Search Results (4,198)

Rockfall is one of the most dangerous and unpredictable natural disasters that can seriously damage infrastructure. In traditional protection systems, sand is commonly used as a buffer material; however, the use of large sandbags as temporary protective structures has still not been investigated, and there are no established design guidelines available. This study aims to reveal the effect of rockfall impact on the dynamic response of a sandbag protection system for temporary restoration work in the event of a natural disaster. Initially, a numerical model based on finite element calculation was adopted to simulate the large sandbags under rockfall impact, which was verified by the full-scale experimental test data. The parameters identified were impactor velocity, acceleration, penetration depth, and sandbag displacement. After validation, the model was used for prediction analysis to examine the dynamic response and energy absorption characteristics of sandbags under different conditions, such as the influence of sand density, impactor velocity, impact height and the number of sandbags in the impact direction. The results propose an analytical basis for the establishment of performance-based guidelines for the design of sandbag walls as a temporary rockfall protection system. Full article

Background: Periprosthetic joint infections (PJIs), a significant complication of total knee replacement surgery, are influenced by patient, surgeon, and healthcare system factors. Natural disasters can disrupt healthcare services and alter microbiological factors in the hospital environment. The impact of natural disasters on pathogen distribution in periprosthetic joint infection (PJI) is unclear. Therefore, this study investigated the association between the 2023 Kahramanmaraş-centered earthquakes in Türkiye and changes in microbiological patterns of PJI after knee arthroplasty. Methods: This retrospective cohort study included patients who developed PJI following total knee arthroplasty at the study center. The patients were divided into two groups based on the timing of their PJI diagnosis: pre-earthquake and post-earthquake. The demographic characteristics, comorbid diseases, and perioperative characteristics of each patient were recorded, and their microbiological profiles were analyzed. Logistic regression analysis examined the relationships between patient-related factors and causative agents. Results: 56 patients were studied and divided into two groups: 26 patients in the pre-earthquake group and 30 in the post-earthquake group. Furthermore, 79 bacterial isolates were obtained from these patients. Demographic, metabolic, and preoperative characteristics were similar between the two groups. No significant difference was found in the overall distribution of bacterial isolates. However, Gram-negative organisms, primarily Acinetobacter baumannii and Pseudomonas aeruginosa, increased in the isolate distribution after the earthquake. Patient analysis revealed that polymicrobial PJIs were significantly more frequent after the earthquake (56.7% vs. 23.1%; p = 0.011). Diabetes mellitus (DM) and smoking were associated with an increased risk of polymicrobial infection; the association was not statistically significant. Conclusions: In the post-earthquake period, patients who had undergone total knee arthroplasty and developed PJI showed a higher proportion of polymicrobial infections and a numerical increase in Gram-negative pathogens, along with more complex infection patterns compared to the pre-earthquake period. Although both patient groups demonstrated similar characteristics regarding patient-related and surgical factors, the observed changes indicate that the pressure on the healthcare system after a natural disaster can affect a hospital’s microbiological ecology. Identifying these indirect effects is crucial for guiding microbiological surveillance and infection control during post-disaster recovery periods, even for elective patients. Full article

Identifying critical nodes in a multimodal transport network is fundamental to enhancing the resilience of transport systems against natural or man-made disasters. Although research on identifying critical nodes in complex networks has been deeply accumulated, its practical application in transport systems is still limited and requires further exploration. To bridge this research gap, an improved integrated method for identifying critical nodes in multimodal transport networks is proposed based on resilience theory. This method comprehensively incorporates disaster consequences and recovery time into resilience assessment, and an Improved Binary Particle Swarm Optimization (IBPSO) algorithm is developed to solve this problem efficiently. Then, a real case in China is conducted to verify the effectiveness of the proposed method. The results show that the proposed method can rapidly identify critical nodes in multimodal transport networks from the resilience perspective. These critical nodes, which are mainly concentrated at choke points with long repair durations, are often overlooked in traditional methods. Compared with the five baseline methods, the proposed method improves identification efficiency by more than 20%, verifying its better performance. The research findings can provide a scientific foundation for decisions on the effective protection and rapid recovery of critical nodes in the network. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

The Niger Delta region of Nigeria experiences multiple environmental stresses due to intensive oil exploration and pervasive gas flaring, both of which contribute to local and regional climate changes, extreme weather events, and excessive and erratic rainfall. Consequently, flooding remains a recurrent natural disaster, disproportionately impacting the low-lying states of Delta, Bayelsa, and Rivers. This study employs remotely sensed geospatial data and a GIS-based weighted overlay analysis to delineate flood-prone areas on a regional scale in the central Niger Delta states. Flood susceptibility was determined through a weighted overlay of digital elevation model (DEM), slope, proximity to streams, rainfall, and LULC data, among others. Weights of criteria were derived through an analytical hierarchy process (AHP) with a very good consistency ratio of 2.5%. Land use and land cover (LULC) and rainfall data were further analyzed to detect trends of changes between 2012 and 2022. The results show that relatively 77% of the study region is prone to flooding. Areas prone to very high flooding are about 16%, high is 29%, moderate is 32%, while low and very low flood-prone areas cover 18% and 5% of the study region, respectively. There is also a notable increase in average annual rainfall and land cover changes. Average rainfall increased by 58.1% between 2012 and 2017, and by 11.5% between 2017 and 2022. Land cover change analysis further indicates that approximately 1.3% of the study area was converted predominantly to flooded zones and water bodies from 2017 to 2022. The results of this study could be useful for urban regional planning, flood mitigation, and resettlement policies aimed at reducing flood vulnerability and enhancing resilience in the central Niger Delta, as well as other places where similar challenges exist. Full article

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications. Full article

Land trusteeship is an innovative agricultural management model that connects smallholder farmers with modern agriculture. It promotes large-scale agricultural operations, but still faces the impacts of conventional natural disasters. Although agricultural disaster insurance serves as a critical mechanism for farmers to mitigate these natural risks, its risk-mitigation potential remains underutilized due to the persistent challenge of low insurance participation rates. This study develops a decision-making model for farmers’ purchase of agricultural disaster insurance under land trusteeship, drawing on protection motivation theory, market failure theory, and quasi-public goods theory. Using structural equation modeling, we empirically analyze survey data from 319 land-trusteed farmers to uncover the mechanisms and pathways influencing their insurance purchase decisions. The results indicate that: (1) Vulnerability and severity are positively associated with protection motivation through perceived response efficacy and self-efficacy, and protection motivation is directly associated with purchase decisions; (2) Government support has both direct and indirect effects on purchase behavior; and (3) Individual and household characteristics are significantly associated with purchase decisions, with pure farmers, Type I part-time farmers, and farmers with larger landholdings tending to purchase agricultural disaster insurance more often. Full article

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

Social workers can play a significant role in promoting environmental citizenship to benefit vulnerable groups, such as those residing in informal settlement areas. With the proliferation of informal settlements in many African countries, the role of social workers in advocating for environmental citizenship is even more crucial. Their involvement should be evidence-based and entrenched in research that promotes an understanding of the impact of environmental degradation on human lives and their roles in environmental citizenship. Such knowledge should then inform environmental citizenship policies and programmes. Despite this crucial role as imposed by their professional mandate, policies, legislations and international treaties to address the conditions of marginalised and vulnerable people, environmental degradation continues to aggravate the vulnerability of people living in informal settlements. Furthermore, the scholarly contribution of social workers to environmental citizenship is delicate, with limited knowledge around the subject matter. Following the integrative literature review method, this paper outlines the nature of environmental citizenship, the relevance of social work to environmental citizenship, and the approach that social workers can adopt to contribute towards environmental citizenship in informal settlements. Literature around environmental citizenship in informal settlements, environmental disasters and informal settlements, and social work, as well as environmental citizenship and social justice, served as a population, from which a sample meeting predetermined inclusion criteria was purposefully drawn and analysed. The study confirms that, by its nature, environmental citizenship is central to social work and that there is a need to empower social workers around the subject matter. Full article

Addressing socioeconomic vulnerabilities is essential for building sustainable societies that can withstand climate change and natural disasters. Yet identifying the areas most at risk is often difficult, because relevant data are typically available only at the national level. To fill this gap, a subnational extension of the GDL Vulnerability Index was recently presented for 1260 areas across the developing world. This GVI is designed to reflect the human dimensions of vulnerability to climate change, natural hazards, and other kinds of shocks. In the present study, we introduce a new database providing comparable subnational data for 490 areas in 48 upper-middle- and high-income countries. Combined with the earlier data, this enables the first overview of subnational vulnerability on a global scale. Our findings show that, since 2000, overall vulnerability in wealthy countries has declined by 27%, while inequality in vulnerability has remained broadly stable. Incorporating subnational variation into the vulnerability profile of these countries raised the total observed variation in vulnerability up to 39%. By highlighting local inequalities in susceptibility, coping capacity and adaptive capacity, the Subnational GVI (SGVI) generates valuable new evidence to improve global monitoring and support policy responses aimed at climate resilience and sustainable development. Full article

Throughout history, wildfires have been prominent natural disasters that cause pollution, environmental damage and loss of lives. Local firefighting agencies and disaster response initiatives have typically managed to contain fires and limit damage to controllable levels. However, in recent times, due to climate change and human population growth, wildfire occurrences are becoming less predictable and result in greater cost and damage. Solutions employing new technologies and a more operations-oriented analysis, through system-of-systems (SoS), could be a promising way to combat further wildfire devastation. Designing new aircraft and strategies that can be used in human transport and firefighting is one of the goals of the COLOSSUS project. To enable international innovation, especially amongst young researchers, a student competition called the X-Challenge was released. This paper will deal with the overview of the challenge, breaking down its objectives, constraints, research contributions and outcomes. Following this paper, four different student teams will present their solutions, including innovative aircraft designs and SoS analysis methods. The knowledge gained, and successes and failures from the challenge, alongside outlook and recommendations for future challenges and SoS exploration, will be discussed in this paper. Full article

Automated building damage assessment from satellite imagery has become increasingly critical for rapid disaster response and humanitarian relief operations. However, current state-of-the-art deep learning models exhibit significant generalization challenges when deployed to geographically and environmentally diverse regions. This study investigates the nature and extent of geographic bias in building damage detection systems, revealing that model performance degradation occurs primarily from geographic and structural characteristics rather than insufficient training data representation. Through a systematic evaluation of top-performing xView2 competition solutions across 17 disaster locations across multiple climate zones, we found that even state-of-the-art models struggle with generalization under geographic shift, particularly for the minor and major damage classes, and exhibit strong geographic biases toward certain regions. In this work, we explore a six-channel Fusion Augmentation strategy that enriches RGB imagery with auxiliary structural enhancement channels, together with supervised fine-tuning and unsupervised CORAL-based domain adaptation for three unseen regions. The experimental results demonstrate a substantial improvement of 7.1% overall F1 score, with notable gains for intermediate damage categories such as minor and major damage. Domain adaptation experiments on three unseen locations show that combining Fusion Augmentation with supervised fine-tuning yields 40.8% and 60.0% improvements over the minor and major classes, while unsupervised CORAL achieves 24.2% and 39.5% improvements over the minor and major damage classes compared to benchmarks. These findings highlight persistent geographic bias and demonstrate that structural feature enhancement combined with domain adaptation is essential for globally deployable damage assessment systems. Full article

Urban–rural information infrastructure (URII) serves as the backbone of the “Digital Village” strategy; however, it faces significant threats from natural disasters and socioeconomic disparities. This study proposes a comprehensive resilience evaluation framework based on the pressure–state–response (PSR) theory. To address the limitations of traditional subjective weighting, we construct an integrated assessment framework that combines the entropy weight method with an improved concept lattice-weighted cluster DEMATEL method, effectively handling cognitive differences among experts. Using Lijin County in Shandong Province as a case study, we assess resilience levels from 2018 to 2022 via the VIKOR method. The results indicate a robust upward trajectory in overall resilience, progressing from a low-level state in 2018 to a high-resilience state in 2022. However, a dimensional comparative analysis identifies pressure resilience as the most critical weak point; consequently, the study establishes that the priority for future resilience enhancement follows the order: pressure > state > response. Based on these findings, specific countermeasures focusing on disaster risk monitoring and infrastructure redundancy are proposed to foster sustainable rural digital development. Full article