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The factors that explain the speed of recovery after disaster remain contested. While many have argued that physical infrastructure, social capital, and disaster damage influence the arc of recovery, empirical studies that test these various factors within a unified modeling framework are few. We conducted a mail survey to collect data on household recovery in four small towns in southern Indiana that were hit by deadly tornadoes in March 2012. The recovery effort is ongoing; while many of the homes, businesses, and community facilities were rebuilt in 2013, some are still under construction. We investigate how households in these communities are recovering from damage that they experienced and the role of social capital, personal networks, and assistance from emergency responders on the overall recovery experience. We used an ordered probit modeling framework to test the combined as well as relative effects of (a) damage to physical infrastructures (houses, vehicles, etc.); (b) recovery assistance from emergency responders (FEMA) as well as friends and neighbors; (c) personal network characteristics (size, network density, proximity, length of relationship); (d) social capital (civic engagement, contact with neighbors, trust); and (e) household characteristics. Results show that while households with higher levels of damage experienced slower recovery, those with recovery assistance from neighbors, stronger personal networks, and higher levels of social capital experienced faster recovery. The insights gained in this study will enable emergency managers and disaster response personnel to implement targeted strategies in facilitating post-disaster recovery and community resilience.

Post-disaster recovery has been addressed in the literature by different sectoral perspectives and scientific communities. Nevertheless, studies providing holistic approaches to recovery, integrating reconstruction procedures and socio-economic impacts, are still lacking. Additionally, there is a gap in disaster recovery research addressing the additional challenges posed by the effect of complex, multiple, and interacting risks on highly interconnected urban areas. Furthermore, recovery has only been marginally explored from a pre-disaster perspective in terms of planning and actions to increase urban resilience and recoverability. This paper provides a critical review of existing literature and guidelines on multi-risk disaster recovery with the twofold aim of identifying current gaps and providing the layout to address multi-risk recovery planning tools for decision-making. The literature on disaster recovery is investigated in the paper by focusing on the definition of the recovery phase and its separation or overlapping with other disaster risk management phases, the different destinations and goals that an urban system follows through recovery pathways, the requirements to implement a holistic resilience-based recovery roadmap, the challenges for shifting from single-risk to multi-risk recovery approaches, and the available tools for optimal decision-making in the recovery planning. Finally, the current challenges in multi-risk recovery planning are summarized and discussed. This review can be a ground basis for new research directions in the field of multi-risk recovery planning to help stakeholders in decision-making and optimize their pre-disaster investments to improve the urban system's recoverability.

Extreme weather, such as rainstorms, often triggers faults in the distribution network, and power outages occur. Some serious faults cannot be repaired by one team alone and may require equipment replacement or engineering construction crews to work together. Rainstorms can also lead to road damage or severe waterlogging, making some road sections impassable. Based on this, this paper first establishes a road network model to describe the dynamic changes in access performance and road damage. It provides the shortest time-consuming route suggestions for the traffic access of mobile class resources in the post-disaster recovery task of power distribution networks. Then, the model proposes a joint repair model with general repair crew (GRC) and senior repair crew (SRC) collaboration. Different types of faults match different functions of repair crews (RCs). Finally, the proposed scheme is simulated and analyzed in a road network and power grid extreme post-disaster recovery model, including a mobile energy storage system (MESS) and distributed power sources. The simulation finds that considering road damage and severe failures produces a significant difference in the progress and load loss of the recovery task. The model proposed in this paper is more suitable for the actual scenario requirements, and the simulation results and loss assessment obtained are more accurate and informative.

Over half the world’s population lives in urban regions, and increasingly disasters are of great concern to city dwellers, policymakers, and builders. However, disaster risk is also of great interest to corporations, financiers, and investors. Risky Cities is a critical examination of global urban development, capitalism, and its relationship with environmental hazards. It is about how cities live and profit from the threat of sinkholes, garbage, and fire. Risky Cities is not simply about post-catastrophe profiteering. This book focuses on the way in which disaster capitalism has figured out ways to commodify environmental bads and manage risks. Notably, capitalist city-building results in the physical transformation of nature. This necessitates risk management strategies –such as insurance, environmental assessments, and technocratic mitigation plans. As such capitalists redistribute risk relying on short-term fixes to disaster risk rather than address long-term vulnerabilities. 

This study reviews the role of Child-Friendly Green Spaces (CFGS) in supporting children’s psychological, physical, and educational recovery following natural disasters. The main research question guiding this review is the following: how do CFGS contribute to holistic child well-being and resilience in disaster-affected contexts, and what barriers and strategies influence their effective integration into recovery frameworks? Employing a rigorous literature review methodology, we synthesized interdisciplinary evidence from environmental psychology, urban planning, public health, and education, encompassing studies published between 2000 and 2024. Findings demonstrate that CFGS significantly reduce trauma-related symptoms such as anxiety, depression, and post-traumatic stress, promotes physical health through active play, and foster educational engagement by improving concentration, attendance, and informal learning opportunities. Furthermore, CFGS contribute directly to multiple Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). Despite these advantages, CFGS are often overlooked in formal disaster recovery planning due to prioritization of immediate relief, financial and logistical challenges, and socio-cultural factors. To address these challenges, this study proposes a participatory, culturally sensitive framework for CFGS implementation, which integrates inclusive design, multi-sector collaboration, and ongoing monitoring and evaluation. Grounded in theoretical perspectives such as the Biophilia Hypothesis, Bronfenbrenner’s Ecological Systems Theory, and restorative environments, CFGS are reframed as critical infrastructures for children’s holistic recovery and resilience. The findings underscore the urgent need to embed CFGS within disaster recovery and urban planning policies to promote child-centered, sustainable community development.

Post-disaster recovery (PDR) is a complex, long-lasting, resource intensive, and poorly understood process. PDR goes beyond physical reconstruction (physical recovery) and includes relevant processes such as economic and social (functional recovery) processes. Knowing the size and location of the places that positively or negatively recovered is important to effectively support policymakers to help readjust planning and resource allocation to rebuild better. Disasters and the subsequent recovery are mainly expressed through unique land cover and land use changes (LCLUCs). Although LCLUCs have been widely studied in remote sensing, their value for recovery assessment has not yet been explored, which is the focus of this paper. An RS-based methodology was created for PDR assessment based on multi-temporal, very high-resolution satellite images. Different trajectories of change were analyzed and evaluated, i.e., transition patterns (TPs) that signal positive or negative recovery. Experimental analysis was carried out on three WorldView-2 images acquired over Tacloban city, Philippines, which was heavily affected by Typhoon Haiyan in 2013. Support vector machine, a robust machine learning algorithm, was employed with texture features extracted from the grey level co-occurrence matrix and local binary patterns. Although classification results for the images before and four years after the typhoon show high accuracy, substantial uncertainties mark the results for the immediate post-event image. All land cover (LC) and land use (LU) classified maps were stacked, and only changes related to TPs were extracted. The final products are LC and LU recovery maps that quantify the PDR process at the pixel level. It was found that physical and functional recovery can be mainly explained through the LCLUC information. In addition, LC and LU-based recovery maps support a general and a detailed recovery understanding, respectively. It is therefore suggested to use the LC and LU-based recovery maps to monitor and support the short and the long-term recovery, respectively.

Measuring disaster resilience from the perspective of long-term recovery ability is important for the planning and construction of urban sustainability, whereas short-term resilient recovery can better reflect a city’s ability to recover quickly after a disaster occurs. This study proposes an analytical framework for urban disaster recovery and resilience based on social media data that can analyze short-term disaster recovery and assess disaster resilience from the perspectives of infrastructure and people’s psychological states. We consider the downpour in Henan, China, in July 2021. The results show that (1) social media data can effectively reflect short-term disaster recovery, (2) disaster resilience can be assessed using social media data combined with rainfall and damage data, and (3) the framework can quantitatively reflect the differences in disaster recovery and resilience across regions. The findings can facilitate better decision-making in disaster emergency management for precise and effective post-disaster reconstruction and psychological intervention, and provide references for cities to improve disaster resilience.

Optimizing the allocation schemes of post-disaster recovery resources can promote the sustainable development of a regional economy. However, previous studies determined the inputs and allocation schemes of recovery resources based on direct economic (DE) loss while neglecting indirect economic (IDE) loss, which restricted economic recovery. This study considered DE and IDE loss, and used a dynamic disastrous computable general equilibrium (CGE) model to simulate multiple scenarios with different inputs and allocation schemes to identify a better economic recovery strategy. Taking Super Typhoon Mangkhut’s landing in Guangdong Province in 2018 as an example, the results showed that the IDE loss had a long-term impact and dynamic accumulation without post-disaster recovery, reaching 15.25 times the DE loss by 2022. In the baseline scenario, the recovery resource inputs, including relief funds, reconstruction funds, and natural disaster commercial insurance, were limited, leading to a cumulative loss recovery rate of less than 2% in 2018–2022. According to our findings, recovery resources needed a 15-fold increase to recover to pre-disaster levels. Considering the impacts of sector connections on IDE loss, six allocation schemes were established based on DE loss, IDE loss, and industrial structure. Compared with the typical allocation scheme based on DE loss, allocating recovery resources according to the diffusion coefficient substantially improved the loss recovery rate and recovery resource utilization efficiency. The dynamic disastrous CGE model conducted multi-scenario simulations to identify the optimal recovery resource allocation scheme that supported rapid and efficient post-disaster economic recovery.

The assessment of post-disaster recovery is often hindered by limited metric and longitudinal data, in addition to the dynamic and long-term processes. Therefore, this study aimed to investigate the early stages after the 2004 Indian Ocean tsunami in Aceh, Indonesia, using the Disaster Recovery Index (DRI). The two initial waves of Study of Tsunami and Aftermath Recovery (STAR) data were used to track the recovery process from 5 to 19 months after the tsunami. The results showed various recovery patterns in three affected areas and five sectors. Furthermore, recovery rates in the medium and heavily damaged areas increased by 2.05 and 7.45 percentage points, respectively, with a 0.33 percentage point decrease in the lightly damaged areas. The social and livelihood sectors showed rapid progress, supported by the establishment of temporary health and education facilities, including Cash-for-Work programs. Meanwhile, other sectors experienced slower recovery due to their complexity. The application of the DRI successfully showed the relative positions across affected areas and sectors over time in a simple way. This confirmed the variety of recoveries in subgroups in the community and suggested the importance of regularly measuring progress using standard metrics to observe long-term conditions.

The evaluation of user satisfaction in temporary housing units, particularly in the container city established in Diyarbakir following the Kahramnmaras earthquake on 6 February 2023, presents a critical area of study in post-disaster recovery efforts. The aftermath of natural disasters often necessitates the rapid deployment of temporary housing solutions such as container cities. However, there is a significant gap in the comprehensive and timely evaluation of user satisfaction in these newly established settlement, particularly in the context of the recent Kahramanmaras earthquake in Turkey. This research aims to address this knowledge gap, by assessing user satisfaction levels across various aspects of the container city in Diyarbakir, including social amenities, technical infrastructure, housing units, and sociocultural relations. By examining these factors, the study seeks to provide valuable insights for disaster management authorities, urban planners, and policymakers involved in post-disaster recovery. The study seeks to identify specific areas of high and low satisfaction among residents, analyse the relationship between physical design elements and user satisfaction and explore the sociocultural factors influencing residents’ experiences. The findings of this research are expected to contribute to the improvement of future temporary housing solutions, emphasising the importance of considering both physical and sociocultural aspects in disaster recovery planning. Through this comprehensive evaluation, the study aims to enhance the overall effectiveness and user experience of temporary housing initiatives in post-disaster scenarios.