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Bicycles and other forms of micromobility have been anecdotally used in past disasters to help save lives and improve community recovery. However, research and practice are scarce on this resilient transportation strategy, which limits its usefulness and possible benefits. To fill this gap, our paper investigates the potential role bicycles and micromobility in facilitating (or limiting) disaster response and recovery. Given the lack of exploration on the topic, we convened an online workshop where we conducted brainstorming and focus group discussions with disaster experts from various government agencies, not-for-profit organizations, academia, and policy groups. We present a synthesis of that discussion, along with a review of the existing literature. We conclude there is strong potential for bicycles and micromobility for different disaster phases, hazard types, and groups of people. However, multiple barriers exist related to implementation and safety, suggesting a need for future research and policy in the transportation and emergency management fields and practices. • Feasibility of micromobility to aid evacuation is determined by disaster type, disaster phase, vehicle type, and geography. • Micromobility has the potential to aid both the short-term response and longer-term recovery from natural disasters. • Micromobility is flexible and cheap for resource movement and can navigate roads that are impassable for cars and trucks. • Availability of bikes and micro-modes is both a barrier and a facilitator for use during disaster response. • Many political and coordination barriers remain to make micromobility a part of disaster planning. [ABSTRACT FROM AUTHOR]

Disaster Recovery (DR) providers define the main services as backup and restore, both with critical data and applications. Most clients of data and application backup, fully rely on DR's services to restore them in the disaster events. Furthermore, in the normal conditions, DR provider backs up data, sometimes with application in routine or regular basis. Thus, providers should deliver the services in sophisticated way through several Key Performance Indicators (KPIs). In this paper, development of KPIs is focused on performance and assurance, which a laboratory testing simulates backup and restore services. On the other hand, experimental data such as network and system parameters are analysed to build performance and assurance framework. Meanwhile, interdependence among each performance and assurance of DRCs was used to build a relationship model. It is acted as a part of service level agreement, that a DR providers can present services dynamically. They also should adapt the computing resources to deliver services to meet SLA (Service Level Agreement) in flexible strategy. In turn, they can negotiate SLA with the customers based on this new approach. [ABSTRACT FROM AUTHOR]

There is a gap between research in disaster resilience and the implementation of resilience via political strategies. This paper uses three frameworks from resilience research to analyse the 2022 German Resilience Strategy as a case study. It answers two questions: Does the German Resilience Strategy reflect resilience understandings from research and what are its strengths and shortcomings? Are such resilience frameworks useful for analysing political documents and what are their strengths and shortcomings? The resilience frameworks are Bruneau et al.'s 4Rs of resilience, Hollnagel's four cornerstones of resilience, and Thoma et al.'s resilience cycle. The paper uses qualitative content analysis to interpret the 345 recommendations for measures of the Strategy. The results show that the Strategy lives up to a holistic definition of resilience. It implicitly also embraces a complexity-informed understanding of resilience, but risks losing sight on adaptive capacity due to its broadness. The Strategy focuses on natural hazards, although it officially follows the all-hazard approach. There is a lack of recommendations on individual and societal disaster recovery. The three resilience frameworks are applicable for analysing political strategies. The 4Rs of resilience framework has a blind spot with respect to unknown disruptions. The four cornerstones of resilience framework bases on an innovative resilience conception and could be useful for disaster studies if it was more thoroughly translated to the latter's specifics. The resilience cycle framework is limited due to its cyclical approach, and it entails prevention, which is not part of resilience. • The German Resilience Strategy has a holistic understanding of resilience. • The German Resilience Strategy lacks recommendations on disaster recovery. • The 4Rs of resilience framework has a blind spot regarding unknown disruptions. • We should translate findings from resilience engineering to disaster resilience. • Prevention is not part of resilience and thus not in the German Resilience Strategy. [ABSTRACT FROM AUTHOR]

• The model of the cyber-physical-transportation network is established. • A two-stage model is applied to enhance the resilience for power system. • The accurate model of restoration time of faulty components is presented. • The load recovery model of cyber-physical is established. In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in this paper to enhance the resilience under extreme scenarios. In the first stage, based on the principle of hierarchical portioning, the responsible area for the emergency repair station is divided by the community theory, and the emergency repair station planning model is developed. In the second stage, the time sequence of repairing the faulty components is first established based on the graph theory. Then, based on the spatial distribution of adjacent time-series faulty components on the transportation network, a faulty component restoration time model is established to accurately reflect the faulty restoration time. On this basis, the functional coupling effects of economic dispatch control and substation automation system failures on load recovery are analyzed to establish a load recovery model. The case studies of the IEEE RTS-79 test system demonstrate that, compared with traditional independent recovery model, the model proposed in this paper performs better in enhancing resilience under extreme scenarios. [ABSTRACT FROM AUTHOR]

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings. • Paper introduces hybrid GRU-CNN model for disaster prediction. • Highlights resilience, reducing false alarms for accurate predictions. • Emphasizes community involvement, fostering a culture of preparedness. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results. [ABSTRACT FROM AUTHOR]

• Meso level vulnerability assessment for extreme heat. • Contextual vulnerability framework for heritage city. • Socio-cultural indicators like social capital, cultural value. • Support for decision making in urban disaster resilience planning. In developing cities, rapid urbanization and climate change leads to drought and urban heat island effects which affects the energy consumption, economy and the environment. Extreme temperature rise has amplified the stress on vulnerable groups and has reduced livability. The sustainability of a city lies in the adaptability of the city to the climatic stresses. The role of adaptability is guided by the contextual vulnerability assessment of the city. This paper intends to assess the contextual vulnerability of Madurai, an ancient city with high social and cultural value. Composite heat vulnerability index (CHVI), a function of exposure, sensitivity and adaptive capacity of the context has been construed by many as the quantitative representation of the vulnerability of a place to high temperature. This paper focuses on the development of a framework to assess the CHVI considering the contextual socio-cultural indicators like social capital and cultural value along with components of meteorological, geophysical, biophysical, fragility, socially-marginalised, and development quotient.The research methodology identifies suitable context specific indicators, computes and maps exposure, sensitivity and adaptive capacity indices, spatially maps the categorised vulnerable zones and identifies the critical indicators that contribute to vulnerability. The framework can support decision-making to enable socially resilient cities. [ABSTRACT FROM AUTHOR]

• Investment portfolio and planning strategy under limited budget for resilience enhancement to realize adaption and mitigation to the risk of extreme disasters under climate change are discussed. • Insurance incorporated as a supplementary strategy to enhance resilience is co-optimized with security investment from the perspective of distribution system operators in the planning stage. • Both emergency power support under extreme disaster scenarios and efficient renewable energy consumption under normal operation scenarios of energy storage are considered. • Entire normal-failure-recovery-loss process to address climate change is considered and modeled as a stochastic mixed integer linear optimization problem based on scenarios. Severe damage and high economic losses caused by frequent extreme disasters are becoming unbearable for distribution system operators (DSOs) under climate change. In order to cope with extreme disasters, this paper proposes an investment portfolio and planning framework of multi-resource for resilience enhancement. In the investment portfolio and planning framework, insurance is incorporated to mitigate high economic losses as a risk sharing and resilience enhancement supplementary strategy, and co-optimized with security investment from the perspective of DSOs. Firstly, the insurance investment is modelled based on Gordon-Loeb model according to insurance industry principles and formulated as constraints by piecewise linearization techniques. Secondly, the investment portfolio and planning framework proposed in this paper is modeled as a scenario-based two-stage stochastic mixed integer linear optimization program (MILP). In the first stage, DSO makes investment and planning decisions including energy storage (ESS) allocation, line hardening strategy and insurance investment. Both operation strategies of normal operation scenarios and extreme disaster scenarios under climate change are optimized in the second stage. Finally, progressive hedging (PH) algorithm is applied to solve the two-stage stochastic MILP model. The proposed model and algorithm are tested on the modified IEEE 33-bus test system. The results verify the validity of investment portfolio and planning framework, and the results show that portfolio investment considering insurance can realize resilience enhancement in both load shedding and economic losses. [ABSTRACT FROM AUTHOR]

Fault diagnosis and location play a pivotal role in expediting fault restoration and enhancing power system resilience. However, integrating distributed generation and diverse load profiles has led to more complicated distribution networks, intensifying fault diagnosis and location challenges. Hence, there is a need to transform traditional distribution networks into resilient active ones that actively guide and utilize distributed energy resources, possessing active control and operational capabilities. This paper comprehensively reviews fault diagnosis and location techniques in distribution networks, focusing on faulted line selection, fault section estimation, and accurate fault location. It provides an in-depth analysis of current fault diagnosis and location techniques, along with an assessment of their challenges, summarizing and evaluating the current research status at both local and international levels. To address existing fault diagnosis and location technologies limitations, a method utilizing micro-phasor measurement units (μPMUs) to enhance situational awareness and state estimation of active distribution networks is proposed. The paper also provides a comprehensive analysis of the optimized configuration strategy for achieving network observability and fault diagnosis using limited μPMU, in addition to comparing and analyzing the advantages and disadvantages of typical solution algorithms for the optimal PMU placement (OPP) problem. In response to extreme disturbance scenarios in active distribution networks, resilience enhancement strategies are proposed from three perspectives: fault prevention, fault response, and fault restoration. Finally, a summary and outlook on current research methods for fault diagnosis and location, as well as the challenges and future research directions in resilience enhancement strategies, are provided. [Display omitted] • Review fault diagnosis, section estimation, and accurate fault location methods. • Improving power system resilience through fault diagnosis and location. • Strategies using limited μPMUs to enhance distribution network situation awareness. • Comprehensive methods for power system resilience in extreme disasters. [ABSTRACT FROM AUTHOR]

This paper explores the utilisation of knowledge graphs and an agent-based implementation to enhance urban resilience and accessibility in city planning. We expand The World Avatar (TWA) dynamic knowledge graph to support decision-making in disaster response and urban planning. By employing a set of connected agents and integrating diverse data sources — including flood data, geospatial building information, land plots, and open-source data — through sets of ontologies, we demonstrate disaster response in a coastal town in the UK and various aspects relevant to city planning for a mid-sized town in Germany using TWA. In King's Lynn, our agent-based approach facilitates holistic disaster response by calculating optimal routes, avoiding flooded segments dynamically, assessing infrastructure accessibility before and during a flood using isochrones, identifying inaccessible population areas, guiding infrastructure restoration, and conducting critical path analysis. In Pirmasens, for city planning purposes, the knowledge graph-driven isochrone generation provides evidence-based insights into current amenity coverage and enables scenario planning for future amenities while adhering to land regulations. The implementation of agents and knowledge graphs achieves interoperability and enhances urban resilience and accessibility by enabling cross-domain correlation analysis that extends various areas including geospatial buildings, population demographics, accessibility coverage, and land use regulations. • Development of versatile tool for disaster and city planning. • Disaster response and planning system through semantic agents interplay. • Holistic 15-minute city planning by status quo monitoring and scenario planning. • Cross-domain analyses in planning via the integration of complementary data sources. [ABSTRACT FROM AUTHOR]

• A systematic review of multiplex network in resilience of interdependent infrastructures. • Three types of interdependencies including proximity, function, and logic. • Research gaps in multi-hazard attacks, high-resolution performance indicators, percolation mechanisms. • Lack systematic evaluation of nature-based and engineering solutions towards infrastructure resilience. • Call for high-quality observation data and informed multi-faced uncertainties. Modeling the effect of extreme events on multiple interdependent critical infrastructure systems (CIS) simultaneously is essential for improving their resilience. To uncover CIS resilience, an increasing number of studies are attempting to incorporate the interdependency in CIS resilience modeling under disaster events, where multiplex network is a powerful and promising representative. This paper presents a systematic review of multiplex networks in CIS resilience modeling and discusses the state of the art on the topic. This review helps to guide the construction of multiplex networks in CIS with different types of interdependencies and highlights the key aspects of CIS resilience modeling including attacks, evaluation, evolution, and control. Moreover, several challenges are identified while providing possible future research directions. The findings and discussions of this study could be helpful to researchers and professionals in the field of CIS resilience modeling especially when leveraging multiplex networks. [ABSTRACT FROM AUTHOR]

The economic repercussions of natural hazards extend beyond immediate damages, impacting more than the directly affected regions through trading ties and interrupted service provisions. The Adaptive Regional Input–Output (ARIO) model has been developed to assess these indirect impacts by considering changes in productive capacity and demand-driven output adjustments. We extend a recently refined ARIO model (R-ARIO) to conduct a multi-regional multi-sector analysis. This extension accounts for the geographical disparities in direct damages and inter-regional economic interactions. To illustrate the effectiveness of this approach, we present a case study of the 2011 Tohoku earthquake and assess its economic impacts across 13 prefectures in the East-Japan region. The paper further compares the outcomes of single- and multi-regional analyses to showcase the effect of inter-regional trading ties on economic resilience. Additionally, trade-offs involved in selecting the study area boundary for the multi-regional analysis are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a Capabilities-based Interface for Socio-Technical Resilience (CI-STR) framework to model community disaster resilience, a process shaped by the complex interplay between social and technical factors. Central to this framework is the concept of human capabilities, defined as the opportunities for individuals to achieve valuable functionings. These capabilities serve as the interface that integrates individuals' social characteristics with community resources provided by infrastructure systems and businesses, both of which can undergo dramatic and rapid changes after a disaster, influencing the fulfillment of capabilities. The CI-STR framework highlights the feedback relationships among individual capabilities, where one capability can affect others through its impact on social and/or technical contexts throughout the post-disaster recovery process. In the social context, changes in capabilities directly reflect on individual's physical, financial, and social conditions, while technical feedback effects can alter the demand, supply, and operation of technical systems. The framework conceptualizes the recovery process as a series of activities driven by human capabilities and introduces three key features to model this dynamic process: capabilities-induced coping strategies, time-dependent and hierarchical capabilities, and capabilities in groups. These features capture the motivations and abilities for recovery activities, prioritize actions based on urgent needs, and assess group-level capabilities, providing unique insights into the temporal evolution of individuals' capabilities during recovery. The CI-STR framework enables comprehensive modeling of the human components and their interactions with technical and social elements, facilitating the assessment of community resilience and the development of recovery strategies across a wide range of community dimensions. • Modeling of dynamic socio-technical interactions in disaster resilience. • CI-STR integrates social and technical elements to fulfil human capabilities. • Capabilities contribute back to social and technical contexts via feedback loops. • CI-STR features the time evolution of capabilities during recovery. • A schematic procedure for implementing CI-STR is proposed. [ABSTRACT FROM AUTHOR]

Extreme weather, especially typhoons, frequently impacts port operations, causing substantial economic losses. Thus, it has become vital to quantitatively evaluate the resilience of ports to typhoon disasters. This paper offers a comprehensive examination of the current evaluation methods used in assessing the resilience of ports and develops a quantitative port typhoon resilience assessment model based on the 4R (i.e., Reliability, Redundancy, Robustness, and Recoverability) framework. Ports' overall resilience to typhoon disasters can be assessed by integrating the analytic hierarchy process method, fuzzy logic, and the evidence reasoning approach. Four representative coastal ports in China were selected as case studies to examine the suitability and efficacy of the suggested model. This study provides innovative theoretical insights and empirical evidence to advance port resilience research, with a valuable reference for port administrators and policymakers, equipping them to address forthcoming challenges of climate change efficiently. [ABSTRACT FROM AUTHOR]

• Disconnected state-civil society knowledge flows and disconnected socio-ecological systems inhibit efforts to build socio-hydrological resilience to water risks. • Cape Town's marginalized urban spaces, while physically located at the periphery, are in fact central to the city's urban social-hydrological systems. • Ignoring marginalized urban spaces does not only disproportionally affect Cape Town's impoverished communities, but also effectively undermines the socio-hydrological resilience of Cape Town as a whole. In 2017 and 2018, Cape Town faced historically unprecedented water shortages. With the imminent possibility of running out of water, the city's leadership prioritized reducing water demand and expanding new water sources, while also reinvigorating the goal of seeking to build system-level water resilience for the longer term. Beyond the context of Cape Town, the crisis captured global attention, highlighting ongoing and future water security challenges, the realities of climate change, and the critical need to foster transitions towards more resilient water futures. Given that much of the discourse and implementation around water resilience remains squarely focused on the biophysical and engineering aspects of water supply and distribution systems, despite repeated calls for the need for greater attention to issues of equity and power, there remains little understanding of the ways that persistent inequities might serve or inhibit possibilities for urban socio-hydrological (or water) resilience. This paper draws on examples from Cape Town to argue that patterns and legacies of inequality, marginalization, and exclusion erode and inhibit possibilities for water resilience. Providing needed empirical evidence on the nature of these linkages, we theorize that deeply rooted inequities and related dynamics act as "counter-currents"—trends that undermine and present persistent challenges to efforts to enhance socio-hydrological resilience. Documenting examples of disconnections between the state and civil society as well as disconnected socio-ecological systems, we argue that these persistent inequities mean that efforts to achieve socio-hydrological resilience are likely to remain elusive. It is only by foregrounding these processes that it will be possible to make cities more resilient in the face of ongoing and future water-related risks, uncertainties, and climatic and environmental change. [ABSTRACT FROM AUTHOR]

Türkiye is known for its intricate tectonic structure and high seismic activity, which makes in-depth research on earthquakes necessary. This research paper presents a multifaceted examination of earthquake impact in Türkiye, encompassing critical elements of seismic vulnerability with a special focus on the latest twin earthquake of February 6, 2023. Firstly, a three-dimensional finite element (FE) analysis has been conducted to explore the buried pipeline behavior under left-lateral strike-slip faults. Secondly, making use of the earthquake data from 1976 to 2023, a new Newmark displacement-based model has been proposed which predicts co-seismic landslide displacement. Finally, new magnitude conversion relations are developed in the generalized moment magnitude scale (M wg) for Türkiye using three statistical regression techniques. The maximum tensile strain was found to be around 2.0% based on the findings of the 3D numerical study of an underground pipe that was subjected to left-lateral strike-slip fault movement. The proposed co-seismic landslide displacement model was developed based on moment magnitude, yield acceleration, and peak ground acceleration, which yields an R-squared value of 0.93. Outcomes of the magnitude conversion study depicted that the improved general orthogonal regression (IGOR) relationships developed in this study performed better than the other conventional methods of regression. Also, the proposed regional relations based on M wg scale perform better than global relations as they provide closer values to the observed ones. By offering a comprehensive perspective on seismic impact, the research lays the groundwork for upgraded predictive methods and refined seismic design considerations. The proposed models and conversion relations contribute to the strengthening of critical infrastructures, providing a forward-looking approach to enhance preparedness and resilience against earthquake disasters in Türkiye. • Examined the response of buried pipelines under left-lateral strike-slip fault. • Proposed a new empirical model for co-seismic landslide displacement for Türkiye. • Developed magnitude conversion relations in the M wg scale for Türkiye. [ABSTRACT FROM AUTHOR]

• Construct a collaborative recovery framework involving four parts: CPS dependency model, recovery model, recovery strategies, and post-recovery assessment. • In the collaborative recovery model, factors such as repair crew, repair time, and network reconstruction are considered, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. • Develop multiple recovery strategies tailored to the structure and functional characteristics of CPS components, and evaluate the impact of each strategy on the recovery process. • Analyze the recovery effectiveness of different recovery times for cyber and physical components and different recovery strategies under various IEEE systems. • The simulation results indicate that moderately reducing repair times for cyber nodes and smaller network sizes contribute to improving repair efficiency. Improving the resilience of Cyber-Physical Systems (CPS) against extreme events is vital. Current research, overly focused on power system restoration, often misses the complex interplay between the physical and cyber aspects of these systems, leading to potential vulnerabilities. This paper first proposes a framework for CPS considering interdependence. The framework accounts for cyber-physical collaborative recovery and the connectivity between cyber nodes and control center in the cyber system. The model is built considering factors such as repair crew, repair time, and network reconstruction, aimed at rapidly restoring the structural and functional resilience of CPS in the shortest possible time. Next, it integrates complex network theory to propose different repair strategies based on structural and functional indicators. Finally, the repair efficiency of the proposed strategies is comprehensively evaluated using structural and functional metrics. Simulation results show that the repair efficiency of CPS is superior to traditional power grids, and moderate reduction in the repair time of cyber nodes and a decrease in network size both contribute to enhancing repair efficiency. [ABSTRACT FROM AUTHOR]

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society. • Quantifying community resilience is an open research challenge • This paper develops a novel framework to quantify resilience after a disaster using network analysis • Using human mobility data associated with two wildfires, we measure robustness and vulnerability of communities • Results show community resilience closely tied to socio-economic and built environmental traits of the affected areas • Our approach paves a way to study disasters and their long-term impacts on society [ABSTRACT FROM AUTHOR]

With the rapid development of transportation systems and increased connectivity between cities, the capacity of regional cities to propagate risks and cooperate with each other has a great impact on regional resilience. Given that a region can be seen as a networked and multidimensional system with complex interactions of cities, it is necessary to assess regional resilience based on the complex network thinking. This paper views the region as a two-layer coupled city network with risk propagation and cooperative recovery and propose the regional resilience assessment framework. Then, a network of 337 prefecture-level cities in mainland China is used as the study area to assess the impact of different cities on the regional resilience after a sudden public health disaster. The results show that cities in western China have less impact on regional resilience, while cities in central and eastern China have more impact. In addition, we found that each indicator under the three dimensions of regional network resilience has different degrees of correlation with influencing factors. Meanwhile, the changes in the same influencing factor may cause indicators under different dimensions of resilience to increase or decrease. There is a trade-off between approaches to improve regional resilience under each dimension. • Portrays the city network under risk propagation and cooperative recovery with a two-layer coupled network model • Proposes a quantitative regional resilience assessment framework in the dynamic perspective • Assesses the impact of different Chinese cities on regional resilience after a sudden public health disaster • Explores the impact of different elements of the regional city network on its resilience [ABSTRACT FROM AUTHOR]

Frequent occurrence of natural disasters has become one of the most serious challenges to human society. As rural communities are the forefront of disasters, enhancing the resilience of rural community is crucial to mitigate the impact of natural disasters and seek rapid recovery. This paper provides an analytical framework for changing trajectories of the resilience of rural communities after disasters. Taking the disaster area affected by the catastrophic debris flow in China as an example, we analyze the changing trajectories of rural community resilience after disaster in three villages with different damage levels from 2010 to 2020 based on the data from key person interviews, the disaster situation and development reports, village work records and farmers' questionnaires. We found that the changes of community resilience after disaster show obvious stages due to the difference in the intensity of effects of government intervention and community self-organization ability during disaster relief reconstruction period and self-recovery period. The resilience of communities with different disaster losses showed an increasing trend. However, during the disaster relief and reconstruction period, the resilience of the severely affected community was improved faster, followed by the most severely affected community, while the resilience of generally affected community was improved more slowly. During the self-recovery period, the resilience of the affected general community was improved faster than that of the other two communities. The evolution process of community resilience reflects the increasing role of self-organizational capacity in post-disaster community development, aligning with the overall global trend in rural community governance. In addition, there is a significant sequence in the improvement speed of different dimensions of community resilience at different stages after disasters. During the disaster relief and reconstruction period, the resilience of community management was improved faster and the resilience of environment was slower. During the self-recovery period, the economic resilience of community was improved faster and the social resilience was improved slower. Finally, some suggestions were put forward to strengthen natural disasters prevention and community resilience in disaster-prone areas. [Display omitted] • Provide an analytical framework for the changing trajectories of the resilience of rural communities after disaster. • Community self-organization ability play an important role in improving community resilience. • There are significant differences in resilience of communities with different disaster losses. • There is an obvious sequence in the improvement speed of different dimensions of community resilience after disaster. [ABSTRACT FROM AUTHOR]

• Investigated impacts of flows among transport, population and facilities on resilience. • Measuring resilience by service availability provides spatially comparable results. • Direction and volume of flow changes create spatial disparity in urban resilience. • Over-strong connected networks with unbalanced service weaken resilience. • Polycentricity, hierarchical networks, and module collaboration enhance resilience. Failures of roads and critical facilities under disasters can result in public service disruptions. Studies quantifying disaster resilience commonly focus on evaluating physical performance of infrastructure systems, often cannot capture their capacity that maintain available service to meet citizens' demands. This paper proposes a flow-based framework for quantitively measuring resilience by regarding changes in service availability to critical facilities as a primary measurement outcome. Through the utilization of multilayer networks, we effectively incorporate diverse datasets pertaining to population demographics, residential areas, critical facilities, and the roadway network. This integration enables us to simulate the intricate dynamics of public service flows within dynamic urban systems, thereby evaluating resilience levels and accurately mapping the spatial distribution patterns. The methodology was validated in Shanghai by quantifying regional variations in healthcare services under pluvial flood scenarios. The research findings highlight the heterogeneous redistribution of service flows during extreme events, leading to spatial variations in resilience outcomes, irrespective of the proximity to hazard-prone areas. The spatial clustering of vulnerable communities demonstrates a close association with mobility correlation patterns, which are strongly influenced by the distribution of facilities and the connectivity of the road network. Thus, using the framework offers valuable insights into risk mitigation and planning of critical facilities in extreme situations. It enhances the understanding on how urban resilience to disaster is influenced by urban form and flow interactions, generating robust evidence for policy. [ABSTRACT FROM AUTHOR]

• An energy-inclusive SVI indicator system is proposed to increase resilience. • Energy factor extends the inclusiveness and integrity of traditional SVI system. • Energy-inclusive SVI reveals spatial differences in potential social vulnerability. • Uneven urbanization exacerbates risks and inequalities for vulnerable groups. A comprehensive vulnerability assessment is a scientific basis for the realization of the United Nations' sustainable development goals. Energy resilience plays a crucial role in mitigating social vulnerability due to disaster shocks. Often, energy infrastructure and services collapse after disasters. The recent Russia-Ukraine war has exacerbated Europe's energy crisis and social vulnerabilities, making it even more urgent to add energy resilience to vulnerability assessments. This paper takes the Netherlands as the study area for vulnerability assessment, constructs a new social vulnerability indicator (SVI) system supplemented with the energy element, and compares that with the traditional energy indicator system. The results indicate that: 1) The introduction of energy indicators fills the gap of traditional SVI assessment. 2) Energy indicators reveal regional and spatial differences in potential social vulnerability in the Netherlands. 3) Energy-inclusive SVI demonstrates that uneven urbanization exacerbates risks and inequalities for vulnerable groups, with potential impacts on social vulnerability. Sustainable urban development requires the search for a recognized and coordinated approach to managing vulnerability across regions. The complementarity of energy indicators offers opportunities to provide a more comprehensive assessment of spatial patterns of social vulnerability, identify potentially vulnerable areas, enhance urban disaster resilience, and achieve sustainable urban development. [ABSTRACT FROM AUTHOR]

Reliable assessment of the resilience of transportation networks to natural disasters is a prominent issue. This paper investigates how the spatial feature of natural disasters affects the resilience of transportation networks. Our research reveals that incorporating spatial feature data on disaster impacts is highly important for assessing network resilience. More precisely, the uncertainty linked to the estimation of the resilience index in the context of disaster-induced interruptions with spatial feature significantly exceeds that of conventional approaches. We present an illustrative case of a transportation network affected by a hurricane. The results reveal that the improved characteristics of disaster-induced outages greatly alter the manner in which the network recovers, including changes in emergency system characteristics. Our research indicates that extant methodologies employed for evaluating the resilience of critical infrastructure potentially inflate the confidence levels pertaining to network recovery predictions, primarily owing to insufficient attention given to the spatial configuration of disasters. [ABSTRACT FROM AUTHOR]

• Water resources system has multi-link characteristics. • The resilience evaluation index system of water resources system is constructed. • The resilience of regional water resources system is evaluated. • The temporal and spatial evolution characteristics of resilience are analyzed. As environmental and human changes combine, water shortage and ecological fragility are becoming increasingly serious issues in the basin. The national "14th Five-Year Plan" proposes to build resilient basins to enhance disaster resilience. On the basis of clarifying the multi-link characteristics of water resources system resilience under the influence of meteorology-hydrology-ecology-society-economy, this paper reveals the interrelationship and chain reaction among the factors affecting the resilience of water resources system by clarifying the multi-link characteristics of water resources system resilience. Based on the interconnections among meteorology-hydrology-engineering-economic society-ecology in regional water resources systems, this study constructs a resilience evaluation index for the water resources system. The index considers different objectives, dimensions, and scales to provide a comprehensive assessment of the system's resilience. Based on the multi-dimensional relationship of the evaluation index system, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method based on entropy weight method is employed to depict the resilience level of the water resources system at varying spatial and temporal scales. By focusing on Shanxi Province as the research subject, a multi-link evaluation framework for assessing the resilience of the water resources system is established, and a quantitative approach to evaluate its resilience is investigated. The results show that the level of water resources resilience in Shanxi Province has improved from 2017 to 2021, and there are certain spatial and temporal differences. In 2021, only Lvliang City and Yangquan City have low resilience level, and the resilience level of other cities has been improved. Among them, Taiyuan City has the highest resilience level of 0.56, which belongs to the high resilience level. The research results can improve the cognitive level of multi-link evolution mechanism of resilience to a certain extent, and provide scientific basis for multi-dimensional targeted regulation of water resources system resilience. [ABSTRACT FROM AUTHOR]

• This paper provides a comprehensive conceptual analysis of disaster resilience in climate-vulnerable communities. • The research employs the Walker and Avant Method to elucidate the notion of resilience within the framework of climate vulnerability. • A practical framework and a set of ecological indicators are proposed for environmental impact assessments in disaster resilience. • The research provides tangible illustrations of the proposed approaches and indicators in practical contexts. Resilience is a prior term of debate in vulnerability research irrespective of discipline. Nowadays the resilience concept has been used abnormally in many cases without realizing its intended meaning. Therefore, this study clarifies the resilience concept in the context of climate vulnerability. An integrated review of the literature has been done by the PRISMA approach to identify the most relevant documents. Besides Walker and Avant Method has been used to analyze the concept of resilience. The use of concept in different disciplines, historical perspectives, dimensions, attributes, consequences, and relationship with vulnerability and adaptability have been reviewed to clarify it. This study also provides a theoretical and workable definition of resilience from the perspective of climate change and natural hazards. A conceptual framework has been developed with concept mapping, contributing to the existing debate on resilience study. A case analysis has been done by focusing on a climate-vulnerable community living in the riverine islands in Bangladesh. This article shows that socio-ecological systems approach to defining small island disaster resilience has shortcomings. Additionally, the research-policy divide in resilience studies is exacerbated by the paucity of studies that use the participatory approach to identify disaster resilience indicators. The study makes a compelling case for further research on how to turn academic discoveries into a resource that might benefit local people, particularly those on small islands. The study suggests that social community-based resilience should be developed to protect geographically isolated communities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Seeing El Niño only as a disaster in Peru obscures its role as a phenomenon of opportunity for desert farming and fishing. • Education for disaster management can align with school curricula to enhance the role of youth and children in development. • Using storytelling methodologies innovatively in school curricula can contribute to better community resilience planning. • A digital curriculum on El Niño gave voice to children as development knowledge producers and communicators during COVID-19. This paper calls for greater attention to the role of youth and children as development actors in the context of education for disaster management. Drawing on debates in disaster studies and children's geographies, we explore the possibilities offered by everyday formal education spaces, often overlooked in disasters management practice, to engage children in disaster preparedness and resilience planning. Using the case study of Peru, we examine the extent to which national responses to the restrictions that the COVID-19 pandemic placed on in-person teaching, opened-up opportunities to engage with disaster management in new ways. We draw on the case of an innovative digital curricula that uses intergenerational storytelling about the El Niño phenomenon to investigate livelihood opportunities and climate change pressures in northern coastal Peru, exploring how the phenomenon benefits desert populations. We assess the role of participatory virtual learning in facilitating disaster knowledge and climate adaptation awareness among students and critically examine the youth subjectivities that are constructed through these processes. We conclude calling for greater engagement with children's formal education spaces in climate adaptation strategies, while cautioning against conceptualising children and young people as only 'adults in the making', rather than as impacted individuals with current agency and everyday capacities. [ABSTRACT FROM AUTHOR]