While international disaster risk reduction frameworks place household preparedness at the center of resilience strategy, substantial proportions of people living in hazard-prone areas remain underprepared. We develop an integrated framework combining protection motivation theory, the capabilities approach, and social contract theory to explain why some residents prepare while others do not. Drawing on survey data from 159 adults and 27 qualitative interviews, we analyze how sociodemographic characteristics, hazard experience, risk perception, residence status, neighborhood, and institutional trust relate to preparedness in Squamish. Preparedness levels were modest, with respondents completing a mean of 4.21 out of 10 preparedness actions. Lower formal education was the strongest negative predictor, while hazard experience and hazard concern were consistent positive predictors. Perceived hazard likelihood did not predict preparedness and, in some resident-only models, was negatively associated with participation in drills and training. We conclude that in Squamish, preparedness is shaped by an individual’s hazard concern, what they are materially able to do, and whether they believe institutions will uphold their side of the social contract.

Community formation in socio-spatial human networks is an important mechanism through which populations cope with and mitigate the impacts of extreme weather hazards. However, limited research has examined the latent network characteristics that shape community formation in human mobility networks during natural hazard-related disasters. In this study, we analyzed human mobility networks in Harris County, Texas, during the managed power outages associated with Winter Storm Uri in 2021 to detect communities and evaluate their underlying characteristics. Specifically, we examined three dimensions of the detected communities: hazard exposure heterophily, sociodemographic homophily, and social connectedness strength. The results show that population movements were shaped by sociodemographic homophily, heterophilic hazard exposure, and social connectedness strength. We also found that communities containing a larger share of high-impact areas tend to drive population movements toward areas with weaker social connectedness. These findings highlight key characteristics that shape community formation in human mobility networks during hazard response. More broadly, the findings suggest that power utility operators should account for the characteristics of socio-spatial human networks when designing managed power outage strategies.

Disaster and emergency management (DEM) systems are increasingly characterized by distributed authority, multi-agency interdependence, and complex hazard environments. While prior research has reframed DEM as a complex adaptive system, less attention has been given to how coherence, adaptive coordination, and legitimacy are sustained across decentralized networks where no single actor holds complete mandate or authority. This study examines system stewardship as a meta-governance function capable of addressing this gap. Drawing on constructivist networked grounded theory, the study analyzes 38 semistructured interviews with 40 practitioners across Aotearoa New Zealand’s DEM system. The analysis revealed four themes: governing a distributed system; enabling coordination through trust and inclusive participation; sharing information and sustaining learning; and sustaining capability over time. The study integrates these findings into a three-pillar framework of system stewardship comprising relational, informational, and institutional dimensions, each representing a distinct but interdependent set of enabling conditions for adaptive coordination. The study argues that sustaining DEM performance requires moving beyond episodic leadership during events toward the deliberate sustaining of these enabling conditions across the system as a whole. In doing so, it contributes a complexity-informed governance framework applicable to DEM systems operating under conditions of decentralization, uncertainty, and compound risk.

Increasingly frequent disasters and the heightened vulnerability of older adults highlight the need for equitable access to lifesaving resources such as evacuation shelters and emergency supply storage. This study evaluates whether the spatial accessibility of these facilities aligns with the distribution of older residents in core and non-core areas of Pudong New Area, Shanghai Municipality. We develop a spatial equity framework employing bivariate local Moran’s I, Lorenz curves, and Gini coefficients to quantify alignment and inequity, using data from the First National Survey on Natural Disaster Risks (2020–2022). Our analysis uncovers a pronounced spatial disconnect. Both shelters and supply storage cluster in the urban core, leaving older adults in peripheral areas with substantially lower access. Shelters correspond more closely to general population clusters than to older adult clusters, and this misalignment worsens as evacuation time thresholds increase. Emergency supply storage exhibit a more even distribution and a positive spatial correlation with ageing communities, yet Lorenz curve analysis reveals persistent inequities outside the core. These results demonstrate that current facility siting amplifies disaster inequities for older adults. We therefore recommend that emergency planning move beyond per capita targets to incorporate spatial accessibility metrics and age-sensitive siting strategies, ensuring that vulnerable populations have timely access to shelter and supplies when disasters occur.

Universities represent a unique context in disaster scenarios, housing millions of students who are especially vulnerable in crisis situations. While universities often have various mental health services for students and staff, there is a lack of research on how those services are incorporated into disaster and emergency management (DEM) communications, particularly through their public-facing websites. Drawing on risk and inoculation communication theories, proactive messaging involving components of forewarning and refutation promote increased community and individual resilience capabilities. The purpose of this study was to examine how the websites of DEM units at 51 Canadian universities communicated mental health for disaster preparedness through the lens of risk communication, with a focus on forewarning, refutation, and resource awareness. Findings demonstrated that fewer than half of universities communicated about internal and external mental health services while 88% communicated physical safety measures. University website messages varyingly used explicit or implied forewarning such as being incorporated into guidance for specific types of emergencies like personal and campus violence, with one case where resources were provided alongside a video simulation of a campus attack scenario. Almost all university communications about disaster mental health included information about resources in lieu of a refutation for building resource awareness and student, staff, and faculty self-efficacy with two notable exceptions. Documenting the disconnect between communicating physical safety and mental health in higher education DEM, these findings have implications for research, practice, and policy of proactive DEM communications involving mental health.

Coastal cities in southeastern China face increasing threats from typhoon-induced compound disasters (for example, torrential rainfall, urban waterlogging, and storm surges) that can cascade into interconnected disaster chains under climate change and rapid urbanization. However, dynamic multi-scale assessments of resilience to such compound disasters remain limited. This study develops an integrated framework that combines multi-scale geospatial analysis with explainable machine learning (XGBoost-SHAP). Using Fujian Province as a case study, we assess typhoon disaster chain urban resilience (TDCUR) in 2010, 2015, and 2020 across grid, administrative unit, and watershed scales, characterize spatiotemporal patterns, and apply XGBoost-SHAP as a post hoc diagnostic to summarize nonlinear indicator-TDCUR association patterns and their spatial concentration under the predefined TDCUR framework. The results indicate that: (1) Provincial TDCUR increased by 6.9% and regional disparities converged, yet major coastal cities experienced declining resilience despite strong economic development; (2) Resilience showed pronounced spatial polarization, with low-resilience cold spots expanding by 48% and clustering in the Xiamen-Quanzhou area; (3) Machine learning diagnostics indicate that typhoon-strong wind-storm surge sensitivity (B8), typhoon-rainfall-flood sensitivity (B7), and impervious surface proportion (A2) show the strongest model-based associations with the spatial variation of TDCUR and display significant interaction effects; and (4) SHAP-based spatial diagnosis identifies the Xiamen-Quanzhou-Fuzhou coastal belt and the Jinjiang Basin as priority areas with concentrated low TDCUR and high cumulative SHAP magnitudes. The proposed framework is transferable and can support spatial screening for targeted resilience actions in coastal regions, with implications for SDG 11.

Coseismic landslides often occur extensively within a short period after an earthquake, posing severe challenges to emergency response and disaster risk reduction. The existing prediction methods generally fail to balance the dual demands of timeliness and accuracy and exhibit limited generalizability across regions with different geological and tectonic settings. In this article, a staged spatial prediction architecture (SPA) for coseismic landslides is proposed. In the rapid prediction stage, the model leverages readily available topographic, geological, and simplified seismic factors, combined with transfer learning, to rapidly generate landslide probability maps under label-scarce conditions in the target domain. In the accurate prediction stage, the measured seismic parameters and fault distance are introduced, and the rapid prediction outputs are incorporated as prior information to enhance the spatial characterization capability of the model. The results show that the rapid prediction stage achieves an area under the curve (AUC) of 0.86 even without labeled data, which can be attributed primarily to effective feature adaptation via transfer learning, whereas the accurate prediction stage further improves the AUC to 0.91 via the integration of high-resolution factors and prior information, significantly improving the delineation of high-risk zones. An ablation study confirmed the distinct contributions of both the transfer learning and prior fusion components to the overall performance. Furthermore, the extension of the proposed framework to pre-earthquake coseismic landslide forecasting is explored, achieving an AUC of 0.85 and demonstrating its potential for use in broader disaster risk management scenarios.

Debris flows pose significant threats to mountainous regions, necessitating accurate activity assessments for effective disaster mitigation and risk management. At a regional scale, debris flow studies have predominantly focused on susceptibility, without adequately addressing frequency and magnitude of these events. However, growing demands for hazard mitigation call for more detailed and comprehensive debris flow activity assessments. This study developed an integrated spatiotemporal debris flow activity assessment framework by combining spatial susceptibility modeling, temporal probability estimation, and potential event magnitude estimation. The assessment results for the Eastern Himalayan Syntaxis successfully identified historically active watersheds, including those impacted by catastrophic debris flows such as the 1953 Guxiang Glacier event. The study area was classified into five activity levels, with 37.8% of watersheds categorized as high or very high activity zones. Quantitative validation further confirmed the method’s reliability: the average debris flow occurrence rate increases markedly from 0.02% in the very low activity zone to 86.9% in the very high zone. Compared to traditional susceptibility models, the proposed framework achieves more accurate assessments by integrating temporal triggers and event magnitude, yielding a 9.4% improvement in area under the receiver operating characteristic curve (AUC). This approach provides a robust tool for land-use planning, infrastructure protection, and disaster risk reduction in debris flow-prone regions, particularly under changing climate conditions.

Future changes in agricultural drought severity and extent are a paramount concern for global food security. However, projections remain uncertain due to the inherent challenge of consistently defining moisture deficits across varied climatic zones. Prevailing assessments, which predominantly focus on long-term mean moisture trends, often overlook the rapid onset of discrete drought events—especially in humid regions. To bridge this gap, we utilize the self-calibrating Palmer Drought Severity Index (scPDSI), driven by the latest CMIP6 multi-model ensemble, to quantify the future dynamics of droughts in terms of frequency, duration, and intensity. Comparing a historical baseline (1980–2014) with future periods (2015–2049, P1; 2050–2084, P2) under three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585), we project that global croplands will enter mild drought by 2060. Compared to the historical baseline, during the P2 period, drought intensity and duration could increase by up to 29% and 370% under SSP245, while drought frequency is expected to rise by up to 87% under SSP585. Global drought severity is projected to intensify to 1.06 and 1.36 times historical levels in P1 and P2, respectively. Under lower-emission scenarios (SSP126 and SSP245), the average intensity of extreme agricultural droughts decreases by 3%. In stark contrast, under SSP585, drought-affected areas are projected to double, expanding by 9.2-fold for extreme droughts (from 1.4 to 12.5% of global croplands). Notably, our analysis identifies clear “humid-region” hotspots, where drought frequency escalates more sharply, underscoring the emergence of significant agricultural risks in traditionally precipitation-reliant regions.

Publicly listed companies are increasingly disclosing climate-related financial risks to their businesses since the promulgation of the Task Force on Climate-related Financial Disclosures (TCFD), and more recently under the climate-related disclosures issued by the International Sustainability Standards Board (ISSB). Hence, financial risk exposures associated with geographically-distributed operations and business activities will need to be quantified and benchmarked. While extant research on individual companies or facilities has been available, no prior methodologies have explored these systemic risks based on industry sector classification in the context of listed indices. In this study, we analyzed the characteristics of corporate financial flood risks across regions and industry sectors for the constituent components in the Nikkei 225 Index, representing 225 companies and more than 18,000 facilities. The modeling approach integrates hydrological datasets with multiple climate models and financial records of corporations and facilities. Using estimated property damage (using plant, property, and equipment or PP&E investment proxies) and revenue losses at the facility, corporate, and industry scales, the expected annual damage (EAD) exceeds USD 8.2 billion by 2030. Approximately 60% of losses is derived from property damage to Asian-based facilities, while business interruption losses are driven by European and Asian operations. When benchmarked to earnings, several sectors and companies with high capital asset valuations, including utilities, information technology, consumer discretionary, and industrials show loss ranges exceeding 5%. While financial flood risk was not weighted based on index component allocations, sectors with high fixed asset investments and business interruption losses expose the index to significant impact from extreme weather events.

Self-built buildings in rural areas exhibit varying levels of seismic performance, and despite the transformative development in the region over the past decades, such buildings continue to exist in Shanghai Municipality. This study conducted field research and theoretical calculations to gain an overall understanding of the seismic performance of these buildings. In this study, 819 self-built buildings were classified according to their age, field research was conducted to test mortar strength and connection reliability, and the seismic performance of these buildings was analyzed. The results confirm that a considerable stock of unreinforced masonry buildings constructed in the peri-urban townships of Shanghai, especially those erected prior to 1990, exhibit markedly deficient seismic performance and are highly vulnerable to collapse or severe damage from earthquakes. This poses a significant threat to catastrophic casualties and disproportionate economic losses. Consequently, a systematic, high-resolution survey coupled with targeted seismic retrofitting is urgently required to mitigate earthquake-induced disaster risks in building inventories.

Emergency evacuation signage, particularly exit signs installed along building corridors and evacuation routes, is critical for life safety during emergencies. Traditional signage design generally prioritizes maximizing visibility and spatial coverage. However, excessive sign installation may introduce visual clutter and interference among overlapping guidance cues, thereby limiting further improvements in evacuation performance. Therefore, a two-stage method is proposed to explore the optimal balance between luminous performance, installed quantity, and spatial distribution of exit signs for efficient evacuation guidance. First, a controlled experiment involving 30 participants was conducted to establish a U-shaped psychophysical relationship between achromatic contrast and the maximum recognition distance (MRD). The relationship is largely independent of both ambient illumination and observer gender. Second, the MRD data were integrated into Pathfinder to systematically assess how occupant density and sign quantity jointly influence evacuation efficiency. The results demonstrate that the optimal signage configuration shows significant density dependence. In the tested corridor scenario, three signs yield the shortest evacuation time at high occupant density, whereas fewer signs achieve comparable or better evacuation performance at medium and low densities. Moreover, a saturation effect is identified in the benefits of MRD improvement. Once the threshold is exceeded, further increases in MRD yield little or no additional improvement in evacuation efficiency. These findings are discussed in terms of hypothesized interference from overlapping guidance cues and congestion from overlapping signs. The study suggests that future signage design guidelines may benefit from incorporating density responsive and performance-based evaluation. Further experimental and field validations are needed to support broader standardization.

This article introduces a comprehensive framework to explore the relationship between access constraints and information deficits in humanitarian logistics. By integrating resource dependence theory (RDT) and institutional theory (IST), we examined how barriers such as movement restrictions, violence, and the presence of mines impact resource flow and operational efficiency. Drawing on RDT and IST, we analyzed data from 150 crisis events across 93 countries, encompassing nine indicators of humanitarian access constraints and information gaps. Utilizing advanced deep learning techniques, including the multi-layer perceptron (MLP) model with permutation feature importance analysis, our findings reveal that restrictions on movement within countries, violence against personnel and facilities, and administrative obstructions are the most significant predictors of information gaps. These constraints significantly hinder humanitarian responses by disrupting resource distribution and violating institutional norms. The findings highlight the primacy of access-related barriers in undermining timely and accurate needs assessment, providing empirical support for the theoretical integration of RDT and IST. The framework offers actionable insights into crisis information dynamics and informs more targeted and resilient response strategies.

With the growing complexity and frequency of humanitarian crises globally, incorporating resilience and sustainability criteria into the supply chains of humanitarian organizations has become essential. This study aimed to enhance the resilience and sustainability of the humanitarian supply chain by integrating economic, resilience, environmental, and social aspects. The Delphi method was employed as the research method, and the best-worst method (BWM) was applied to compute the context-specific supplier evaluation criterion weights. As an outcome of this research, the current and desirable supplier evaluation criteria were identified, and a holistic supplier evaluation framework was developed for humanitarian organizations (HOs). The study revealed that the integration of environmental criteria in supplier evaluation is still in its emerging stage. Moreover, increasing stakeholder engagement and securing donor support are critical for achieving resilience and sustainability in humanitarian procurement. This study provides an easy step-by-step supplier evaluation process that can be applied by HOs to enhance sustainable and resilient humanitarian procurement.