Despite the increasing global emphasis on decentralized disaster risk reduction (DRR), the capacity of local institutions to implement effective risk governance strategies remains inconsistent and often inadequate. This study conducted a comparative analysis of institutional vulnerability at the municipal level in two hazard-prone regions: Teziutlán, Mexico, and Tola, Nicaragua. It employed comparative case study methods alongside structured surveys administered to local DRR actors. An integrated analytical framework was used, synthesizing the Pressure and Release model, the Forensic Investigations of Disasters approach, the MOVE framework, and the Institutional Analysis and Development model. The research identified key institutional vulnerabilities, including preparedness, coordination, information access, infrastructure, legal enforcement, climate integration, and informal governance. The findings indicate that the deficiencies within these institutions are not merely technical but are significantly influenced by systemic disparities in power, resource allocation, and political stability. Both municipalities exhibit notable gaps between formal DRR mandates and their operational implementation, reflecting a structural disjunction between institutions articulated in policy (institutions-in-form) and those in practice (institutions-in-use). The study concluded that effectively addressing institutional vulnerability necessitates transitioning from compliance-driven reforms towards integrated strategies that foster institutional learning, enhance cross-sectoral coordination, promote climate adaptation, and acknowledge informal and community-based governance structures. These findings offer policy-relevant insights for national and regional policymakers seeking to strengthen the institutional foundations of local DRR implementation within Latin America and comparable decentralized governance contexts.
Given Türkiye’s high seismic risk, earthquake preparedness is vital. This study explored factors affecting earthquake preparedness behaviors among residents in high-risk areas. The factors affecting earthquake preparedness behaviors Scale based on protection motivation theory was developed, and its validity was tested. Scale development consists of a pilot and main application process. The pilot application was carried out with 50 participants, and the main application was carried out with 804 participants. The sample consisted of 804 participants who were divided into two groups for exploratory and confirmatory factor analysis. The sample was selected from 11 neighborhoods using the cluster sampling method. IBM SPSS 25 was used for exploratory factor analysis, while AMOS 24.0 was employed for confirmatory factor analysis. The final version of the scale consists of 24 items across four dimensions, with a Cronbach’s alpha of 0.883, exceeding the acceptable threshold of 0.7 for each dimension. The Kaiser–Meyer–Olkin value is 0.898, and Bartlett’s test of sphericity is significant, with the total variance explained being 51%. Confirmatory factor analysis results indicate a good fit for both the measurement and structural models (χ2/df = 1.779; RMSEA = 0.44; CFI = 0.96; NFI = 0.91; TLI = 0.95; p<0.001). Structural equation modeling showed that, among the constructs of protection motivation theory, perceived efficacy (β = 0.690) is the most important factor affecting earthquake preparedness behaviors. The results show that the scale is a valid and reliable tool to determine the factors affecting earthquake preparedness behaviors in Turkish society.
The purpose of this study was to describe and analyze how and why contradictions recurrently arise regarding how incoming emergency calls at dispatch centers should be assessed, sorted, and handled. The study is based on data from documents, study visits, and interviews with representatives from dispatch operators in a Swedish context. The results identify competing alarm and healthcare logics that are incompatible, which leads to contradictions between the national and regional approaches and, consequently, makes collaboration more difficult and affects the precision of emergency call assessment. The study also shows that the assessments of emergency calls are governed more by which logic is applied than by the assessor’s formal competence. The study highlights the importance of feedback and subsequent analysis from the health service to the initial call assessment stage, which can improve the alarm logic’s weaknesses with over-triage, without extending the response time. We also focused on the mechanisms required to develop and maintain a long-term sustainable alarm function.
This study was conducted to develop a comprehensive framework for empowering women who work in disaster risk governance in Sri Lanka. Women’s empowerment in disaster risk governance has been identified as a strategy to reduce women’s vulnerability to disasters and strengthen the disaster risk governance system towards building a resilient society. The study was conducted within the Sri Lankan disaster preparedness system because of the high disaster profile and the lower level of women’s empowerment in the decision-making system. A case study strategy was employed for data collection. Three highly disaster-prone districts were selected and we conducted 26 semistructured case study interviews. In addition, 14 expert interviews were conducted for better triangulating the results. Thematic analysis and cognitive mapping were adopted for data analysis and identifying strategies. Based on the study findings, a comprehensive framework was developed with four intervention mechanisms: individual, community, organizational, and legislative. Each group of interventions was divided into primary and secondary actions based on their priorities. The validated framework will guide policymakers and practitioners in supporting women’s empowerment in governance with the ultimate objective of enhancing societal resilience.
From 10 to 15 April 2025, China experienced a rare persistent extreme wind-dust compound disaster that swept from north to south. Based on observational data, historical disaster records, and situations of various exposed elements, this study analyzed the formation mechanisms and evolution of this extreme event and conducted a rapid assessment of the associated loss and damage. The results indicate that the direct cause of this extreme wind-dust compound disaster was a strong cold vortex system generated in Mongolia, which moved eastward and southward, combined with the amplification effects of topography and urban structures, and the downward transmission of momentum from higher troposphere. The analysis revealed that approximately 697.47 million people were exposed to strong winds, while about 1,374.54 million people were exposed to high concentrations of PM10. The strong winds also caused varying degrees of damage to buildings, transportation networks, agricultural greenhouses, and forests. Based on vulnerability curves for wind-related loss and damage, it was estimated that the number of victims affected by this extreme wind-dust compound disaster ranged from 0.209 to 1.044 million, with casualties between 5 and 13 individuals. The number of damaged buildings was estimated to be between 2115 and 4607, and the area of affected crops was between 229 and 783 km2. The direct economic losses could reach as high as RMB 0.076–3.501 billion yuan. This study revealed the causes of this extreme wind-dust compound disaster and quantified the disaster loss and impact, providing new insights for the prevention of associated disasters.
In the face of disasters, a strong organizational network is the foundation for effectively accomplishing emergency relief tasks. In an emergency response network comprising tasks and organizations, the failure of certain organizations may cause large systemic losses owing to internal component associations. To analyze the response system’s robustness, we developed emergency response networks based on the associations between organizations and tasks. A cascading failure model was established considering task reassignment after organizational failure, and indicators in terms of tasks and structures were identified to observe robustness. In the proposed model, we developed random, bond-based, and bridge-based organizational failure modes, and average, capacity-based, and surplus-based reassignment programs. To validate the model, simulation experiments were conducted in the context of extreme rainstorms. The results show that bridge-based failures were the most damaging to network systems, and the average reassignment program was the least effective. The analysis of model parameters illustrates the critical effectiveness of individual organizational capability in enhancing system robustness. The proposed framework and model enrich the study of emergency response networks with favorable applicability, and the results can provide theoretical references for emergency management practices.
Flash floods are characterized by their destructive power, rapid onset, and unpredictability, often causing severe damage to both natural environments and socioeconomic systems. Understanding the detailed disaster-causing mechanisms of flash floods is critical for effective disaster risk reduction. However, current studies have not captured the comprehensive circumstance of flash floods that integrates environment, hazard, and exposure from the perspective of disaster systems theory. To address the gap, this study established a systematic framework for comprehensively evaluating flash floods disaster-causing mechanisms in ungauged mountainous micro-watersheds by integrating multi-source data, including remote sensing observations, meteorological station data, unmanned aerial vehicle measurements, and participatory geographic information system data, with hydrological-hydrodynamic and statistical models. The proposed framework consists of four interconnected steps: design storm estimation, flash flood process simulation, critical rainfall calculation, and disaster loss evaluation. Through a case study conducted in Qialegeer Village, Xinjiang, China, we demonstrated the framework’s applicability by reconstructing flash flood scenarios, including the 2017 event as well as those of 10 and 20 years return periods. The results demonstrate that our framework robustly and systematically elucidates flash flood disaster process in the region with high reliability. Furthermore, it is adaptable to other ungauged mountainous micro-watersheds. This framework ultimately serves to enhance disaster risk mitigation and build resilience in vulnerable mountainous communities.
Floods are one of the most frequent natural hazards worldwide. Accurate flood risk mapping is critical for effective flood management in flood-prone areas. In this study, we employed the multi-criteria decision analysis (MCDA) method to develop a flood risk map that combines flood susceptibility and vulnerability factors. Three machine learning models—random forest (RF), XGBoost, and LightGBM—were selected as the basic classifiers for creating flood susceptibility maps. Historical flood data and 13 flood-influencing factors were extracted for machine learning training. Model performance was assessed using precision, recall, accuracy, F1-score, and AUC through 5-fold cross-validation. All three models performed well, but RF slightly outperformed the other two according to the evaluation results. We used the analytic hierarchy process (AHP) method to combine the flood susceptibility map generated by the RF model with flood vulnerability indicators to produce a flood risk map. Our findings demonstrate that integrating advanced machine learning techniques with MCDA method offers an effective approach for flood risk assessment, providing a robust foundation for decision making in flood risk management.
This study introduces antifragility as a transformative lens for disaster risk governance, shifting emphasis from restoration to disruption-induced improvement of systems. We distill six principles for operationalizing antifragility in disaster risk reduction contexts and delineate ethical, systemic, and learning-based implications for future resilience. Together, these elements reframe disaster risk governance as dynamic, adaptive, and self-reinforcing amid compounding climate risks.