Ten years after the adoption of the Sendai Framework for Disaster Risk Reduction 2015–2030, disaster risk governance remains one of its most ambitious yet unevenly implemented priorities, particularly in African contexts. While Priority 2 articulates a comprehensive vision of inclusive, coordinated, and multisectoral governance, many African countries continue to operate without updated disaster legislation or coherent institutional frameworks. This study critically examined how Priority 2 has been interpreted and operationalized in five African countries—Kenya, Nigeria, Egypt, Namibia, and the Democratic Republic of Congo—drawing on qualitative document analysis and a thematic framework derived from the Sendai Framework governance dimensions. The study found partial alignment with Sendai Framework’s aspirations, especially in legal reforms, multilevel planning, and stakeholder engagement in countries like Kenya and Namibia. However, persistent gaps remain in integrating disaster risk reduction into sectoral policies, institutionalizing participation, and ensuring transparency and accountability. The Sendai Framework’s emphasis on technocratic coordination and universal governance models often overlooks power dynamics, historical inequalities, and informal institutional realities, limiting its transformative potential. Participation is frequently symbolic rather than substantive, and risk is treated as a technical variable rather than a product of structural vulnerability. These findings underscore the need to move beyond compliance-driven governance models toward more context-sensitive, adaptive, and justice-oriented approaches. As global risk landscapes evolve, the post-2030 agenda must prioritize institutional learning, power redistribution, and inclusive decision making.

Companion animals are becoming increasingly common, and as natural hazards grow in frequency and severity, they play a critical role in guardians’ decision making about evacuation and shelter during disasters. Although many studies have explored the relationship between risk perception and willingness to evacuate, it remains unclear whether companion animals play a role in this relationship. This study investigated whether companion animal guardians exhibit a distinct risk perception-willingness to evacuate relationship compared to non-guardians during Category 1–2 and Category 3+ hurricanes. It also explored how guardianship characteristics, such as the number of animals or their dual role as support animals, influence this relationship. The findings indicate that being a guardian and the number of animals significantly affect willingness to evacuate and its connection to risk perception. For Category 3+ hurricanes, the presence of chronically ill animals further influences this relationship. Probability plots reveal that guardians have similar evacuation willingness as non-guardians at lower levels of perceived risk, but at higher levels of perceived risk, guardians show a significantly greater willingness to evacuate. Additionally, guardians with more animals are more likely to evacuate at a lower perceived risk but less likely at a higher perceived risk. For Category 3+ hurricanes, guardians of healthy animals show a higher evacuation willingness at lower levels of perceived risk than those with sick animals. These findings highlight the complex nonlinear role that companion animals play in evacuation decisions and provide insights into some of the contradictory evacuation behaviors by guardians reported in the literature.

Contemporary disaster risk management emphasizes the importance of mitigation and preparedness. Individual preparedness is essential for mitigating hazard impacts. This study examined earthquake preparedness behaviors through the lens of the precaution adoption process model (PAPM), a health psychology framework. The study aimed to identify differences among individuals at various PAPM stages regarding factors influencing preparedness. The sample consisted of 577 participants, aged 18 to 62, residing in Istanbul. After data collection, group comparison tests were conducted. The findings suggest significant differences between individuals in Stage 3 (undecided about acting) and those in Stage 5 (decided to act) regarding fatalism, perceived barriers, perceived benefits, subjective norms, and perceived behavioral control. Participants who decided to act (Stage 5) reported lower levels of perceived behavioral control than did those who had already acted (Stage 6). Future intervention studies should develop strategies to reduce fatalism and perceived barriers while enhance perceived benefits, subjective norms, and perceived behavioral control to encourage individuals to decide on performing earthquake preparedness behaviors. Increasing perceived behavioral control is essential in future intervention programs to facilitate the transition to action among individuals who have decided to perform preparedness behaviors. More comprehensive mixed-methods studies are needed to thoroughly identify the barriers to preparedness behaviors among individuals at different stages of the PAPM. In addition, more longitudinal studies are necessary to understand the dynamics between stages better.

International disaster response and humanitarian actors are important for mega-disaster relief, especially for disasters occurring in developing countries. China has been very active in international disaster response in the last decade, and both governmental agencies and nongovernmental organizations have been involved. This study investigated the narratives of the Chinese public on social media regarding the 2023 Türkiye-Syria Earthquake. Social media data from Weibo between 6 February and 5 March 2023 were collected, and topic modeling and emotion analysis were performed. The results show that the term “Türkiye Earthquake” was primarily used, followed by “Türkiye and Syria Earthquake,” while the term “Syria Earthquake” was used least. The general public tended to use the “Türkiye Earthquake,” while news media and institutions mainly used the “Türkiye-Syria” expression. The posts primarily discussed Chinese disaster and humanitarian response activities (including impacts, rescue efforts, and survivor stories), and the primary emotion expressed was positive. In posts about Syria, sanctions from the United States emerged as an independent topic, and negative emotions were associated with it. This study contributes to disaster studies regarding the public’s attitudes toward international disasters and humanitarian efforts using social media data on real cases.

The China-Pakistan Economic Corridor (CPEC) region faces persistent threats from natural hazards and disasters, posing significant challenges to sustainable development. While digital technologies and big data offer new solutions for disaster management, imbalanced digital resources and inadequate data management systems hinder their full potential. This study proposed the Digital CPEC platform, an open technology solution (using, for example, Python, PostgreSQL, MapServer) developed to share disaster-related open data in a fully digital format, thereby enhancing data accessibility and lowering economic and technical barriers. The platform offers a scalable and replicable model for strengthening disaster resilience in underdeveloped and transboundary regions worldwide, driving innovation in policy assurance, data integration, software support, and customized deployment. To ensure data integrity and security, four disaster management standards and a structured data integration framework were established, consolidating resources from seven thematic data corridors spanning natural hazard, ecological environment, and socioeconomic fields. Additionally, 18 metadata indicators and 38 identifiers were introduced into the metadata schema to enhance data quality and usability, alongside specialized analytical tools (for example, Echarts, area calculation) that improve user interaction. Its practical applications in pre-disaster risk monitoring, early warning coordination, and post-disaster recovery assessment demonstrate its strong knowledge-driven policy insights for environmental sustainability throughout the entire disaster management cycle at both the regional and national levels.

The traditional approach to probabilistic seismic hazard analysis (PSHA) relies on ground motion records, which restricts its application in regions with sparse seismic records or low seismicity. Recently, the 3D physics-based simulation (PBS) has been recognized as a more effective tool, which offers the flexibility to generate time histories of simulated ground motions. The PBS methods are essential for obtaining ground motion parameters and compensating for lack of records. In this study, building on the theoretical framework of the China Probabilistic Seismic Hazard Analysis (CPSHA) method, we integrated the hierarchical potential focal region model from the fifth-generation seismic ground motion parameters zonation map of China and an appropriate seismicity model reflecting spatial distribution characteristics. Ground motion parameters at the target scale were calculated using PBS for near-field seismic simulations and ground motion prediction equations (GMPEs) for far-field seismic predictions, accounting for the uncertainties in ground motion attenuation from both methods to compute the seismic hazard of each site. In this manner, we established a comprehensive regional probabilistic seismic hazard analysis method combining PBS and GMPE. Using Tianjin as a case study, a probabilistic seismic hazard analysis was conducted with this method, providing seismic hazard curves for specific sites within each administrative region and zoning maps, which were then compared with the results of the fifth-generation zonation maps. The results indicate that the calculated seismic hazard values are generally consistent with the fifth-generation map at the lower limit, while the upper limit is slightly higher due to the near-fault effect.

On 7 January 2025, at 09:05 local time, a M6.8 earthquake occurred in Tingri County, Tibet Autonomous Region, China, causing significant casualties and widespread building damage. In response to the urgent need for post-earthquake loss assessment, we proposed a comprehensive rapid assessment framework. This framework synergistically integrates remote sensing data, seismic intensity maps, building distribution data, building damage matrices, national census data, and regional building surveys to expedite the estimation of affected areas and the quantification of damaged bulidings. The methodology involved the development of structural damage matrices and seismic fragility curves specific to various structural types, facilitating the assessment of direct economic losses. Our findings reveal that over 70% of the buildings within the high seismic intensity zones (IX and VIII) near the epicenter were earth-timber structures, characterized by limited seismic resistance. This structural vulnerability status led to disproportionately higher rates of building collapse and severe damage compared to the areas with better seismic-resistant buildings. The assessment identified approximately 254,000 affected buildings within the epicentral and surrounding regions, with a total affected building area of 12.30 million m². The analysis of buildings at different damage levels showed that 26% of the buildings were slightly damaged, 15% were moderately damaged, 6% were severely damaged, and 3% collapsed. The direct economic losses from building damage was estimated at approximately CNY 3.62 billion. This study established a practical technical framework for rapid building loss assessment for earthquakes in the Qinghai-Tibet Plateau and adjacent regions, enabling timely and reliable evaluation of seismic impacts. The proposed methodology enhances decision-making efficiency during emergency response by providing critical information for response strategies. Furthermore, this study could offer a fundamental reference for post-earthquake recovery planning, supporting the development of targeted reconstruction efforts. More broadly, this study holds significant potential for improving disaster management in seismically vulnerable regions, particularly those with similar structural weaknesses and high seismic risk.

In the context of increasing frequency and impact of flood events, traditional methods for estimating flood depth have become insufficient to meet current demands, leading to a gradual shift toward machine learning approaches. This article reviews, for the first time, the applications of machine learning models—including both single and hybrid models—in flood depth estimation, referencing 108 relevant studies. Through statistical analysis, this research explored the most commonly used machine learning models and their primary data sources. Building on this foundation, we also examined the potential for integrating machine learning methods with smart city frameworks and artificial intelligence large models for flood depth calculations. The findings indicate that machine learning models excel in handling large-scale complex data and nonlinear relationships, and their performance can be further optimized through combinations with various models, significantly enhancing the accuracy and efficiency of flood depth estimation. However, these models also face challenges such as data dependency, model interpretability, and transferability. This review reveals the potential of applying machine learning models in flood depth estimation, providing directions for future research and reliable support for disaster prevention and reduction efforts.

Storm surges in the Western North Pacific cause significant economic damage and loss of life, highlighting the need for accurate storm surge predictions. This study evaluated four storm surge models: the Global Tide and Surge Model (GTSMv3.0), ERA20C neural network (ERA20C_nn), ERA20C multiple linear regression (ERA20C_ml), and 20th Century Reanalysis multiple linear regression (20CR_ml), using data from 160 tidal stations. The results show that the ERA20C_nn model outperformed others, with the highest correlation to tide-gauge observations. The GTSMv3.0 model follows closely, although slightly less accurate. The ERA20C_ml and 20CR_ml models were less effective, especially in predicting extreme surges. The ERA20C_nn model also provided more reliable estimates for 100-year return surge levels, outperforming other models. These findings suggest that neural network-based models, particularly ERA20C_nn, are better suited for assessing coastal flood risks in the region.

Assessing corporate financial risk exposure to floods facilitates strategic decision making to make prudent investments for risk mitigation. Financial models for flood risk assessment are informed by hydrological and financial data as well as geospatial information to capture corporate exposure through the lens of individual facilities’ risk. This article proposes an approach that integrates datasets derived from hydrological models with those from corporate financial records. Flood damage algorithms are then used to quantify both direct impacts on capital assets and indirect effects on business interruption loss (BIL). Capital stock losses are derived from the valuation of capital investments in fixed assets. The model was tested on publicly disclosed corporate records from four Japanese companies, to quantify under- and over-estimation of the approach, and to understand sources of uncertainty. The comparison demonstrates reasonable results with the modeled estimates, while also highlights the importance of carefully interpreting and selecting regional property damage curves and business interruption duration data. By using sales and investment in fixed assets as a baseline for business interruption and property damage, flood impacts vary based on corporate business activities, such as manufacturing or food and beverage. Two corporations show a variation in property damage criteria exhibiting greater values than those of interruption, implying that the former dominates financial risks. Mean changes in BIL parameters for another company show greater values, indicating its dominant role in financial flood risk. The results from expected annual damage assessment at the facility and corporate scale facilitate strategic investment decisions for flood risk mitigation.

Dike-break floods, characterized by high flood peaks, large volumes, and sudden onsets, seriously threaten the flood control and safety of river basins. In addressing the computationally intensive and time-consuming problem of numerical modeling of large-scale outburst floods, this study proposed a novel hybrid alternative modeling approach. The proposed methodology integrates a low-fidelity (LF) hydrodynamic model with a sparse Gaussian processes (SGP) model, incorporates rotated empirical orthogonal functions (REOF) to address high-dimensional data challenges, streamlines the model structure, and enhances computational efficiency. The SGP model uses training data from the high-fidelity (HF) model to rectify LF model results, enhancing computational efficiency while ensuring precise reproduction of HF model results. Validation in the Yongding River floodplain demonstrates that the hybrid model significantly improves flood extent and depth predictions compared to the LF model, with substantially lower computational costs than the HF model. The results indicate that the REOF-SGP model achieved probability of detection (POD) values higher than 0.8 and rate of false alarm (RFA) values lower than 0.2 within the 120-hour simulation period. The prediction error for inundation depth in the floodplain generally fell within the range of (−0.1 m, 0.1 m). The computational efficiency was 11 times higher than that of the HF hydrodynamic model. This method enhances large-scale flood inundation calculation efficiency while ensuring refined simulation of dynamic flood area changes, aiding rapid prediction of nonlinear flood evolution and water depth distribution.

The middle and lower reaches of the Yangtze River are key rice-producing regions in China. Conducting vulnerability assessments on rice production is beneficial for reducing agricultural disaster losses and improving China’s disaster prevention and control capabilities. This study used a crop growth model to fit hazard-loss rate curves (HLC) representing crop sensitivity to drought, employed an integrated index method to assess the adaptability and coping capacity, and developed a comprehensive vulnerability assessment framework from both physical and social vulnerability dimensions. The results suggest that, high adaptability is concentrated in Jiangsu and northern Zhejiang Provinces, while low adaptability is observed in western Hubei, Hunan, and Anhui Provinces. Approximately 25% of the areas exhibit relatively low coping capacity. Sensitivity levels exceeding 0.4 are primarily found in high-altitude regions, with a general trend of lower sensitivity in the north and higher sensitivity in the south. Comprehensive vulnerability is significantly greater south of the Yangtze River compared to the north, with a positive correlation between altitude and comprehensive vulnerability. Implementing targeted countermeasures based on vulnerability assessments can reduce rice losses in the region and ensure food security.

Lamar University established a Center for Resiliency in 2021 in response to the increasing natural and human-made disasters in the Southeast Texas region. Now more than ever a robust decision-making framework is essential for this recently established regional interdisciplinary resiliency center to make well-informed decisions, prioritize funding effectively, and nurture collaboration across various fields to help communities vulnerable to these threats. This article provides a progress update and presents a methodology integrating VOSviewer and Google Trends to develop such a decision-making framework for the Center for Resiliency at Lamar University in the Southeast Texas region. Four prominent study areas in resilience—Climate Stressors and Disasters, Mental Wellness, Energy and Optimization, and Resilience Planning—were identified. These findings were validated with real-time insights from Google Trends, ensuring practical relevance to recent resilience needs to provide an understanding of evolving resilience dynamics. Furthermore, the paper discusses the status of research conducted at the Lamar University Center for Resiliency, showcasing its commitment to fostering resilience through diverse initiatives across five academic colleges. Integrating VOSviewer and Google Trends offers a robust framework for informed decision making, aligning research efforts with the Southeast Texas region’s current and future resilience needs.