Search Results (31)

Climate-related disasters are escalating in frequency and severity, yet global adaptation finance remains critically insufficiently structured to respond after disasters occur rather than before. This study empirically examines disaster loss data, climate finance flows, and financial instrument evidence to test two hypotheses: whether climate finance is disaster-reactive, and whether policy uncertainty constrains it. We integrate data from the Emergency Events Database (EM-DAT), covering seven climate-induced hazard types (droughts, extreme temperatures, floods, glacial lake outburst floods, wet mass movements, storms, and wildfires), in addition to the OECD Creditor Reporting System (CRS), the World Uncertainty Index (WUI), the ND-GAIN vulnerability index, and the World Governance Indicators, the Green Climate Fund Open Data Library, and the Artemis Deal Directory across 131 countries (2011–2024) for Hypothesis 1 and 100 countries (2012–2024) for Hypothesis 2. Fixed-effects panel regressions with Driscoll–Kraay standard errors confirm that prior-year disaster losses significantly predict subsequent climate finance flows (β = 0.040, p = 0.009; N = 1769 country-year observations), establishing a reactive financing pattern. Policy uncertainty interacting with high vulnerability is found to suppress adaptation finance flows (β = −2.587, p = 0.080, N = 878 country-year observations), with the effect concentrated among the most climate-exposed economies. We propose a risk-layered climate finance architecture aligning instruments with distinct hazard tiers across the Global South. Credible policy signals, strategic public investment, and systematic integration of insurance mechanisms are essential preconditions for unlocking scalable, forward-looking resilience finance. Full article

This study evaluates deterministic flood fatality models using a harmonised dataset of river and flash flood events in Europe (1980–2024). The objective is to quantify differences across data sources and critically assess the applicability of commonly used prediction models for hydrological floods, with particular emphasis on flash floods, which remain poorly represented in existing methodologies. The analysis integrates large-scale databases on flood fatalities (HANZE, EM-DAT) with detailed event-based studies containing hazard and other indicators, enabling a combined evaluation from different sources. Three model groups are assessed by comparing predicted and observed fatalities: Damage–Fatality, Depth–Fatality, and Depth–Velocity–Fatality approaches. Results confirm discrepancy between exposure and mortality: river floods dominate in terms of affected population (87%) and economic losses (71%), whereas flash floods account for nearly half of all fatalities despite affecting only 13% of people. All evaluated models show significant limitations for prediction of flash floods fatalities; single-parameter approaches perform poorly, while multi-parameter models remain highly sensitive to uncertain hydraulic inputs. The study demonstrates that current methods are not transferable to flash flood conditions and highlights the need for integrated, multi-variable approaches supported by consistent and high-quality datasets. The main contributions of the study are the first systematic validation of widely used models against historical river and flash flood events, revealing their uncertainties, and a comprehensive assessment of their robustness and sensitivity to key input indicators. Full article

Against the backdrop of intensifying global climate change and human activities, the increasing frequency and evolution of major wildfire events pose severe challenges to global disaster prevention and mitigation systems. Systematically understanding their disaster characteristics, spatiotemporal patterns, and societal response efficacy is an urgent scientific requirement for formulating effective coping strategies. This study constructed a comprehensive database covering 137 major global wildfire events from 2018 to 2024, with data sourced from GFED, EM-DAT, and official national reports. Utilizing a synthesis of methods including descriptive statistics, spatiotemporal clustering analysis, K-means pattern recognition, and non-parametric tests, a multi-dimensional quantitative analysis was conducted on disaster characteristics, evolutionary trends, casualty patterns, and policy effectiveness. Despite potential reporting biases and heterogeneous data standards across countries, the analysis reveals the following: (1) All key wildfire metrics (e.g., burned area, casualties, evacuation scale) exhibited extreme right-skewed distributions, indicating that a minority of catastrophic events dominate the overall risk profile; (2) Global wildfire hotspots demonstrated dynamic expansion, spreading from traditional regions in North America and Australia to emerging areas such as Mediterranean Europe, Chile, and the Russian Far East, forming three significant spatiotemporal clusters; (3) Four distinct casualty patterns were identified: “High-Lethality”, “Large-Scale Evacuation”, “Routine-Control”, and “Ecological-Destruction”, revealing the differentiated formation mechanisms under various disaster scenarios; (4) A substantial gap of nearly 65 times in emergency evacuation efficiency—defined as the ratio of evacuated individuals to total casualties—was observed between developed and developing countries, highlighting a significant “development gap” in emergency management capabilities. This study finds evidence of increasing extremization, expansion, and polarization in global wildfire risk within the 2018–2024 event sample. The conclusions emphasize that future risk management must shift from addressing “normal” events to prioritizing preparedness for “catastrophic” scenarios and adopt refined strategies based on casualty patterns. Simultaneously, the international community needs to focus on bridging the emergency response capability gap between nations to collectively build a more resilient global wildfire governance system. Full article

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

Natural disasters occur continuously across the globe, posing severe threats to human life and property. Remote sensing technology has provided powerful technical means for large-scale and rapid disaster monitoring. However, the deep integration of remote sensing observations with sector-specific disaster statistical data to construct a knowledge system that supports early warning decision-making remains a significant challenge. This study aims to address the bottleneck in the “data-information-knowledge-service” transformation process by constructing an integrated natural disaster early warning knowledge graph that incorporates multi-source heterogeneous data. We first designed an ontological schema layer comprising six core elements: disaster type, event, anomaly information, impact information, warning information, and decision information. Subsequently, multi-source data were integrated from various sources, including the Emergency Events Database (EM-DAT), sector-specific websites, encyclopedic pages, and remote sensing imagery such as Gaofen-2 (GF-2) and Sentinel-1. A Bidirectional Encoder Representations from Transformers with a Conditional Random Field layer (BERT-CRF) model was employed for entity and relation extraction, and the knowledge was stored and visualized using the Neo4j graph database. The core innovation of this research lies in proposing a quantitative methodology for assessing disaster intensity, impact, and trends based on remote sensing evaluation, establishing a knowledge conversion mechanism with sector-specific warning levels, and designing explicit warning issuance rules. A case study on a specific wildfire event (2017-0417-PRT, Coimbra, Portugal) demonstrates that the knowledge graph not only achieves organic integration and visual querying of multi-source disaster knowledge but also facilitates warning decision-making driven by remote sensing assessment indicators. For this event, quantitative analysis of Gaofen-2 imagery yielded intensity, impact, and trend levels of 4, 3, and 3, respectively, which, when applied to our warning rule (intensity ≥ 1 or impact ≥ 1 or trend ≥ 3), automatically triggered an early warning, thereby validating the rule’s practicality. A preliminary performance evaluation on 50 historical wildfire events demonstrated promising results, with an F1-score of 74.3% and an average query response time of 128 ms, confirming the system’s practical responsiveness and detection capability. In conclusion, this study offers a novel and operational technical pathway for the deep interdisciplinary integration of remote sensing and disaster science, effectively bridging the gap between data silos and actionable warning knowledge. Full article

Floods are among the most frequent and damaging natural hazards in the Mekong River Basin, where the interplay of monsoon-driven climate variability, complex topography, and rapid socio-economic change creates high exposure and vulnerability. This study presents a comprehensive assessment of flood disaster patterns, loss distribution, and regional disparities across five countries in the Lower Mekong Basin—Cambodia, Laos, Myanmar, Thailand, and Vietnam. Using multivariate spatiotemporal analysis based on EM-DAT, MRC, and national government datasets, the study quantifies flood frequency, casualties, and affected population to reveal cross-country differences in disaster impact and timing. Results show that while Vietnam and Thailand experience high flood frequency and storm-induced events, Laos and Cambodia face riverine flooding under constrained economic and infrastructural conditions. The findings highlight a basin-wide increase in flood frequency over recent decades, driven by climate change, land use transitions, and uneven development. The analysis identifies critical gaps in adaptive governance, particularly the need for dynamic policy frameworks that can adjust to spatial disparities in flood typologies (e.g., Vietnam’s storm floods vs. Cambodia’s riverine floods) and improve transboundary coordination of reservoir operations. Despite the region’s extensive reservoir capacity, most infrastructure prioritizes hydropower over flood mitigation. The study evaluates the role of regional cooperation frameworks such as the Lancang–Mekong Cooperation (LMC), demonstrating how strengthened institutional flexibility and knowledge-sharing mechanisms could enhance progress toward Sustainable Development Goals (SDGs) related to water governance (SDG 6), resilient infrastructure (SDG 9), and disaster risk reduction (SDG 11). By constructing the first integrated national-level flood disaster database for the basin and conducting comparative analysis across countries, this research provides empirical evidence to support differentiated yet coordinated flood risk governance strategies at both national and transboundary levels. Full article

The increasing recognition of historical emissions and uneven financial capacities among developed and developing nations has highlighted the need to look for equity and fairness in global climate action. This study aims to present a revised method that enables mapping the current state of fairness in the global energy transition, addressing both the contribution to the climate crisis and the burden that different countries face in coping with the climate disasters resulting from it. For this purpose, we revise various methods and indices used to measure the progress of energy transition efforts, as well as existing methodologies to appraise the responsibility for climate change and the resulting financial capacity. We propose changes to the existing methods to allow for a clearer analysis of the fairness of the global energy transition. An exemplary use of the proposed modified methodology is applied to six countries that represent developing and developed countries using publicly available data from renowned sources such as IRENA, EM-DAT, and the World Bank, showing the applicability of the method. The main trends in the results highlight the added value of the proposed method. The progress in the energy transition is evaluated in terms of fairness as a transition index by taking into account historical responsibility and financial capacity. Damage from climate-induced disasters and contribution towards climate financing are added as contextual considerations. The country’s historical emissions, GDP, NDC, financial costs of climate-induced disaster, and financing from the Green Climate Fund are used as the basis for the analysis. The findings underscore the differences in energy transition achievement, as well as the differences in pledged and deposited funds among various types of countries. The results demonstrate a disproportionate burden experienced by lower-income nations and depict the ongoing challenges in translating principles of “common but differentiated responsibilities” into concrete outcomes. This study provides an open-source and data-driven perspective that highlights the need for change in global policy discourse and also advocates for the creation of more nuanced, just, and effective approaches to accelerate the clean energy transition worldwide. Full article

The increasing frequency and destructiveness of natural disasters necessitate scalable, transparent, and timely analytical frameworks for risk reduction. Traditional disaster datasets—curated by intergovernmental bodies such as EM-DAT and UNDRR—face limitations in spatial granularity, temporal responsiveness, and accessibility. This study addresses these limitations by introducing a novel, AI-enhanced disaster intelligence framework that leverages 19,130 publicly available news articles from 453 global sources between September 2023 and March 2025. Using OpenAI’s GPT-3.5 Turbo model for disaster classification and metadata extraction, the framework transforms unstructured news text into structured variables across five key dimensions: severity, location, media coverage, economic resilience, and casualties. Hypotheses were tested using statistical modeling, geospatial aggregation, and time series analysis. Findings confirm a modest but significant correlation between severity and casualties ($\rho =0.12$, $p<{10}^{-60}$), and a stronger spatial correlation between average regional severity and impact ($\rho =0.31$, $p<{10}^{-10}$). Media amplification bias was empirically demonstrated: hurricanes received the most coverage (5599 articles), while under-reported earthquakes accounted for over 3 million deaths. Economic resilience showed a statistically significant but weak protective effect on fatalities ($\beta =-0.024$, $p=0.041$). Disaster frequency increased substantially over time (slope ${\eta }_1=53.17$, $R^2=0.32$), though severity remained stable. GPT-based classification achieved a high average F1-score (0.91), demonstrating robust semantic accuracy, though not mortality prediction. This study validates the feasibility of using AI-curated, open-access news data for empirical hypothesis testing in disaster science, offering a sustainable alternative to closed datasets and enabling real-time policy feedback loops, particularly for vulnerable, data-scarce regions. Full article

The Lancang–Mekong Region encompasses six countries, covering an area exceeding five million square kilometers and containing a population of more than 400 million. Floods in this region may cause extremely serious losses of lives and property. However, due to the severe shortage of flood disaster data, loss data and meteorological monitoring data, the assessment of flood disaster risks in this region remains highly formidable. In view of this, we systematically integrated the flood disaster data from EM-DAT (the Emergency Events Database), Desinventar (a disaster information management system), Reliefweb (a humanitarian information service provided by the United Nations Office for the Coordination of Humanitarian Affairs), and ADRC (the Asian Disaster Reduction Center), coupled with GLDAS (Global Land Data Assimilation System) precipitation data and the population and economic data from the World Bank, and comprehensively considered vulnerability, exposure, and loss criteria to assess the flood disaster risks in the Lancang–Mekong Region. The research findings are as follows: (1) From 1965 to 2017, a total of 370 floods occurred in the Lancang–Mekong Region, among which the proportion of floods in Vietnam and Thailand combined was as high as 43.7%. In contrast, the number of floods in Qinghai and Tibet in China was relatively small, with a combined proportion of only 1.89%. (2) When mild flood disasters occur, the southern part of Myanmar, the western part of Thailand, and the northeastern part of Vietnam are faced with relatively large loss threats; when moderate flood disasters occur, the central part of Myanmar, the eastern part of Cambodia, and the southern part of Vietnam are faced with comparatively large loss threats; when severe flood disasters occur, high-loss areas are mainly concentrated in the southern part of Vietnam. (3) Considering exposure, vulnerability, and hazards comprehensively, the high-risk areas of floods in the Lancang–Mekong Region are mainly distributed in the central–southern part of Myanmar, the northeastern part of Vietnam, and the southern part of the area bordering Cambodia and Vietnam; the medium-risk areas are mainly distributed in the central part of Thailand and the eastern part of Sichuan in China; relatively speaking, other areas in the Lancang–Mekong Region have a lower flood risk level. This research can provide references for flood risk assessment in regions with scarce data and technical support for flood disaster prevention and control as well as risk management in the Lancang–Mekong Region. Full article

This study examines the global health implications of extreme drought events from 2000 to 2023. Utilizing data from the International Disaster Database (EM-DAT), we analyzed the number of people affected and the total deaths attributed to drought. Our findings reveal that over 1.6 billion people have been impacted by drought globally, with Southern Asia and Sub-Saharan Africa being the most severely affected regions. India and China account for a significant portion of the affected population, with 688.2 million and 327.35 million impacted people, respectively. Drought-related mortality has also been substantial, with over 24,000 deaths recorded globally, including more than 20,000 in Somalia alone. The study highlights the uneven distribution of drought impacts, underscoring the need for targeted interventions and comprehensive drought preparedness strategies. Our analysis also reveals the critical role of socio-economic factors in exacerbating the health impacts of drought, particularly in regions with inadequate healthcare infrastructure and limited access to resources. This study provides novel insights into the specific health impacts of drought, including the correlation between drought frequency and mortality rates, and offers actionable recommendations for improving future emergency responses and health system preparedness. These recommendations are tailored to address the unique challenges faced by the most vulnerable regions, emphasizing the importance of context-specific strategies to enhance resilience against the growing threat of climate-induced droughts. Full article

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Significant trends and disparities in disaster occurrences and their widespread consequences are revealed through the utilization of the comprehensive international EM-DAT database. The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269). The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America. Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent’s disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the countries most frequently affected by both types of disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010–2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Pearson’s correlation analysis identified statistically significant links between socioeconomic factors and the effects of disasters. It shows that nations with higher GDP per capita and better governance quality tend to experience fewer disasters and less severe negative consequences. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies. Full article

This study investigates the impact of disasters on sustainability, resilience, and poverty, using data from the “Sustainable Development Report” and the Emergency Events Database (EM-DAT) from 2000 to 2023. Regression models assessed the effects of disasters, deaths, injuries, affected individuals, and economic damage on normalized values of the dependent variables with lag periods of one, two, and three years of independent variables. The results reveal that disasters consistently negatively impact sustainability and resilience, highlighting the need for robust disaster risk reduction strategies and resilient infrastructure. Higher mortality rates significantly hindered development, emphasizing the importance of improving early warning systems, emergency preparedness, and healthcare infrastructure. While injuries and the number of affected individuals did not show significant associations, economic damage was positively associated with resilience, suggesting that financial losses might drive recovery investments. Additionally, disasters were found to exacerbate poverty levels over time with significant associations in the two and three-year lag models. This study also uncovered significant regional disparities with lower resilience, sustainability, and higher poverty levels in certain regions compared to others. Higher-income groups demonstrated better resilience and lower poverty levels. These findings underscore the necessity for targeted, region-specific strategies to enhance resilience, reduce poverty, and support sustainable development, leveraging post-disaster recovery phases for long-term improvement. Full article

Severe weather events can be a catalyst for intimate partner violence, particularly in agricultural settings. This research explores the association between weather and violence in parts of East Africa that rely on subsistence farming. We used IPUMS-DHS data from Uganda in 2006, Zimbabwe in 2010, and Mozambique in 2011 for intimate partner violence frequency and EM-DAT data to identify weather events by region in the year of and year prior to IPUMS-DHS data collection. This work is grounded in a conceptual framework that illustrates the mechanisms through which violence increases. We used logistic regression to estimate the odds of reporting violence in regions with severe weather events. The odds of reporting violence were 25% greater in regions with severe weather compared to regions without in Uganda (OR = 1.25, 95% CI: 1.11–1.41), 38% greater in Zimbabwe (OR = 1.38, 95% CI: 1.13–1.70), and 91% greater in Mozambique (OR = 1.91, 95% CI: 1.64–2.23). Our results add to the growing body of evidence showing that extreme weather can increase women’s and girls’ vulnerability to violence. Moreover, this analysis demonstrates that climate justice and intimate partner violence must be addressed together. Full article

As an important mode of transportation for the global trade, waterborne transportation has become a priority option for import and export trade due to its large load capacity and relatively low cost. Meanwhile, shipping safety has been highly valued. By collecting technological water traffic accident data from the EM-DAT database, the spatiotemporal distribution and evolution characteristics were investigated in Asia since 2000. The methods of gravity center and standard deviation ellipse analysis were utilized to determine the spatial and data-related characteristics of water traffic accidents. Temporally, the results indicated that accidents occurred most frequently during the seasons of autumn and winter, leading to a significant number of casualties. Spatially, both South-eastern Asia and Southern Asia emerged as regions with a high frequency of water traffic accidents, particularly along the borders of Singapore, Malaysia, Indonesia, and the Bay of Bengal region. In addition, the Daniel trend test and R/S analysis were conducted to demonstrate the evolution trend of accidents across various regions and seasons. The present study provides guidance for improving marine shipping safety, emergency resource management, and relevant policy formulation. Full article

This research undertakes a focused analysis of transportation disasters in Western Asia from 2003 to 2023. Utilizing a curated dataset from the EM-DAT database, we delve into the patterns and outcomes of these significant events, categorizing by modality such as air, rail, road, and water. The results highlight a concerning surge in mishaps between 2003 and 2010, followed by a welcome decline. Road-related incidents emerge as a dominant category, but a large portion (73.8%) remains ambiguously categorized as “Unknown”, underscoring potential data gaps or reporting inconsistencies. Turkey stands out, accounting for nearly 45% of all documented incidents, emphasizing its central role in the regional transportation disaster landscape. Advanced ANOVA analyses illustrate variations in fatality rates across years and countries, although differences in injury rates across disaster types did not exhibit statistical significance. The study underscores the importance of continuous safety enhancements, public awareness efforts, and regional cooperation. Ultimately, it underscores the pressing need for strengthened safety frameworks and the value of inter-regional collaboration to uplift transportation safety standards in Western Asia. Full article