mitigation frameworks that overcome the limitations reflected in the intertwined DRM model.

In 2017, Mexico was struck by the most powerful earthquake since the 1932 Jalisco earthquake. Moving beyond the predominant focus on climatic events and short-term effects in existing literature on disaster impacts, this study provided new evidence on how seismic shocks generated unequal impacts across the labor market in a developing economy. It examined disparities not only across workers and across firm characteristics but also within firms—by legal form, ownership type, informality status, and size—and among workers, based on pre-disaster job quality. Additionally, it captured the temporal dynamics of these effects, distinguishing between those that appeared immediately and those that developed or persisted over time. The empirical strategy was grounded in a panel event study design that integrates difference-in-differences estimation with measures of earthquake exposure. By contrasting treatment definitions based on an exogenous geophysical indicator and official disaster area classifications, the study strengthened causal identification and captured both direct physical shocks and institutional responses. This dual approach contributes to the disaster impact evaluation literature by offering a more comprehensive assessment. The evidence reveals an asymmetry in post-disaster adjustment: firms did not exhibit statistically significant impacts either immediately or over time, while workers appeared to have absorbed the consequences. This suggests that firms may have partially displaced the shock onto their workforce, using adjustment mechanisms associated with declines in job quality among vulnerable workers. The evidence underscores the need to expand post-disaster recovery frameworks beyond physical reconstruction, highlighting the importance of integrating labor rights into resilient and socially inclusive post-disaster strategies, particularly in developing economies.

How to adapt urban economies and industrial development to natural hazards is a key focus of sustainable development goals. Existing research predominantly identifies the impacts of natural hazards and disasters at the aggregate economic level, leaving scope for analysis from an economic structural perspective. Taking seismic events in China’s mainland as an example, we followed the vein of evolutionary economic geography, and examined the impacts of earthquakes on regional industrial change. We integrated data from the Geocoded Disasters Database and the China Customs Trade Database to formulate a panel that covers more than 300 prefecture-level administrative units of China’s mainland. Our empirical findings indicate that earthquakes undermine export-based product entry via crowding out innovation resources, hampering the entry of new firms, and increasing business operation costs. Our work contributes to the dialogue in literature between evolutionary economic geography and disaster economics, while providing recommendations for policymakers and urban planners.

Effective disaster risk management requires bottom-up approaches that leverage citizen feedback to gain insights into disaster events on field. While much existing research in this domain focuses on social media platforms, the rich potential of government-operated digital feedback systems in capturing citizen experiences and concerns during disaster events remains underexplored. To address this gap, this study utilizes sentiment analysis to evaluate complaints submitted on the Pakistan Citizen Portal concerning flood and heat events. A hybrid methodology is adopted, combining VADER, SVM, LR, and BERT models to uncover sentiment trends and emotions expressed by citizens. Additionally, Latent Dirichlet Allocation is employed to identify key topics of concern, enhancing decision making and resource allocation. Our findings demonstrate the effectiveness of BERT in capturing sentiment trends while addressing the linguistic and cultural nuances inherent to the data. Emotions are detected based on Ekman’s six basic emotions, with sadness and anger emerging as the predominant ones. Key topics identified include property damage from floods and electricity problems related to heat events. The insights derived from this analysis provide a policy-relevant assessment of disaster risk management in Pakistan, illustrating how digital citizen feedback can inform resource allocation, preparedness planning, and communication strategies. By translating citizen sentiment into actionable insights, this study demonstrates the potential of digital platforms like the Pakistan Citizen Portal to enhance community resilience, strengthen preparedness, and support more responsive and inclusive disaster governance.

Mass casualty incidents (MCIs) resulting from compound disasters are characterized by a sudden surge in casualties that often overwhelms emergency medical services. In such situations, the efficient and coordinated transportation of casualties to hospitals becomes a critical component of the disaster response effort. However, current transportation strategies largely rely on static triage rules (for example, the immediate-first rule) and limited coordination between field, hospitals, and ambulances, which can lead to suboptimal use of scarce resources and to additional loss of life. To address these limitations, this study introduced the dynamic casualty transportation (DCT) model, a novel decision support tool designed to guide real-time transportation decisions during complex emergencies. The model dynamically prioritizes casualty transportation, aiming to maximize the total number of survivors. Unlike rule-based approaches commonly used in practice, DCT adapts to evolving field conditions and simultaneously determines both the optimal assignment of casualties to hospitals and the routing of ambulances. Numerical experiments show that DCT consistently outperforms the immediate-first rule, achieving up to a 59% improvement in survival under high-severity conditions when hospitals are nearby and over 300% when longer ambulance travel times are involved. Sensitivity analyses further confirm the robustness of the model to travel-time estimation errors. To enable practical deployment and support timely, data-driven decisions, we also proposed a real-time implementation architecture that integrates live streams from GPS, traffic-informed mapping services, mobile networks, and electronic triage assessments.

Emergency logistics is an important safeguard to ensure the safety of human beings, properties, and economic activities under major disasters and emergencies. This study proposed a vulnerability assessment framework for emergency logistics systems (ELS) from three dimensions: exposure, sensitivity, and adaptability, in which the principal component analysis and comprehensive index evaluation methods are adopted. Furthermore, a geographically weighted regression model (GWR) was established to explore the driving mechanism of vulnerability change of ELS. By taking provincial-level data in the mainland of China from 2010 to 2022, this empirical study found that ELS vulnerability showed strong fluctuation, in which the overall trend was upward during 2010–2018, but downward during 2019–2022. Meanwhile, ELS vulnerability was generally low in southeast China and high in northwest China, and strength of this spatial clustering showed a fluctuating upward trend. Four driving factors affected the spatiotemporal change of vulnerability—ecological threat level mainly positively drove the change, while natural environment protection level, logistics transportation capacity, and logistics fixed asset investment were negatively correlated. This study provides management implications for improving regional emergency logistics systems according to their spatiotemporal variation and local conditions.

Extreme weather events, particularly freezing rain (FR), pose significant threats to electricity infrastructure through ice accretion on transmission lines and towers. This study conducted a comprehensive national-scale assessment of FR impacts on China’s electricity infrastructure using an enhanced methodological framework. We integrated high-resolution (0.1°) gridded FR data spanning 2000–2019 with infrastructure data from OpenStreetMap, developing Gaussian kernel density estimation models to map damage probability distributions and employing generalized extreme value distributions to project future vulnerabilities. Our analysis quantified combined ice and wind loads on high-voltage (HV) lines and transmission towers, assessed damage probabilities, and evaluated the effectiveness of de-icing (DI) measures across different return periods (50, 100, 200, and 500 years). The results reveal distinct spatial patterns of risk, with Hunan Province consistently emerging as the most critical hotspot. Feature importance analysis using random forest methodology demonstrated that ice load dominated HV line failures (60–85% importance), while transmission towers exhibited more complex risk patterns with significant contributions from drag forces (30–50%) in southern regions. Model validation against the catastrophic 2008 ice storm shows strong predictive capabilities (R² > 0.95) for most provinces. Critically, while DI measures effectively reduce risk for moderate events (50-year return period), their effectiveness diminishes substantially for extreme scenarios (500-year return period), with high-risk categories persisting in provinces like Hunan, Hubei, Yunnan, and Zhejiang Provinces. These findings provide actionable insights for developing targeted, region-specific resilience strategies and highlight the need for adaptive risk management approaches that scale with event severity across China’s diverse geographical regions.

Seismic risk assessments are essential for mitigating and preparing for future seismic events and have been conducted globally, including in seismically quiet regions like eastern Canada. Although eastern Canada has relatively low seismic activity, it has experienced damaging earthquakes. Recent studies suggest that these events resulted from fault reactivation, indicating the potential for future earthquakes in the region. This article presents a scenario-based seismic risk assessment in the Timiskaming region of eastern Canada. Unlike previous studies of the region, which primarily conducted seismic risk assessments based on historical events, this study incorporated the identified causative fault of the 1935 Timiskaming earthquake, which had a moment magnitude of 6.1. The seismic risk assessment of this study was conducted on wooden buildings, as they represent approximately 90% of the buildings in the region. This study investigated the impact of two earthquake scaling relationships on fault-source characterization in eastern Canada. Because these relationships contain inherent uncertainties, rupture scenarios were generated under two conditions: (1) incorporating the uncertainties in the scaling relationships; and (2) using their median values. In the source-generation process, ruptures were either placed at the center of the fault plane or allowed to float along the plane. Additionally, the study evaluated the influence of two preferred ground-motion models (GMMs) and two site condition models on seismic risk in the region. The results indicate that the differences between the two GMMs and the two site condition models are more substantial than the differences between the two scaling relationships and the two source-generation approaches.

Tropical cyclones pose a significant threat to coastal regions through hazard-inducing factors such as wind, rainfall, and storm surge, whose interactions often lead to amplified impacts. Existing studies often fail to capture the complex dependence among these factors. This study focused on the coastal counties of Zhejiang Province, utilizing numerical simulation data of tropical cyclone-induced winds, rainfall, and storm surges from 1979 to 2022. A joint probability model based on the C-vine copula function was developed to characterize the synergistic mechanisms among these factors, and to analyze return periods and failure probabilities of engineering structures under different hazard scenarios. Furthermore, a comprehensive hazard index was introduced to assess the hazard of tropical cyclone events. The main findings are as follows: (1) The simulated data agreed well with observations, with root mean square errors below 4 m/s for wind and 0.2 m for storm surge, and correlation coefficients all above 0.75. (2) Neglecting multiple factors and their dependence introduced bias in the return period and failure probability estimates. For example, when the exceedance probability for each single factor was 0.05, the mean return period for the three factors under the independence assumption (1.760 years) was 35% shorter than that considering dependence (2.698 years). (3) The comprehensive tropical cyclone hazard in the coastal counties of Zhejiang exhibited a distinct spatial pattern, with higher values in the south and lower values in the north. This study provides a scientific basis for disaster risk management and the design of tropical cyclone protection infrastructure in coastal areas.

Bangladesh, a low-lying country in the Bengal delta, is highly vulnerable to tropical cyclones and associated storm surges, which pose severe threats to coastal communities and agriculture. This study evaluated the structural integrity and functional resilience of two coastal embankments (Polders 47/1 and 48) in Kalapara Upazila, Patuakhali District, in the coastal region. Employing high-precision RTK GNSS elevation profiling, storm surge modeling, post-cyclone inundation mapping, salinity assessment, and InSAR land subsidence analysis, this research provided a comprehensive understanding of embankment sustainability. First, a Trimble RTX-enabled RTK GNSS survey was conducted based on existing embankment geometry and elevation. Second, the storm surge from Cyclone Sidr was simulated using the Delft3D-FLOW model to estimate worst-case inundation extents relative to measured embankment heights. Third, Sentinel-1 SAR imagery and ground-truth data quantified actual flood inundation during Cyclone Remal in 2024, while Sentinel-2 optical indices validated against field electrical conductivity measurements assessed post-cyclone soil salinity changes. Finally, time-series InSAR with determined spatial subsidence rates estimated future changes in embankment elevations to evaluate future sustainability against cyclonic and storm surge events. The results indicate that large embankment segments, particularly in Polder 47/1, lie below simulated surge levels, leading to extensive overtopping and prolonged waterlogging. Salinity indices increased significantly post-Remal, and subsidence hotspots exceeded 14 mm/yr, undermining the long-term efficacy of the embankment. This study concluded that existing embankment infrastructure is insufficient under current and future climatic pressures. These findings emphasise the urgent need to integrate engineering advancements with nature-based solutions and community-involved monitoring to enhance coastal resilience and protect livelihoods.

Coastal flooding poses a significant threat to China’s coastal zones, driven by the combined effects of global climate change and rapid urbanization. However, owing to notable discrepancies in data accuracy, model construction, and parameter settings, the applicability of findings from existing regional studies is limited when extrapolated to broader macroscale risk assessments. Therefore, this study quantitatively assessed the risk of casualties and economic losses in China’s coastal areas under different return period scenarios using multi-source spatial data based on the LISFLOOD-FP hydraulic model with a fine 30-m spatial resolution. Extreme water levels corresponding to return periods of 20-, 50-, 100-, 200-, and 500-years estimated by historical observations from 65 tide-gauge stations were employed as boundary conditions to simulate the flood inundation. Taking inundation depth-damage curves into consideration, we subsequently quantified the spatial distribution of the population casualty rates and economic losses under different inundation scenarios. Our assessment results reveal pronounced spatial characteristics in coastal flood risk, with the most severe impacts concentrated in low-lying urban areas, such as the Bohai Rim regions, the Yangtze River Delta, and the Pearl River Delta. The results indicate that under the 500-year return period inundation scenario, the total flooded coastal area across 11 provinces reaches 20,983 km², with the number of casualties amounting to 176,000 and economic losses totaling CNY 303.8 billion yuan (about USD 42.2 billion). The high-resolution flood risk maps developed in this study provide spatial information and data support for national-scale coastal management, disaster risk reduction, and land-use planning in China’s coastal areas.

Urban flood risk poses an escalating threat to urban safety and sustainable development amid climate change and rapid urbanization. Although various flood risk assessment methods exist, most studies rely on single analytical approaches, neglecting the advantages of methodological integration and the multifaceted nature of risk characterization. Furthermore, prevailing assessment frameworks apply uniform hydrodynamic models across entire urban areas, inadequately capturing the diverse inundation processes arising from spatial heterogeneity of urban surfaces. This research developed an integrated multi-method framework for cities exhibiting significant spatial heterogeneity in environmental, infrastructural, and socioeconomic characteristics, enabling efficient high-precision flood simulation and risk assessment by coupling the indicator system method (ISM) with the cloud model (CM). The framework comprises: (1) spatially-differentiated hydrodynamic modeling for pipeline-dense and pipeline-sparse areas; (2) entropy-analytic hierarchy process weighted grid-based flood risk assessment across multiple rainfall scenarios; and (3) cloud model-driven risk evaluation at sub-drainage functional zones to address uncertainty in assessment. The results indicate that the integrated multi-model approach effectively captures flood formation mechanisms and identify an expansion of high-risk areas. Grid-based assessment delineates fine-grained risk distribution, while the cloud model assessment reveals the stability and uncertainty of risk levels. This research advances flood risk assessment methodology by bridging sophisticated hydrodynamic modeling integration with multi-scale risk evaluation, providing a robust framework for urban flood management.

When a forest fire occurs, timely and effective initial attacks can control the fire in its early stages, reducing the risk of spread and minimizing disaster losses. Enhancing the efficiency of forest fire suppression and the success rate of initial attacks has become a critical issue. Therefore, this study thoroughly investigated the key factors of the initial attack phase of forest fires, employing the weighted cross-entropy similarity and relative entropy models, combined with Geographic Information Systems (GIS) and machine learning technologies for precise simulation and analysis. The research introduced additional firefighting water reserve points in the study area to enhance the suppression capability and efficiency of unit firefighting resources, thereby increasing the success rate of the initial attack and reducing the losses caused by fires. An empirical study in Xichang City, Sichuan Province, China, demonstrated that this method significantly improves the success rate of initial attacks. Simulation results indicate that, under various weather conditions, especially extreme weather, the newly established firefighting water reserve points greatly enhance the success rate of initial attacks. This approach not only aids in the scientific planning of firefighting resources but also provides a practical foundation and theoretical guidance for future research to further improve the success rate of initial attacks.

This study aimed to evaluate the impact of extraordinary events and disasters on migration in Turkey in 2023. Multiple hazards, and accompanying social vulnerabilities and disasters have caused notable shifts in internal migration patterns. The research investigated the spatial and quantitative relationship between disasters and migration, emphasizing how different hazard types shape population mobility across regions. Data were obtained from multiple official sources: extraordinary event and disaster records from the Disaster and Emergency Management Presidency (AFAD) Integrated Disaster Management Platform (AYDES) database, internal migration and population statistics from the Turkish Statistical Institute (TÜİK), and spatial data analyzed through Geographic Information Systems (GIS). The 2017 Socio-Economic Development Index (SEGE) was also used to explore the connection between post-disaster migration and provincial development levels. The analysis employed correlation, multiple regression, and GIS-based spatial mapping techniques to examine links between disaster frequency, affected populations, migration rates, and socioeconomic indicators. Results show that not only disasters but also extraordinary events—which do not reach official disaster thresholds—significantly influence migration dynamics. Large-scale disasters such as earthquakes, floods, and fires triggered intense migration flows, while spatial analysis revealed distinct regional disparities, particularly across Eastern Anatolia, the Black Sea, and the Mediterranean regions. These findings highlight that disasters extend beyond physical destruction, reshaping demographic, social, and economic recovery processes. The study underscores the need for an integrated policy approach linking disaster management and migration planning to support resilient urban development and sustainable recovery.