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In most disasters that have been studied, the underlying dangerous cause does not persist for very long. However, during the COVID-19 pandemic a progressively emerging life threat remains, exposing everyone to varying levels of risk of contracting the illness, dying, or infecting others. Distancing and avoiding company have a great impact on social life. Moreover, the COVID-19 pandemic has an enormous economic impact for many losing work and income, which is even affecting basic needs such as access to food and housing. In addition, loss of loved ones may compound the effects of fear and loss of resources. The aim of this paper is to distil, from a range of published literature, lessons from past disasters to assist in mitigating adverse psychosocial reactions to the COVID-19 pandemic. European, American, and Asian studies of disasters show that long-term social and psychological consequences of disasters may compromise initial solidarity. Psychosocial disruptions, practical and financial problems, and complex community and political issues may then result in a 'second disaster'. Lessons from past disasters suggest that communities and their leaders, as well as mental healthcare providers, need to pay attention to fear regarding the ongoing threat, as well as sadness and grief, and to provide hope to mitigate social disruption.

This chapter contains sections titled: Introduction The Magnitude of the Damage Produced by the Event Exceptional External Agent The Nature of the Agent Threat to the Social System Social Vulnerability Post‐Modern Perspective Disasters are Political Events Scapegoating in Disasters A Disaster Unmasks False Myths Victims or Damaged? Compensations in Disasters References

After the 5.12 Wenchuan Earthquake which took place on May 12, 2008, aerial remote sensing has rapidly covered all the influence sites of the earthquake, emergency aero-photo interpretation has revealed the disaster distribution limitation, and captured the information of location, dimension of the destroyed houses, roads and other structures, blocked rivers, etc, which provided destroying conditions of the time for rescuing lives, rebuilding traffic lines and estimating disaster situation. The further interpretation and analysis indicate that large scale second growth geological disasters mainly distribute in the distance of 0–300 km and 45°–50° orientation from the epicenter. About 137 latent large-scale landslides and debris-flows will occur in this rain season and coming several years, so how to avoid current geological disasters as well as the latent large disasters should be considered in making rebuilding and developing plan.

The coronavirus disease 2019 (COVID-19) pandemic has already exerted an enormous impact on the entire world. Everything is overwhelmed in the face of a rapid escalation of cases. The countries that have already reported the peak of transmission are easing their preventive measures yet fearing a second wave of infection. If the virus causes that next wave, are we sufficiently prepared to deal with it? I argue that the stakeholders concerned should simultaneously handle the ongoing pandemic while making effective preparations for its second wave. To relax the preventive measures, countries must thoroughly revisit their situations based on scientific evidence.

Background: This paper addresses the need to clarify and highlight the vital roles non-state actors play in strengthening the disaster management ecosystem, drawing on knowledge and experience across sectors and entities. The objective is to underscore the irreplaceable roles of non-state actors in disaster response and the need for shared capacities through the coordination, adoption, and application of agreed-upon protocols across actors and contexts. The research’s ultimate goal is to provide policymakers, crisis managers, non-state actors, and volunteer coordinators with a comprehensive overview of the functional areas, competencies, and capacities of civic organisations across all phases of disaster management. Integrating these organisations into existing governmental crisis management systems offers an opportunity to enhance community resources and capacities through unified communication and interoperability protocols based on existing technical and ethical standards. Methods: The research reviews academic literature, legal and policy frameworks, and grey literature, including recommendations and experiences documented in a repository of 140 CORDIS EU-funded initiatives that illustrate expert and institutional opinions on disaster management. The manuscript also relies on secondary data analyses presenting the opinions collected from 50 participants in an interactive group exercise on the role of non-state actors and volunteers. It further draws on aggregated knowledge from nine consultative workshops involving 20 civic and governmental organisations, synthesising practices, formal standards, robust coordination frameworks, and command-and-control system rules into an innovative voluntary disaster response protocol for non-state actors and volunteers. The findings demonstrate the value of non-state actors in disaster management and how gaps in their engagement can create opportunities to strengthen the disaster management ecosystem by enhancing the cohesion of capacities and resources. Compared with international standards (INSARAG, etc.), a protocol incorporating technical and integrity norms in an accessible, adaptable format emphasises the importance of integrating non-state actors into the formal disaster crisis management system. Conclusions: Establishing a set of standards for coordinated awareness and response, facilitated by continuous communication of roles and competencies among disaster responders at both local and international levels, is essential for the sustainable mitigation of negative impacts before, during, and after emergencies or catastrophic events.

Providing temporary housing (TH) units after natural hazards and social conflicts is often an urgent necessity. Beyond their initial configuration, the second life of these units is crucial, given their temporary nature. Despite growing interest in second-life strategies, many TH units and associated infrastructure remain unused or inefficiently managed after their initial deployment. Second-life strategies are presented for post-disaster and post-conflict (PDPC) TH units and settlements in Türkiye, using two case studies—the 2011 Van earthquakes and Syrians under temporary protection—and expert insights from the Disaster and Emergency Management Presidency of Türkiye (Afet ve Acil Durum Yönetimi Başkanlığı—AFAD). A three-step methodology was employed, including a literature review, semi-structured expert interviews and hybrid deductive–inductive thematic analysis. Findings reveal that second-life outcomes are largely shaped by policy gaps, operational conditions, tenure constraints, institutional decisions and user practices rather than by design-based circular approaches. The proposed framework provides practical guidance for policymakers and practitioners in Türkiye and other crisis-prone contexts to improve resource efficiency and integrate second-life planning into preparedness and recovery processes. POLICY RELEVANCE Policy gaps are a major barrier to implementing second-life strategies for TH and settlements in Türkiye. Strategies are proposed to address them. Housing, land and property rights—the rights to obtain and reside in housing that ensures safety, security and dignity—should form the foundation of any second-life policy, as tenure determines whether entire settlements or only individual units can be repurposed. A national take-back policy with clear guidelines and structured repair and decommissioning processes is essential to maintain unit quality and avoid the redeployment of unusable units. Neighbourhood dynamics and social considerations may also affect the feasibility of converting temporary settlements into permanent ones. The long-term concentration of affected populations in a single location may hinder social and psychological recovery. Integrating second-life planning into the preparedness phase can enhance the efficiency and sustainability of sheltering responses, optimise resource allocation, reduce waste, and mitigate environmental impacts.

Shelters are safe facilities that protect a population from possible damaging effects of a disaster. For that reason, shelter location and traffic assignment decisions should be considered simultaneously for an efficient evacuation plan. In addition, as it is very difficult to anticipate the exact place, time, and scale of a disaster, one needs to take into account the uncertainty in evacuation demand, the disruption/degradation of evacuation road network structure, and the disruption in shelters. In this study, we propose an exact algorithm based on Benders decomposition to solve a scenario-based two-stage stochastic evacuation planning model that optimally locates shelters and that assigns evacuees to shelters and routes in an efficient and fair way to minimize the expected total evacuation time. The second stage of the model is a second-order cone programming problem, and we use duality results for second-order cone programming in a Benders decomposition setting. We solve practical-size problems with up to 1,000 scenarios in moderate CPU times. We investigate methods such as employing a multicut strategy, deriving Pareto-optimal cuts, and using a preemptive priority multiobjective program to enhance the proposed algorithm. We also use a cutting plane algorithm to solve the dual subproblem since it contains a constraint for each possible path. Computational results confirm the efficiency of our algorithms.

Disasters can produce a substantial amount of waste that can threaten the capacity of waste management systems. This paper presents a methodology for estimating waste accumulation caused by disasters considering the uncertainty of the timing and scale of disasters that can be used to estimate the return period and the reliability of the disaster waste management system. To estimate the reliability of the system, the first-order second-moment reliability assessment method, in which the reliability index (β) is used to judge the reliability of a system, is applied in this paper. In addition, two case studies illustrate how the methods can be applied to the real world. The reliability index curve of the system developed from sensitivity analysis can provide information for decision-makers in terms of disaster waste clean-up arrangements. The approach developed can be used to analyze the effects of different parameters involved in the waste clean-up system after disasters.

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network.

High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction.