In 2024, the economic losses due to natural disasters worldwide amounted to about *** billion U.S. dollars. Natural disasters occur as a result of natural processes on Earth. Many different types of natural disasters can occur, including floods, hurricanes, earthquakes, and tsunamis. Natural disasters in 2024 Tropical cyclones generated the highest amount of economic losses in 2024 with *** billion U.S. dollars worldwide. Hurricanes Helene and Milton were the most destructive events worldwide that year with over 100 billion U.S. dollars in economic losses. Flooding events ranked second in the costliest events in 2024, with flooding in Valencia, Spain, and South and Central China being the worst examples. Asia hardest hit by natural disasters A highly destructive force, Asia is one of the most susceptible regions to natural disasters. The repercussions of natural disasters are not only physical, but also economic. Costs may be high – depending on the severity – as areas affected by natural disasters might need to be rebuilt. Lower income countries are more likely to be affected by natural disasters for a multitude of reasons, including a lack of developed infrastructure, inadequate housing, and lack of back-resources.

In 2024, the direct economic loss that resulted from natural disasters in China was about *** billion yuan. That year, around ***** million hectares of agricultural land were affected by natural disasters in China.

In 2024, tropical cyclones caused the most damage in the United States. Such a type of storm, for instance, resulted in overall losses of ***** billion U.S. dollars. Meanwhile, wildfires, droughts, and heatwaves resulted in economic losses of $ **** billion U.S. dollars. Severe convective storms were the second most destructive natural disaster that year, with a loss of almost **** billion U.S. dollars. Impact of severe thunderstorms in the U.S. Severe thunderstorms pose a great risk to public safety and often result in fatalities. People can be harmed in many ways during a thunderstorm, such as being directly struck by lightning or hurt when a building collapses/tree falls. In 2019, ** people were killed as a result of severe thunderstorms. Lightning strikes alone caused ** deaths and *** injuries in that year. How much was paid out due to thunderstorms? The high risk of damage posed by thunderstorms means that insurance cover is an important tool in reducing the losses incurred. In 2020 alone, approximately ****** homeowner insurance claims were paid due to lightning losses.

In 2024, the global economic loss caused by tropical cyclones amounted to *** billion U.S. dollars, more than any other type of natural disaster that year. Flooding followed in second, at ** billion U.S. dollars. That same year, the total economic loss from all natural disasters globally reached *** billion U.S. dollars.

The hurricanes in the U.S. and Mexico in September and October incurred an economic loss of about 110 billion U.S. dollars, the most of any natural disaster event in 2024. Three of the ten most expensive catastrophes in that year were hurricanes. Weather, climate, water related disaster The disasters that caused mortality in large numbers include droughts, storms, floods, and extreme temperatures. Hurricanes alone generated 35 percent of the total economic losses among the leading disasters over these 50 years. The global cost of natural disaster losses was primarily financial losses. Low-income countries are more affected by natural disasters when compared to the richer countries in the world. American Hurricanes Floods were the most common weather-related disasters recorded, yet storms had the highest human and economic losses. As the most common cause of damage, storms are the only disaster for which the attribution component grows. As of 2023, Hurricane Katrina was by far the most destructive hurricane in the United States. Officials confirmed more than 1,800 deaths, estimated damages of about 200 billion U.S. dollars, the destruction of approximately 350,000 homes, and displaced almost a million individuals.

Dataset

This dataset was created by Soumik Nayak00

Released under CC0: Public Domain

Contents

Floods that hit Thailand between June and December 2011 were the most expensive flood disaster recorded since 1900, with economic losses surpassing ** billion U.S. dollars. Three of the ** costliest floods in recent history all happened since 2020. China was the country most hit by economic damage in the past century, registering *** of the top 10 floods in terms of economic loss.

The earthquake and subsequent tsunami in Japan in 2011 was the costliest natural disaster since 1900, with losses reaching 235 billion U.S. dollars. The tsunami hit the nuclear plant at Fukushima, causing a nuclear disaster in the area. Hurricane Katrina, which hit the Gulf Coast of the United States in 2005, and Hurricane Harvey, which hit the North American country in 2017, tied with the second-largest economic losses in the period, each with 125 billion U.S. dollars.

This report analyses the economic losses (financial) of damage caused by natural disasters in the United Kingdom. There is no complete database with regards to total financial loss due to natural disasters (e.g. the total value of insurance claims related to damage caused by a natural disaster in the United Kingdom, the estimated cost of replacing and repairing levelled or damaged buildings and infrastructure, the estimated impact on business activity and value output). However, IBISWorld has collated and analysed related information and data provided by a number of sources – these include, albeit not limited to: the Centre for Research on the Epidemiology of Disasters (CRED); the Organisation for Economic Co-operation and Development (OECD); PreventionWeb; the UN Office for Disaster Risk Reduction (UNDRR); the Cabinet Office; the European Commission; the European Environment Agency (EEA); and the Inter-Agency Standing Committee Reference Group on Risk, Early Warning and Preparedness.

From 1970 to 2021, economic losses due to climate disasters were approximately 4.3 trillion U.S. dollars. At 38 percent, the economic losses caused by tropical cyclones were the highest during this period. Floods followed in close second, accounting for 32 percent of losses.

Economic losses from natural disasters vary by countries, and it has been hypothesized that institutional, political, and other national conditions and policies all play a role in determining the severity of loss. Many empirical studies for understanding the determinants of disaster losses, however, suffer from endogeneity and selection bias, which can potentially make their results method-dependent. To demonstrate, we investigate the relationship between disaster propensity, wealth, and economic loss from a panel data collected by [Neumayer et al., 2014]. We first demonstrate that the original data is subject to endogeneity and selection bias, reconstruct the dataset, and apply Heckman correction. The bias-corrected estimated impact of disaster propensity changes direction from the original result by [Neumayer et al., 2014] — countries that experience more frequent disasters tend to suffer from greater economic damage, holding everything else equal. We suggest that disaster propensity could be an indicator of vulnerability, or a sign of insufficient prevention and mitigation measures. Although we cannot provide any definitive explanation for the phenomenon, our result shows that correcting selection bias matters when dealing with natural disasters data. For future work, a more sophisticated construction of the latent propensity variable and the application of quantile regression for endogenous selection models could broaden our understanding.

Economic damage from natural hazards can sometimes be prevented and always mitigated. However, private individuals tend to underinvest in such measures due to problems of collective action, information asymmetry and myopic behavior. Governments, which can in principle correct these market failures, themselves face incentives to underinvest in costly disaster prevention policies and damage mitigation regulations. Yet, disaster damage varies greatly across countries. We argue that rational actors will invest more in trying to prevent and mitigate damage the larger a country’s propensity to experience frequent and strong natural hazards. Accordingly, economic loss from an actually occurring disaster will be smaller the larger a country’s disaster propensity – holding everything else equal, such as hazard magnitude, the country’s total wealth and per capita income. At the same time, damage is not entirely preventable and smaller losses tend to be random. Disaster propensity will therefore have a larger marginal effect on larger predicted damages than on smaller ones. We employ quantile regression analysis in a global sample to test these predictions, focusing on the three disaster types causing the vast majority of damage worldwide: earthquakes, floods and tropical cyclones.

Weather catastrophes caused economic losses of 329 billion U.S. dollars worldwide in 2021. Sudden cataclysmic disasters cause devastation on impact. Some weather and climate-related extreme events are storms, floods, heat waves, cold waves, droughts, and forest fires. Climate-related hazards pose risks to human health and can lead to substantial economic losses. Global natural disaster economic loss The economic damage caused by disasters varies based on geography and affects natural resources. Capital assets and infrastructure, along with the loss of life, disrupt the economic structure. In 2021, the economic loss due to natural disasters globally was about 343 billion U.S. dollars, and flooding generated the highest loss that year. Billion-dollar natural disaster events in the United States The United States experienced nearly two dozen billion-dollar disasters in 2021. At an economic loss of around 75 billion U.S. dollars, Hurricane Ida, a Category 4 storm that landed on the Louisiana coast in August, was the costliest.

In 2024, Hurricane Helene was by far the most significant natural disaster in the United States in terms of economic loss, with expenses totaling ** billion U.S. dollars. That year, the overall total of economic losses from natural disasters across the United States was estimated at around *** billion U.S. dollars.

IntroductionEscalating climate change has intensified storm surge disasters in China, whose economic repercussions are not confined to coastal areas but cascade nationwide through industrial supply chains. However, existing research overlooks these nationwide implications.MethodsTo address this gap, this paper proposes an innovative assessment framework for evaluating the economic footprint of storm surge disasters, quantifies the indirect economic losses inflicted by storm surge disasters in China from 2011 to 2020 and further trace the diffusion of these losses across various industries and regions by developing a disastrously-extended input-output model.ResultsThe findings reveal that indirect economic losses constituted over 60% of the total economic losses from storm surge disasters during the aforementioned period. Interestingly, regions remote from the direct impact of the storm surge disasters were not immune to their effects. Among these, Henan Province emerged as the inland area most severely impacted by storm surge disasters, while the northwest and southwest regions typically experienced minimal indirect economic losses. Furthermore, the majority of the indirect economic losses originated from the Resource Processing Industry and Service Department of the directly affected regions, and the secondary industry of the potentially affected regions.DiscussionThese findings demonstrate the inadequacy of localized disaster policies and underscore the urgent need for a nationwide resilience strategy focused on critical supply chain vulnerabilities.

Series Name: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)Series Code: VC_DSR_CILNRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Series Name: Direct economic loss attributed to disasters relative to GDP (percent)Series Code: VC_DSR_LSGPRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 11.5.2: Direct economic loss in relation to global GDP, damage to critical infrastructure and number of disruptions to basic services, attributed to disastersTarget 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situationsGoal 11: Make cities and human settlements inclusive, safe, resilient and sustainableFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Series Name: Direct agriculture loss attributed to disasters (current United States dollars)Series Code: VC_DSR_AGLHRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersGoal 1: End poverty in all its forms everywhereFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Graph and download economic data for Federal Government; Disaster Losses, Transactions (BOGZ1FA315404003Q) from Q4 1946 to Q2 2025 about disaster losses, transactions, federal, and USA.

This dataset provides the economic disaster risk assessment results of Tajikistan under different return periods, systematically reflecting the economic loss risk under various setting conditions including 10-year, 20-year, 50 year, 100 year return period, and expected scenarios under extreme precipitation background. Among them, "once every 10 years" indicates that extreme events of this intensity occur on average once every 10 years, with an annual probability of 10%, and so on. "Once every 100 years" indicates an annual probability of 1%. The expected scenario refers to the most acceptable risk state that the regional economic system can achieve under specific intervention measures. The data is presented in GeoTIFF raster format with a resolution of 1km, providing risk maps for two types of extreme precipitation indicators, namely R95PTOT (referring to the total precipitation with daily precipitation greater than the 95th percentile of the reference period) and RX5day (referring to the sum of the maximum continuous 5-day precipitation within the year). The field naming convention is as follows: "R95PTOT_10rpuer. tif" represents the economic risk map for the 10-year scenario under the R95PTOT indicator. This dataset integrates 2019 global high-resolution per capita GDP raster data, urbanization level classification data based on the 2020 GHSL (Global Human Settlement Layer) human settlement layer (7 levels in total), and landslide susceptibility probability maps constructed through multi-source environmental variables and random forest models. Among them, urbanization levels are divided according to human settlement density, and after reverse assignment and normalization, they are used to describe vulnerability indicators to reflect the sensitivity of regional economy to natural disasters; The landslide point data mainly comes from the surface landslide data provided by the World Bank, which is converted into central points for spatial modeling. The data covers a wide area but does not include specific occurrence times; After screening and cleaning, 2847 landslide points were shared for model training, and an equal proportion of non landslide points were generated for training validation; The modeling adopts the construction of landslide susceptibility layers through multi-source environmental variables and random forest models; Exposure is characterized by per capita GDP in 2019, with all factors aligned with a 1km pixel standard to ensure spatial accuracy and data consistency. The overall quality of the data is high, the modeling process is rigorous, the risk assessment system structure is reasonable, and it has strong logic and operability. This dataset can be widely applied to economic resilience analysis, disaster risk management, emergency resource allocation, insurance product design and pricing at the national or regional level, and is particularly suitable for supporting disaster reduction investment decisions and the implementation of sustainable development strategies.