This article discusses the emerging practice of disaster branding, herein intended as a specific form of place branding associated with post-disaster reconstruction. Adopting Italy as a reference context, the article discusses renowned architects’ role in promoting a neoliberal and consumerist gaze on the affected places and steering public attention from post-disaster reconstruction shortcomings. To this aim, the article combines place branding theory and disaster critical theory as a theoretical base. Examining starchitects’ project descriptions in Italian post-disaster contexts, the article adopts critical discourse analysis to investigate the phenomenon over the last 15 years. Results show that some disaster and place branding keywords, like rebirth, identity, authenticity, effectiveness, and safety, are frequently adopted in the analyzed case studies, suggesting the emergence of a neoliberal “selling place” approach in post-disaster recovery. Furthermore, in the recent Italian post-disaster context, disaster branding appears to be a strategy serving the public institutional response to the emergency, showcasing efficiency and shadowing the fallacies of the reconstruction. In light of these initial findings, the article proposes disaster branding as a novel lens to explore place branding and built environment transformations in neoliberal post-disaster recovery.

In the decade since the adoption of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), there has been a growing recognition of the importance of integrating gender into disaster risk reduction (DRR). Achieving gender equality by 2030 remains a key objective of the UN Sustainable Development Goals. Yet, gender considerations are often insufficiently incorporated into policy design and implementation, with limited attention to how gendered experiences, particularly those shaped by deep-rooted cultural norms and social practices, influence people’s vulnerability, resilience, and recovery in disasters. This article examines how embodied gendered norms in Japan, especially those associated with women’s self-restraint and fear of being labelled Wagamama, exacerbate their vulnerability during disasters. Drawing on feminist scholarship and published testimonies from the 2011 Great East Japan Earthquake and the 2024 Noto Peninsula Earthquake, we argue that these unspoken and embodied social norms, particularly those structured around Wagamama and expectations of self-restraint, disproportionately constrain women, girls, LGBTQI+ people, and persons with disabilities, heightening their vulnerability during disasters. By shaping perceptions of roles, entitlements, and appropriate behavior, these norms operate as powerful mechanisms of social regulation that influence both exposure to risk and access to support in disasters. Recognizing these mechanisms is crucial for developing culturally grounded and truly inclusive DRR policies and programs.

This qualitative review examines recent developments in impact-based decision making in flood risk management, with a particular focus on bridging behavioral and engineering sciences. Floods pose a significant global threat, with increasing importance under climate change, but also with challenges in operational decision making, as needed to control structures and warn the population before and during an event. The review examines factors influencing decision makers, such as cognitive biases and bounded rationality, and evaluates interdisciplinary approaches to modeling these factors. It highlights advances in seamless weather forecasting and real-time impact assessment, emphasizing their integration into operational flood management to improve decision accuracy and trust in warning systems. Techniques such as ensemble forecasts and impact libraries are assessed for their potential to address uncertainties and improve risk communication. While many studies do not yet leverage theoretical foundations, this review highlights the potentials of prospect theory, regret theory, robust decision making, fuzzy set theory, and Bayesian decision theory in advancing flood management practices. Highlighting these theoretical applications reveals a gap in current flood management research, urging scholars and practitioners to explore how incorporating social science insights can better inform and enhance existing systems.

Although the human health risk from cold stress may be greater than from heat stress, population cold exposure has received little attention compared to heat exposure in the context of climate warming. A particular puzzle is that while the number of cold days has markedly decreased under climate warming, the cold-related influenza positivity rate has substantially increased. Here we reveal global hourly population exposure for different cold categories based on observations and climate model simulations from 1979 to 2100, and explore the potential link between population cold exposure and influenza positivity rate. Our results show that the number of cold hours did not decrease uniformly across all categories under climate warming, but shifted from extreme cold to moderate cold. Global hourly population cold exposure increased substantially from 1979 to 2023 (2.05 × 10¹⁰ person-hours yr⁻¹), and this trend is expected to persist in the near term with continued population growth. The number of influenza positives and the influenza positivity rate were significantly correlated with hourly population cold exposure. These findings imply a current risk of population cold exposure and emphasize the need for increased attention to this risk.

Exposed built-up land in flood areas is a vital indicator determining flood losses. It potentially changes in the context of rapid urbanization and climate change particularly in hotspot countries like China. However, a comprehensive understanding of the spatiotemporal patterns of flood extent and exposed built-up lands is hampered due to limited information of historical floods. To fill the research gap, this study developed the Spatial Dataset of Historical Floods in China (SDHFC) with event-explicit exposure from 2000 to 2020 using Google Earth Engine, based on internationally available flood event inventories and remote sensing-based surface water data. The Mann-Kendall test and rectified Theil-Sen trend analysis were applied to quantify the changes in flood extent and exposed built-up lands. The SDHFC delineated the inundation extent for 212 flood events in China during 2000–2020, demonstrating a notable improvement compared with global database. Both flood extent and exposed built-up lands increased significantly, with the latter growing at a rate of 9.65%·a^-1, approximately 1.8 times that of the former. Continuous expansion of built-up lands was identified as the primary driver (62%) for the rapid increase in their exposure to floods, much higher than the contribution of the observed flood extent (38%). The findings are crucial for understanding the complex interactions between flood patterns and urbanization processes in China in 2000–2020. The methodology could be applied in various regions for investigating long-term sequences of flood extent and exposed built-up lands.

Wind disasters often cause widespread power outages, posing a serious threat to the safe operation of cities. This article systematically reviews key advances in early warning research on wind-induced power outages based on the framework of risk source, risk exposure, and risk mitigation. For risk sources, existing studies focus on wind load forecasting and the mechanisms that induce tree failure. For risk exposure, research has matured in analyzing wind-induced transmission and distribution circuit failures, while critical node identification and fault-propagation modeling have garnered extensive interest. For risk mitigation, the application of physical reinforcing, network reconfiguration, emergency power deployment, and intelligent dispatch has markedly strengthened pre-event preparedness and mid-event response. However, several gaps remain in current research. (1) Wind-field modeling often relies on stationary-wind assumptions and single-parameter meteorological inputs, making it difficult to capture the complexity of dynamic processes under evolving, nonstationary typhoon winds. (2) In the urban environment, the cascading faults in the fault propagation mechanism of the power system have not yet been fully analyzed. (3) Power outage prediction models rely on static data and analysis of isolated variables. Mainstream machine learning methods overly focus on historical data fitting while neglecting the physical consistency of the disaster process. (4) Mitigation strategies often emphasize localized hardening and post-event response, lacking pre-disaster resilience planning and system design.

Urban pluvial floods have become increasingly frequent under the combined effects of climate change and urbanization, leading to increased disaster losses. This study developed a data-driven urban pluvial flood prediction model using convolutional neural networks (CNNs) to enhance computational efficiency while maintaining simulation accuracy. A high-resolution cellular-based flood model generated the training dataset through systematic patch-based sampling combined with fixed step size selection strategies. The established framework enabled flood simulations through integrated analysis of topographic features and rainfall processes. Shapley additive explanations (SHAP) and Group masking analysis (GMA) were implemented to interpret the decision-making mechanisms of CNN model. The model was validated in a relatively independent drainage area, demonstrating strong agreement with conventional cellular model outputs across six design storm scenarios and two historical rainfall events. Computational experiments showed that the CNN model reduced simulation time from minutes to seconds compared to process-based approaches, while maintaining low absolute errors in water depth predictions. Both SHAP and GMA interpretation revealed that topographic features, particularly building, digital elevation model (DEM), and aspect, exert dominant influence on model predictions. This data-driven framework established an efficient computational paradigm for urban flood modeling, with SHAP and GMA analysis guiding input variable selection while explaining model behavior. The methodology demonstrated potential for real-time monitoring integration, supporting rapid flood risk assessment and resilience enhancement.

Under global warming, compound heat and drought days (CHDDs) pose significant threats to both human health and agricultural production. This study systematically investigated CHDDs and the population and major crops exposed to the events across China from 1961 to 2022, using daily-scale temperature and standardized precipitation evapotranspiration index (SPEI). By integrating conventional percentile thresholds and crop-specific physiological stress temperatures for major crops, we employed a 3D visualization framework to quantify the co-occurrence patterns of extreme heat, drought, and CHDDs. The analysis revealed significant decadal changes: a sudden increase in CHDDs was observed in the late 1990s in eastern regions, while western China has shown a consistent upward trend since the 1980s. The highest exposure levels were concentrated in eastern China, where dense populations and extensive crop cultivation have driven a significant rise in both population and crop exposure since the 1990s, particularly for maize and rice. Pronounced regional disparities were evident in the characteristics of CHDDs: while drought days were more frequent in the northwest, southeastern China experienced the highest frequency and fastest growth rate of CHDDs. As a result, southeastern China has experienced the most significant increases in population exposure and the exposure of maize and rice crops. Meanwhile, the northwest arid region has seen rapid growth in population exposure and the exposure of maize and wheat crops, while Northeast China has shown a notable rise in maize exposure. These findings highlight the need for region-specific adaptation strategies to protect China’s food security and public health in a warming climate.

Accurate forecasting of regional building stock distribution is a crucial prerequisite for large-scale seismic risk assessment and the enhancement of regional disaster resilience. Marked regional disparities in China’s existing building stock and limitations in current macro-level forecasting methods necessitate improved prediction capabilities. This article introduces a regional classification-based framework for forecasting building stock. A four-dimensional variable system (comprising geographic, administrative, economic, and demographic variables) and Ward’s hierarchical clustering were used to classify 372 cities in China’s mainland into nine distinct regions (I–IX). Analysis of these regions revealed significant heterogeneity and a distinct spatial gradient in existing building stock levels (higher values in the East/Central regions compared to the West/Northeast). Regression analyses subsequently identified both GDP growth and construction industry value-added growth as strong predictors of building stock growth (r > 0.85) and uncovered an inverted U-shaped relationship between economic growth and stock growth rates. Based on these findings, region-specific, multivariate regression models were developed and validated against data from 2016 to 2020. Projections to 2025 using these models suggest continued stock growth across all regions, maintaining the observed spatial patterns. Collectively these findings provide a quantitative basis for differentiated urban planning, targeted policy interventions, and seismic risk assessments. Specifically, the framework supports urban renewal in high-stock regions, infrastructure optimization in less-developed regions, strategic planning for low-carbon building transitions, and targeted seismic risk assessments to enhance disaster mitigation for vulnerable urban systems.

While wildfires can be beneficial and part of a natural process, there have been numerous instances around the world, particularly in recent years, where wildfires have had devastating consequences for society. Weather conditions have created extreme wildfire behavior, resulting in speeds and intensities that can overpower suppression resources. It is ever more critical that communities and agencies take actions to mitigate and prevent wildfire disasters. We have developed a tool that enables wildfire practitioners to assess the risk of wildfire to structures in a straightforward, rapid, and affordable manner. The approach leverages information often collected by communities (for example, building footprints, zoning) and available vegetation datasets. In conjunction with local wildfire management regulations, our project also used wildfire exposure to help identify wildland-urban interface (WUI) boundaries. We used this approach on three communities in the Arctic (Anchorage and Fairbanks, Alaska, and Whitehorse, Yukon) to assess wildfire risk. We determined that there is considerable wildfire risk in urban Arctic communities, with a greater percentage of structures at high or very high risk in Fairbanks (26%) and Whitehorse (22%) compared to Anchorage (14%). Combining local wildfire management practices with wildfire exposure is a successful way to identify meaningful WUI boundaries, which are essential for obtaining mitigation funds and planning. The key to producing updatable wildfire risk and vulnerability maps is accurate, up-to-date information on vegetation, building footprints, and zoning. With this information and the tool outlined here, communities and agencies have a way to inform community wildfire protection plans and identify impactful mitigation actions.

The role of social capital (SC) in disaster response has been widely studied. However, comparative analyses of SC differences across community types in post-disaster contexts remain limited. Previous research has focused primarily on rural communities (RCs) and urban communities (UCs), while transitional communities (TCs)—increasingly prevalent amid China’s rapid urbanization—have received insufficient attention. This study compared SC in disaster response across UCs, TCs, and RCs in flood-affected Zhengzhou City, China. Employing a mixed-methods strategy, this study collected 1837 questionnaire responses in flood-affected communities, conducted analysis of variance (ANOVA), and used semistructured interviews and participant observations in three representative communities. The results identified bonding SC as the predominant form across community types, whereas bridging SC remains underdeveloped. Transitional communities exhibited the lowest overall SC in disaster response, UCs demonstrated the highest, and RCs showed moderate levels. These differences can be attributed to distinct community characteristics. Urban communities benefit from formal organizations, stable management structures, and broader external networks; RCs rely on strong internal ties despite limited external resources. Transitional communities face challenges due to high residential mobility, weakened social cohesion, and disorganized management structures. This study contributes to the understanding of SC in disaster contexts across diverse Chinese communities, particularly in TCs, and offers targeted policy recommendations to strengthen disaster response capacities through SC development.

Although the significance of public emergency participation is beyond doubt, the public’s emergency knowledge in China and the present societal demands have a considerable gap. Therefore, this study constructed a theoretical model of public’s emergency knowledge diffusion based on the weighted small-world network model, and explored the diffusion patterns and influencing factors of public’s emergency knowledge under the different selection strategies of emergency knowledge senders and different network intensities by MATLAB simulation. The results show that regardless of the intensity of the relationship in the public’s emergency knowledge diffusion network, determining the sender with the knowledge priority strategy can bring a higher emergency knowledge growth rate in the short term. In addition, public participation policies, protection laws, and information technology play a positive role on the diffusion efficiency of public’s emergency knowledge, while the diffusion cost has a negative impact. Compared with weak relation networks, the diffusion efficiency is higher in strong relation networks, and the diffusion process of public’s emergency knowledge in weak relation networks is more susceptible to the influence of external factors. This study not only fills the gap in the study of public’s emergency knowledge diffusion, but also provides a theoretical reference for the improvement of public’s emergency knowledge.

This study developed a machine learning-based framework for assessing roadway vulnerability and impacts in hurricane-prone regions, utilizing remote sensing techniques. To quantify the immediate and consistent impacts of hurricanes on the roadway network, the study developed two key metrics: the road closure impact index (RCII) and the roadway vulnerability index (RVI). The RCII assesses the severity of roadway closures by analyzing detected bounding boxes from high-resolution aerial imagery, offering insight into the spatial extent and severity of disruptions caused by each storm. In contrast, the RVI evaluates the consistency of roadway closure patterns across multiple events, revealing vulnerabilities within the transportation infrastructure through geospatial analysis. Also, by leveraging aerial imagery, remote sensing technology, and advanced machine learning models, the study assessed the impacts of Hurricanes Idalia and Debby on Taylor County, Florida, effectively classifying county roadway conditions in their aftermath into three categories: open, partially closed, and fully closed. Findings indicate that Hurricane Idalia caused significant structural damage due to wind and storm surge, while Hurricane Debby led to prolonged flooding and subsequent road submersion. By comparing the impacts of these two hurricanes, the study highlights the critical role of integrating machine learning, geospatial analysis, and remote sensing for enhanced disaster preparedness and response strategies. Ultimately, this framework provides critical insights for improving infrastructure resilience and planning efforts in coastal communities vulnerable to extreme weather events.

This perspective traces the emergence and evolution of early warning systems (EWS) and anticipatory action (AA). It revisits how major disasters, advances in science and forecasting, and global policy frameworks progressively expanded the ambition of EWS—from hazard-centered monitoring to more impact-based, people-centered approaches—while exposing persistent gaps in communication, local capacity, financing, and political will that prevented warnings from reliably triggering timely action. We examine how AA emerged in the humanitarian sector from these shortcomings as an operational bridge between long-term risk reduction and disaster response using risk information to enable pre-agreed, financed early actions, reviewing evidence that anticipatory interventions can improve food security, protect assets and livelihoods, enhance dignity and agency, and strengthen coordination in humanitarian contexts. Despite having the same goal of protecting the lives and livelihoods of at-risk populations, AA and EWS investments remain fragmented, and they struggle in fragile, data-poor, and conflict-affected settings. Drawing on this history and evidence, we argue that efforts to strengthen EWS and AA must be coherent and mutually reinforcing, building on the broader work on disaster risk reduction and climate-resilient development. The paper outlines concrete action points: align investments across EWS and AA; link government-led and humanitarian mechanisms wherever possible; strengthen inclusive, locally grounded partnerships; level up regional hydrometeorological and climate service capabilities; and institutionalize accountability and learning at scale. By acting on these priorities, practitioners and policymakers can move from pilots and parallel initiatives toward integrated systems that consistently enable people to act ahead of crises. The paper aims to address the following research question: From a humanitarian perspective, how have early warning systems and anticipatory action evolved in theory and praxis over the last three decades?