Search

The Indian subcontinent is highly susceptible to natural disasters. To abate the effect of the disasters, the government emanates institutional mechanisms and strategies for disaster preparedness, mitigation and response. Although there is well-established institutional framework in India for disaster management, there is no study that proposes a disaster supply chain structure under the institutional framework and integrates information and digital technologies (IDT) of industry 4.0 within it. The present study overcomes this research gap. It introduces an IDT structure within the institutional framework of India for multi-agency information sharing, coordination and decision making. It formulates a mathematical model and analyses the impact of IDT on the total response time through three scenarios. The results of scenario analysis reveal the significance of IDT on the disaster supply chains and its capability to handle information delays and IDT failure. The present study can be adopted by disaster management institutions to construct and implement better response practices at the operational, planning and strategic levels. Furthermore, the proposed model in the study can be used for vaccination planning, which includes vaccine distribution, monitoring, regulation and effective implmentation as a response against the current pandemic situation caused due to SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Developing an effective emergency collaborative distribution system is critical to improve on-time response performance, especially considering secondary disasters. To address the challenge, this paper investigates a bi-objective robust optimisation model on relief collaborative distribution among three echelons of authorities, the province, the municipality, and the county, which aims to minimise the total travel time and the total humanitarian logistics cost simultaneously. The optimal location of relief supply facilities and the relief distribution schemes will be determined by the optimisation model, which considers uncertain demand and travel time. Moreover, two robust optimisation methods are utilised to deduce the robust counterparts of the proposed model. An epsilon-constraint-based approach is used to solve the bi-objective optimisation model. A real-world case study based on an earthquake and aftershocks with different magnitudes in Yunnan Province is provided. The results show that incorporating secondary disaster scenarios contributes to reducing the total travel time and cost. For making full use of emergency resources and preventing situations from worsening, the centralised decision scheme is more effective than the decentralised one. The uncertainty of demand of primary disaster relief has a higher impact on the optimal solution than that of travel time. [ABSTRACT FROM AUTHOR]

After exogenous demand shocks caused by natural disasters, FinTech lenders are more responsive to increased demand for reconstruction mortgages than traditional banks and non-FinTech shadow banks. Both FinTech and traditional banks increase credit supply, but FinTech supply is more elastic without increases in risk-adjusted interest rates or delinquency rates. Comparing lending supply channels, banks respond to regulatory incentives to lend to damaged areas, whereas FinTech lenders supply more credit when traditional banks rely more on balance sheet financing and physical branch networks. Compared to traditional banks, FinTech lenders increase supply elasticity more aggressively in response to local competitive pressure. [ABSTRACT FROM AUTHOR]

In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program. It enriches the solution method of forest fire emergency rescue vehicle dispatching problem in Great Khingan area, which is of great significance to improve the emergency rescue capability in case of sudden forest fire. Through simulation experiments, the proposed Improved Genetic Algorithm (IGA) achieved an average rescue time reduction of 8.5% compared to conventional Genetic Algorithm (GA) and 3.5% compared to Improved Artificial Bee Colony (IABC) algorithm, with an average solution time of 9.4 ms. [ABSTRACT FROM AUTHOR]

Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities. Subsequently, we establish an initial regression model using the TrAdaBoost algorithm based on the hydrologic data from the selected watershed stations. Finally, we refine the initial model by incorporating multiple spatiotemporal views, employing semi-supervised learning to create the STH-Trans model. The results of our experiments underscore the efficiency of the STH-Trans model in predicting runoff for ungauged basins. This innovation leads to a substantial increase in model accuracy ranging from 7.9% to 30% compared to various conventional methods. The model not only offers data support for water resource management, flood mitigation, and disaster relief efforts, but also provides decision support for hydrologists. [ABSTRACT FROM AUTHOR]

Background: Social trust in relief organizations is one of the prerequisites for community preparedness for disasters. This study aims to explore the challenges of social trust in disaster management in Iran with a focus on the COVID-19 pandemic, and provide recommendations for building social trust. Materials and Methods: This is a qualitative study using a conventional content analysis method. Participants were 27 lay people and managers of emergency and relief organizations in Tehran who were selected purposefully with maximum diversity. We used in-depth semi-structured interviews to explore their experiences and perceptions. Data analysis was performed using MAXQDA software, version 2018 and Graneheim and Lundman's content analysis method. Results: In this study, two main themes emerged, "challenges to building social trust" and "recommendations for building social trust." The challenges had four categories: Managerial issues, misunderstanding of the situation, lack of transparency, and lack of coordination. The recommendations had three categories: Management improvement, efficient risk communication, and experience-based interventions. Conclusion: Social trust during disasters in Iran can be improved by transparency in the performance and tasks of relief organizations, timely and accurate provision of information (risk communication), and emphasis on social participation and solving managerial issues. [ABSTRACT FROM AUTHOR]

American communities have been the hardest hit by the COVID-19 pandemic. The collaboration between governments and nonprofits is needed to satisfy the needs of the American communities. This article investigates the organizational factors of nonprofits that contribute to the obtainment of government disaster relief funds in the context of the COVID-19 pandemic by surveying nonprofits whose main offices were located in California. The findings show that, in general, having internal professionals in fundraising, and having previous experience in government applications contribute to the successful receipt of government relief funds. Moreover, different factors are associated with the receipt of relief funds from different levels of government. This article offers valuable insights to government and nonprofit funding relations and how their collaboration can enhance community resilience during times of disasters. [ABSTRACT FROM AUTHOR]

The frequent occurrence of global disasters poses unprecedented challenges to nursing practice. The frontline nurses in disaster relief are exposed to these events and bear significant levels of stress and psychological distress. Resilience and posttraumatic growth (PTG) are essential protective factors that contribute to sustaining their mental health. The purpose of this study was to determine the directional relationship between resilience and PTG using a cross-lagged design. Furthermore, employing longitudinal mediation to test whether the T1 resilience of frontline nurses would promote the development of T3 resilience through the mediating role of T2 PTG. A total of 258 frontline nurses were selected as subjects. They completed self-reported measurements in three periods. The present study was conducted using a cross-lagged panel model and a longitudinal mediation model. The results of cross-lagged path analysis from T2 to T3 showed that PTG could positively predict the development of resilience (β = 0.235, p < 0.001). Resilience did not positively predict the development of PTG (p > 0.05). The analysis of mediating effect results showed that the development of PTG at T2 mediated the relationship between resilience from T1 to T3. Findings may be limited by self-report, recall bias of resilience before the epidemic and short tracking frequency. These results can identify individuals with an increased risk of low resilience under disaster and the mediating role of posttraumatic growth in promoting the development of nurses' resilience, which provides a theoretical basis for psychological crisis intervention and the resilience promotion plan for posttraumatic growth under disaster events. • Cross-lagged and longitudinal mediation are used to infer causality. • The posttraumatic growth of frontline nurses can positively predict their resilience. • The posttraumatic growth of frontline nurses serves as a mediator in the development of resilience. • Developing posttraumatic growth in frontline nurses is essential to bolster resilience and prepare for future medical crises. [ABSTRACT FROM AUTHOR]

This paper examines the causal effects of disaster relief on employment using municipality level data. To do this, we focus on the disaster relief for the Great East Japan Earthquake. Our empirical results show that while simple difference-in-differences estimation does not necessarily show statistically significant effects, the estimation results using some matching techniques are robust. Our results suggest that natural disaster relief could contribute to employment recovery in afflicted areas, contrary to some theoretical assumptions that fiscal expansion depresses private-sector employment. [ABSTRACT FROM AUTHOR]

In this article, we study the integrated hurricane relief logistics and evacuation planning (IHRLEP) problem, integrating hurricane evacuation and relief item pre‐positioning operations that are typically treated separately. We propose a fully adaptive multistage stochastic programming (MSSP) model and solution approaches based on two‐stage stochastic programming (2SSP). Utilizing historical forecast errors modeled using the auto‐regressive model of order one, we generate hurricane scenarios and approximate the hurricane process as a Markov chain, and each Markovian state is characterized by the hurricane's location and intensity attributes. We conduct a comprehensive numerical experiment based on case studies motivated by Hurricane Florence and Hurricane Ian. Through the computational results, we demonstrate the value of fully adaptive policies given by the MSSP model over static ones given by the 2SSP model in terms of the out‐of‐sample performance. By conducting an extensive sensitivity analysis, we offer insights into how the value of fully adaptive policies varies in comparison to static ones with key problem parameters. [ABSTRACT FROM AUTHOR]

Against the backdrop of global warming and rising sea levels, storm surge disasters occur frequently, often forming complex chains of events that lead to severe crises. However, systematic research on storm surge disaster chains is scarce. To characterize these chains, this research proposes a storm surge disaster chain analysis system based on complex networks and Bayesian networks. The system consists of three modules: evaluation, prediction, and measurement. The evaluation module uses a complex network model to quantitatively analyze the vulnerability, key nodes, and critical transmission paths of the disaster chain complex network. The prediction module establishes a Bayesian network-based model to forecast the complex network evolution process, forecasting the occurrence probability and loss scenarios of the disaster events. The measurement module measures and calculates the chain effect based on the dependence relationship and loss degree of the disaster event loss scenario. The results elucidate that most key nodes are primary and secondary disasters such as seawater flooding, flooding, dam damage, rainstorm, and house damage. Meanwhile, edges such as the sea wave–seawater flooding and house damage–human casualties have a critical impact on the storm surge disaster chain complex network. Key evolutionary paths such as strong winds–human casualties and over-warning tide level–social influence need to be focused on. Disaster reduction strategies such as maintaining dams, reinforcing houses, and removing disaster-bearing body can effectively break the chain and mitigate disasters. This research has a reference value for the scientific understanding of storm surge disaster chains and can serve as a scientific basis for comprehensive disaster reduction, disaster preparedness, and disaster relief. [ABSTRACT FROM AUTHOR]

In northern Ethiopia, meteorological droughts overwhelm crops and often lead people to food insecurity and poverty traps. Socioeconomic droughts similarly aggravate poverty and impact livelihoods; causing significant challenge on communities. Such issues in the area were not researched and not well addressed. Hence, the objective of this study was to evaluate the temporal and spatial trends of meteorological drought and the implied socioeconomic drought impacts in the Tekeze Watershed. The main data sources for this study included survey questionnaires, field observations, and CHIRPS data. Precipitation data from various stations were also employed to validate the CHIRPS data using a random forest regression model. The results revealed a strong coefficient of determination for the model, with values of 0.88, 0.87, 0.88, and 0.84 for the Mekele Obseva, Sekota, Yichila, and Ashere stations, respectively. This indicates that the precipitation recorded at these stations can be well-explained by the CHIRPS data. The temporal trends of meteorological drought showed that most of the years faced shortage of rainfall, where the year 2015 exceptionally faced a severe drought. The drought conditions in the area were exacerbating from time to time with a drought reoccurrence period of 2 years. The socioeconomic drought also found similar results within the agricultural drought that the years 2000, 2002, 2004–5, 2009–11, 2013–15, 2017, and 2021–23 were affected by droughts of different severity levels and associated socioeconomic impacts. Accordingly, almost all (99.5%) of the respondents reported that they were personally experiencing droughts. The major causes of drought in the study watershed were climate variability, land use changes, land degradation, water mismanagement, deforestation, war, and desert locusts. The consequences of these overlapping crises include pandemics, malnutrition, displacement, crop losses, desertification, and conflicts over resource use. While proposed interventions like improved irrigation, water infrastructure, drought-resistant crops, and emergency relief were intended to address these issues, ill-guided procedures and inadequate execution have undermined their effectiveness. Thus, these measures have not been successfully implemented and have fallen short of addressing the tangible impacts of drought. To this end, the study recommends effective implementation of the mitigation measures initially implemented by the government and nongovernmental organizations, emphasizing the active involvement of the local community. [ABSTRACT FROM AUTHOR]

Background: Climate-related disasters have tripled in the past 30 years. Between 2006 and 2016, the global sea levels rose 2.5 times faster than the entire 20th century. More than 20 million people a year are forced out of their homes because of climate change impacts. Rapid urbanization and increasing population density in coastal mega-metropolitan areas will inevitably lead to more large-scale disasters due to extreme weather events, i.e., stronger storms and massive flooding. Despite the inevitability of these events, disaster risk reduction is still locally based in each country, many of which have scarce resources to devote to the activity. It is widely assumed that the global community will respond when a calamity occurs. This perspective article explores the appropriateness of the current international relief and aid paradigm in light of near and middle-term trends in global disasters. Main body: After briefly summarizing the anticipated effects of global climate change, population growth, and progressive urbanization in low-lying coastal and riverine environments on the frequency and scale of future disasters, this paper examines how existing concepts of international relief following disasters are insufficient to address the challenges to come. Current paradigms are tested against selected case studies demonstrating the growing frequency of mega-disasters. For example, in 2010, the world saw a catastrophic earthquake in Haiti, very large-scale floods in Pakistan, a major earthquake in Chile, and heat waves that resulted in the death of tens of thousands of people in Russia and many more in other countries. However, the world exhausted most of its humanitarian aid, responding to Haiti in January of that year. The review closes with a proposition for a new paradigm to re-organize international relief to meet the challenge posed by our rapidly changing world – one that is more adaptable to the current challenges of climate change and other trends that will almost certainly increase the frequency and intensity of disasters. Conclusion: The future of international disaster aid depends on our ability to foster greater cooperation between the various organizations and donor countries and more seamless cooperation between both groups and the affected countries or regions. Planning and relief operations should utilize new technologies and innovative financing where feasible. A holistic approach that focuses on building large-scale agreements and coordination mechanisms, teaching citizens how to help each other until aid arrives, and strengthening resilience at the local level will equip communities for adaptive action during a disaster, improve coping and long-term rehabilitation, will lead to a more efficient, fairer and more durable global aid system. [ABSTRACT FROM AUTHOR]

Food insecurity in Ethiopia is an immediate humanitarian crisis that is expected to worsen due to population growth and climate change. This study applied GIS-based approaches to evaluate the feasibility of deploying an emergent type of single-cell protein (SCP) technology to supplement the nutritional needs of Ethiopian citizens who are most vulnerable to drought. The technology—power-to-protein (PtP)—uses H2 and O2 from water electrolysis and CO2 from woody biomass combustion in a two-stage bioprocess to produce nutrient-rich protein powder for human consumption. Population density, land use, and other geographical data were used to identify optimal site locations for these PtP systems based on two deployment strategies: large centralized plants vs. small decentralized units. The model also accounted for biomass availability, collection, and distribution logistics. The analysis revealed three sites that are both (highly) vulnerable/food-insecure and accessible within walking distance. The identified sites are proximate to the urban areas of Mekele in northern Ethiopia, Addis Ababa in central Ethiopia, and Hawasa south of Addis Ababa. If centralized PtP were deployed, the protein requirements of these populations could be sustained for several months, assuming a modest biomass collection radius of 35 km. Decentralized PtP deployment was similarly effective, requiring a distribution density of 5.4–11.0 PtP units per km2 under conservative estimates and 0.76–1.1 units per km2 under optimistic estimates. Lastly, a theoretical comparison showed that PtP is more efficient than conventional agricultural food production regarding biomass-to-protein conversion yields. Overall, our study suggests that PtP technology would be a feasible approach to supplement the nutritional needs of Ethiopian people in times of drought-related emergencies. However, given logistical limitations and considering social preference factors, it would be more practical to implement PtP in conjunction with standard emergency food aid measures. [ABSTRACT FROM AUTHOR]

Efficient landslide mapping is crucial for disaster mitigation and relief. Recently, deep learning methods have shown promising results in landslide mapping using satellite imagery. However, the sample sparsity and geographic diversity of landslides have challenged the transferability of deep learning models. In this paper, we proposed a universal adapter module that can be seamlessly embedded into existing segmentation models for transferable landslide mapping. The adapter can achieve high-accuracy cross-regional landslide segmentation with a small sample set, requiring minimal parameter adjustments. In detail, the pre-trained baseline model freezes its parameters to keep learned knowledge of the source domain, while the lightweight adapter fine-tunes only a few parameters to learn new landslide features of the target domain. Structurally, we introduced an attention mechanism to enhance the feature extraction of the adapter. To validate the proposed adapter module, 4321 landslide samples were prepared, and the Segment Anything Model (SAM) and other baseline models, along with four transfer strategies were selected for controlled experiments. In addition, Sentinel-2 satellite imagery in the Himalayas and Hengduan Mountains, located on the southern and southeastern edges of the Tibetan Plateau was collected for evaluation. The controlled experiments reported that SAM, when combined with our adapter module, achieved a peak mean Intersection over Union (mIoU) of 82.3 %. For other baseline models, integrating the adapter improved mIoU by 2.6 % to 12.9 % compared with traditional strategies on cross-regional landslide mapping. In particular, baseline models with Transformers are more suitable for fine-tuning parameters. Furthermore, the visualized feature maps revealed that fine-tuning shallow encoders can achieve better effects in model transfer. Besides, the proposed adapter can effectively extract landslide features and focus on specific spatial and channel domains with significant features. We also quantified the spectral, scale, and shape features of landslides and analyzed their impacts on segmentation results. Our analysis indicated that weak spectral differences, as well as extreme scale and edge shapes are detrimental to the accuracy of landslide segmentation. Overall, this adapter module provides a new perspective for large-scale transferable landslide mapping. [ABSTRACT FROM AUTHOR]

Automatic Target Recognition (ATR) from Synthetic Aperture Radar (SAR) data covers a wide range of applications. SAR ATR helps to detect and track vehicles and other objects, e.g. in disaster relief and surveillance operations. Aircraft classification covers a significant part of this research area, which differs from other SAR-based ATR tasks, such as ship and ground vehicle detection and classification, in that aircrafts are usually a static target, often remaining at the same location and in a given orientation for longer time frames. Today, there is a significant mismatch between the abundance of deep learning-based aircraft classification models and the availability of corresponding datasets. This mismatch has led to models with improved classification performance on specific datasets, but the challenge of generalizing to conditions not present in the training data (which are expected to occur in operational conditions) has not yet been satisfactorily analyzed. This paper aims to evaluate how classification performance and generalization capabilities of deep learning models are influenced by the diversity of the training dataset. Our goal is to understand the model's competence and the conditions under which it can achieve proficiency in aircraft classification tasks for high-resolution SAR images while demonstrating generalization capabilities when confronted with novel data that include different geographic locations, environmental conditions, and geometric variations. We address this gap by using manually annotated high-resolution SAR data from TerraSAR-X and TanDEM-X and show how the classification performance changes for different application scenarios requiring different training and evaluation setups. We find that, as expected, the type of aircraft plays a crucial role in the classification problem, since it will vary in shape and dimension. However, these aspects are secondary to how the SAR image is acquired, with the acquisition geometry playing the primary role. Therefore, we find that the characteristics of the acquisition are much more relevant for generalization than the complex geometry of the target. We show this for various models selected among the standard classification algorithms. • Performance of different dataset in DL models for SAR aircraft classification. • Generalization capabilities of DL models for SAR aircraft classification. • Different geographic locations, environmental conditions, and geometric variations. • Comparison using different classes of airplanes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic brought wide-ranging, unanticipated societal changes as communities rushed to slow the spread of the novel coronavirus. In response, mutual aid groups bloomed online across the United States to fill in the gaps in social services and help local communities cope with infrastructural breakdowns. Unlike many previous disasters, the long-haul nature of COVID-19 necessitates sustained disaster relief efforts. In this paper, we conducted an interview study with online mutual aid group administrators to understand how groups facilitated disaster relief, and how disaster relief initiatives developed and maintained over the course of the first year of COVID-19. Our findings suggest that the groups were crucial sources of community-based support for immediate needs, innovated long-term solutions for chronic community issues and grew into a vehicle for justice-centered work. Our insights shed light on the strength of mutual aid as a community capacity that can support communities to collectively be more prepared for future long-haul disasters than they were with COVID-19. [ABSTRACT FROM AUTHOR]

There is a growing recognition of the impact of the climate crisis and related disasters on coastal communities around the world. This qualitative study examined written reflections of nine graduate social work students from a public university in the Midwest region of the continental United States, following a service-learning course focused on disaster relief work in San Juan, Puerto Rico. The course linked critical reflection and service activities during the reconstruction phase following successive hurricanes. The activities were coordinated in partnership with Proyecto ENLACE, a public corporation that unites eight communities and twelve grassroots organizations located along the Caño Martín Peña waterway in center of San Juan. Students engaged in dialogue, daily group debriefings, and journaling. Service activities, determined by Proyecto ENLACE, included debris removal, clean-up, and painting of a community building that served as a hub for communication and distribution of food and water, medicine, and supplies. Following completion of the service-learning activities, students wrote papers critically reflecting on their assumptions, experiences, and learning processes. A qualitative analysis of students' papers revealed three themes: 1) understanding the dynamics of power, privilege, and oppression following a disaster; 2) increased knowledge of social, economic, and environmental justice issues in Puerto Rico; and 3) recognizing community agency, caring, and resilience. Findings support the use of critical reflection as a valuable pedagogical tool to teach students how to practice reflexivity, examine the dynamics of power and positionality, and prepare for the complexities of professional practice. [ABSTRACT FROM AUTHOR]

Geological disasters, as a common occurrence, have a serious impact on social development in terms of their frequency of occurrence, disaster effects, and resulting losses. To effectively reduce the casualties, property losses, and social effects caused by various disasters, it is necessary to conduct real-time monitoring and early warning of various geological disaster risks. With the growing development of the information age, public attention to disaster relief, casualties, social impact effects, and other related situations has been increasing. Since social media platforms such as Weibo and Twitter contain a vast amount of real-time data related to disaster information before and after a disaster occurs, scientifically and effectively utilizing these data can provide sufficient and reliable information support for disaster relief, post-disaster recovery, and public appeasement efforts. As one of the techniques in natural language processing, the topic model can achieve precise mining and intelligent analysis of valuable information from massive amounts of data on social media to achieve rapid use of thematic models for disaster analysis after a disaster occurs, providing reference for post-disaster-rescue-related work. Therefore, this article first provides an overview of the development process of the topic model. Secondly, based on the technology utilized, the topic models were roughly classified into three categories: traditional topic models, word embedding-based topic models, and neural network-based topic models. Finally, taking the disaster data of "Dongting Lake breach" in Hunan, China as the research object, the application process and effectiveness of the topic model in urban geological disaster information mining were systematically introduced. The research results provide important references for the further practical innovation and expansion of the topic model in the field of disaster information mining. [ABSTRACT FROM AUTHOR]

Disasters cause huge economic losses, affect the lives of many people, and severely damage the environment. Effective resource management during disaster preparedness and response phases improves distribution efforts and service levels and, hence, accelerates the disaster relief operations. Resource management in response to catastrophe has received increasing research attention in recent years, but no review paper focuses on this specific topic. Thus, the main purpose of this paper is to review the existing literature on resource management for disaster relief published in English in peer-reviewed journals in order to fill the gap. We apply bibliometric, network, and content analyses in our review to identify popular research topics, classify the literature into research clusters, and analyze the interrelationships between these research clusters. The second purpose of this paper is to identify gaps and trends in existing research. Finally, we propose six future research directions that provide a roadmap for resource management research for disaster relief. [ABSTRACT FROM AUTHOR]

The retrospective voting theory suggests that citizens vote for governing parties in response to distributive benefits. Knowing this, governments may reward voters by providing particularistic benefits—i.e., pork—prior to elections. Previous studies, however, do not account for the endogeneity. We address this problem by focusing on disaster relief and exploiting exogeneity of disaster. In particular, by using maximum hourly rainfall as an instrumental variable for disaster relief, we analyze the causal effect of disaster relief on incumbent's electoral outcomes. Our analyses of Japanese data in the past few decades indicate that disaster relief increased governing parties' vote share. Specifically, when the disaster relief per capita increases from zero to its mean, the predicted value of the governing parties' vote share increases by 2.8 and 5.4% points in the lower and upper chambers, respectively. The finding is consistent with retrospective voting behavior. Moreover, our results imply that the incumbent's electoral gain is brought about by persuading voters from oppositions to governing parties rather than by mobilizing supporters of governing parties. [ABSTRACT FROM AUTHOR]

Seismic simulation of urban buildings and roads is significant for regional pre-disaster mitigation and post-disaster recovery. To consider the interrelated influences of buildings and roads, an integrated seismic assessment method for urban buildings and roads is proposed. The seismic damages of buildings were assessed using various methods based on structural characteristics and different degrees of available building information. Both physical and topological characteristics of the road network are considered in the proposed method to determine post-earthquake road network traffic capacity. To quantitatively evaluate post-earthquake road network traffic capacity, we comprehensively considered the seismic damage to roads, blockages caused by earthquake-induced debris, and the potential risk of falling debris from damaged buildings. The proposed integrated seismic assessment method was applied to a real earthquake event to demonstrate its feasibility and effectiveness, and also applied to a real city, of which information on buildings and roads was based on open-source data and statistical data, to demonstrate its applicability. The proposed method provides a solid prediction on the seismic performance of urban buildings and road networks, serving as a reference for urban earthquake disaster rescue and relief. [ABSTRACT FROM AUTHOR]

Geological disasters in China have caused enormous damage to humans and the economy. The Chinese government has made significant efforts to mitigate geological disasters. Usually, the efficiency of disaster emergency response holds top priority. In this study, we considered the historical analysis of China's geological disaster emergency response as the primary line and developed a slacks-based measure data envelopment analysis model to evaluate the performance of 18 geological disasters reliefs during 2015–2019 in China. This model is used to examine the performance of the geological disaster emergency response activities. The results indicate that although the capabilities of geo-disaster relief have continuously improved from 2015 to 2019, China's geological disaster emergency response system remains in its primary stage. In particular, the efficiency of landslide emergency response operations is low. We analyzed the factors influencing efficiency and provided several suggestions for capacity improvement in geo-disaster emergency responses. [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequency, intensity, and duration of heatwave events have markedly increased, bearing profound implications for both natural ecosystems and human societies. To effectively cope with this challenge, it is imperative to accurately identify and comprehensively assess the risks posed by heatwaves. This study undertakes a systematic approach and robust methodology to assess heatwave risks by leveraging a diverse array of data sources—encompassing remote sensing, statistical analyses. The methodology integrates the risk triangle theory alongside established risk assessment frameworks laid out by the Intergovernmental Panel on Climate Change (IPCC). Employing a three‐dimensional evaluation framework encompassing hazard, exposure, and vulnerability, we unravel spatial–temporal patterns, high‐risk zones, and dominant dimensions of heatwave risks contributing to potential disasters. Results indicated that during 1999–2008, roughly 27% of the study areas were affected by high and above risk levels of heatwaves, and the areas with high and very high hazard, exposure, and vulnerability accounted for approximately 19.5%, 10%, and 32.5%, respectively. During 2009–2018, the proportion of areas with high and very high risk, hazard, and exposure increased to about 31%, 26%, and 14%, respectively, while the percentage of areas with high and very high vulnerability decreased to about 24.43%. Notably, Xinjiang and the western part of Northwestern China are characterized by hazard‐dominant conditions, while Southern China's risk profile has shifted from 1999–2008 to 2009–2018 from high hazard and vulnerability conditions to a more complex interaction involving hazard, exposure, and vulnerability. Moreover, Northern China and the northern segment of Southwestern China exhibit simultaneous high‐risk rankings across hazard, exposure, and vulnerability dimensions, forming a comprehensive high‐risk zone. These findings characterize heatwave risk patterns and offer critical insights for risk management decisions, guiding effective disaster prevention and relief measures to ensure socio‐economic stability and public health. [ABSTRACT FROM AUTHOR]

Copyright of FinanzRundschau is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper tackles a complex logistics challenge of disaster management, encompassing warehouse location, pre-disaster inventory planning, routing, and post-disaster relief supply delivery. We establish an iterative process for optimizing relief distribution to shelters. Adaptable warehouse inventory reallocation responds to fluctuating demands, guided by a two-phase mathematical programming approach. In the first phase, a two-stage stochastic programming (TSSP) model determines optimal warehouse and shelter locations and inventory levels. In the subsequent phase, we introduce a mixed-integer programming (MIP) model to minimize the overall delivery time by making routing decisions. To streamline the process, we introduce a novel enumeration algorithm that trims down route options by considering unavailable links, effectively transforming the MIP model into an assignment-based model. This innovation results in a noticeable 74% reduction in solution time. Further efficiency is achieved by developing a branch-and-cut algorithm for swift MIP resolution. A real-world case study confirms the practicality of our approach. [ABSTRACT FROM AUTHOR]

The article discusses the landslides triggered by an extreme rainstorm in the Haihe River Basin, China, in July 2023. The heavy rainfall led to widespread landslides, causing casualties and significant property losses in the Beijing-Tianjin-Hebei region. The study highlights the increasing frequency of extreme weather events and emphasizes the need for vigilance in monitoring and early warning systems for rain-induced geological disasters to prevent future losses. [Extracted from the article]

The role of the armed forces in Latin America is characterized by their participation in multiple internal missions. These range from security functions to the provision of social, educational, and public services, among others; their role also involves providing emergency relief. However, some of the armed forces involved in this type of mission do not have specialized units or corps. This poses obvious problems not only from a technical and operational point of view, but also from the perspective of civil-military relations and the definition of the roles of armies. Some Latin American countries have looked to the Emergency Military Unit in Spain as an example to follow for the implementation of a civil defense model based on specialized military resources. The aim of this paper is therefore twofold. First, it seeks to explain that the militarization of emergencies does not involve expanding the use of force but that it can become a "wildcard" policy tool instead. Second, it intends to show how the apparent success of the Spanish Emergency Military Unit resulted from some—not always positive—lessons that can be replicated in the armed forces in Latin America. [ABSTRACT FROM AUTHOR]

Recent disasters across the United States highlight the devastating effect of climate-change on individuals and households. The effects of these disasters on access to housing and housing stability are pressing issues of social equity and urban policy. How, if at all, do disasters affect rents? We find that severe floods are associated with significant increases in rents for households renting units priced at the bottom of the rent distribution, but not the middle or the top. Second, is there a relationship between federal rental assistance to affected households and any price changes in the market? We find that disaster rental assistance is not associated with changes in rents after flooding disasters. These findings raise important questions about how policy should support low-income renters after disasters, including those not directly displaced by the disaster who are facing increased rents after a disaster but unable to access federal post-disaster rental assistance. [ABSTRACT FROM AUTHOR]

This article examines how the New Zealand Defence Force (NZDF) builds and deploys its capabilities in the evolving Indo-Pacific strategic environment. Geography, domestic politics, and historical moments shape defense policy and spending. The NZDF continues traditional deployments--peacekeeping, logistics, maritime security, and humanitarian operations--aiming to maintain an independent foreign policy while contributing modestly to the international order. The NZDF plays a critical role in humanitarian assistance and disaster response (HADR) in the Pacific and Southeast Asia, areas where New Zealand enjoys goodwill. However, despite improved troop quality, New Zealand's defense spending and personnel numbers have declined, eliminating its air combat capabilities. While HADR and other nontraditional roles grow, including search, rescue, and resource protection, the NZDF shifts away from its traditional role of border defense. Coupled with reluctance to formalize alliances beyond Australia, this approach in a riskier environment seems like a dangerous gamble. [ABSTRACT FROM AUTHOR]

Extreme weather events, such as hurricanes with intense rainfall and storm surges, are posing increasing challenges to local communities worldwide. These hazards not only result in substantial property damage but also lead to significant population displacement. Federal disaster assistance programs are crucial for providing financial support for disaster response and recovery, but the allocation of these resources often unequal due to the complex interplay of environmental, social, and institutional factors. Relying on datasets collected from diverse sources, this study employs a structural equation model to explore the complex relationships between disaster damage (DD), social vulnerability (SV), public disaster assistance (PDA), the national flood insurance (NFI), and population migration (PM) across counties in the contiguous US. Our findings reveal that communities with lower SV tend to experience higher levels of DD across US counties. SV is negatively associated with PM, PDA, and NFI, both directly and indirectly. Furthermore, PDA is positively linked to PM, whereas DD has a direct negative effect on PM but an indirect positive effect through PDA. [ABSTRACT FROM AUTHOR]

Summary: High‐throughput satellites play an important role in emergency disaster relief, maritime, and other fields. A new generation of high‐throughput satellites with large deployable antennas and broadband beamforming networks, namely, very high‐throughput satellites (VHTS), is developing towards hundreds, even thousands of extremely narrow beams with Tbps capacity, which puts forward higher requirements for satellite pointing and system construction costs. In order to solve the problem that those traditional beam pointing measurement and calibration algorithms are difficult to apply or the performance is limited, this paper builds a service beam pointing measurement and calibration architecture. A user terminal‐assisted beam pointing measurement algorithm based on the Gauss‐Newton method is proposed for the general case, which can effectively reduce the construction cost of onboard and ground pointing measurement system, and improve the measurement accuracies of three axes of the satellite. Simulation results demonstrate the excellent performance under the ideal scenario. To achieve the future engineering application under the non‐ideal scenario, the terminal positioning error can be first neglected, then the pattern processing error and the terminal signal measurement error must be reduced by decreasing the pattern sampling interval, increasing the number of participant terminals, and other means. By comparing with a traditional beam pointing measurement algorithm, the proposed algorithm can achieve much lower beam pointing error than the baseline. [ABSTRACT FROM AUTHOR]

Purpose: The Elementary and Secondary School Emergency Relief (ESSER) requires districts to deliver "evidence-based interventions" to students impacted by the pandemic. The policy has created a unique opportunity for researchers and practitioners to engage with evidence to learn how recovery interventions work and under what conditions. Design/methodology/approach: This study is part of a research-practice partnership (RPP) between Guilford County Schools, AIR-CALDER, Harvard University and NWEA to understand the impacts and implementation of ESSER-funded recovery programs. We use a case analysis approach and frameworks of evidence-use and RPPs to explain how researchers and Guilford leaders engage with evidence to improve and evaluate programs. Findings: The RPP used evidence to inform Guilford leaders' recovery approaches and strengthened researchers' evaluations of programs. Conditions that enabled evidence engagement included the RPP's goals, research activities and collaborative conditions such as boundary spanning activities, team meetings, relationships and trust. We also observed factors that hindered evidence engagement, including the RPP's nascent stage, structure and breadth of goals, rapid policy timelines and other organizational conditions in Guilford. Originality/value: Given the complexities of pandemic recovery, RPPs can help researchers evaluate programs in their local context, and present evidence in ways that are actionable to guide decision-making. District leaders can play a valuable role in co-designing research studies attuned to local priorities and context and facilitating research participation among internal stakeholders. However, newly formed RPPs with broad goals for impact will need more time and resources to build an improvement infrastructure for sustaining pandemic recovery. [ABSTRACT FROM AUTHOR]