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 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.

In 2023, there was a global protection gap of *** U.S. dollars for natural disasters worldwide. The estimated economic loss of natural disasters worldwide was *** billion U.S. dollars, while the estimated insured loss amounted to *** billion U.S. dollars.Where did the most costly natural disaster occur?Natural disasters are extreme, sudden catastrophes that are caused by natural processes by the earth. Different types of natural disasters include floods, hurricanes, tornadoes, earthquakes, and tsunamis. There are many consequences that occur as a result of natural disasters, which include death, economic and infrastructural damage, and public health issues. The 2011 earthquake and tsunami that happened in Japan caused the most economic damage worldwide in the past four decades. Most costly disasters for insurersThe impact of natural disasters on insurance companies varies depends on the prevalence of insurance coverage in the affected region. Generally, losses from natural disasters that occur in wealthy countries such as the United States include a greater percentage of insured losses than disasters that occur in lower income countries. 2017 remains the worst year for insured property losses in the United States due to several major hurricanes in the U.S. and the Caribbean. Domestically, Hurricane Katrina was the most expensive natural disaster of all time.

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.

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.

In 2024, the economic losses caused by natural disaster events in the Americas (excluding the United States) amounted to some ** billion U.S. dollars, nearly half the losses reported the previous year. The economic losses associated with widespread flooding in the southern region of Brazil that year totaled roughly *** billion U.S. dollars.

Little is known about the fiscal costs of natural disasters, especially regarding social safety nets that do not specifically target extreme weather events. This paper shows that US hurricanes lead to substantial increases in non-disaster government transfers, such as unemployment insurance and public medical payments, in affected counties in the decade after a hurricane. The present value of this increase significantly exceeds that of direct disaster aid. This implies, among other things, that the fiscal costs of natural disasters have been significantly underestimated and that victims in developed countries are better insured against them than previously thought.

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.

The percentage of respondents who report they experienced damage to their home or livestock as a result of natural disasters or severe weather events in the past three years The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

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.

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.

Series Name: Direct economic loss in the housing sector attributed to disasters (current United States dollars)Series Code: VC_DSR_HOLNRelease 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 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/

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.

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.

Series Name: Direct economic loss attributed to disasters relative to GDP (percent)Series Code: VC_DSR_LSGPRelease 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/

The Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational units to their respective national GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational unit. Once the national GDP has been spatially stratified into the smallest administrative units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data population distributions. A per capita contribution value is determined within each subnational unit, and then this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN). To provide a spatial surface of the total economic impacts of global earthquake hazard.

The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Flood Total Economic Loss Risk Deciles is a 2.5 minute grid of global flood total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origins. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data of population distributions. A per capita contribution value is determined within each subnational Unit, and this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by flood hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

The Global Multihazard Total Economic Loss Risk Deciles is a 2.5 minute grid of global multihazard total economic loss risks. First, for each of the considered hazards (cyclones, droughts, earthquakes, floods, landslides, and volcanoes), subnational distributions of Gross Domestic Product (GDP) are computed using a methodology developed from Sachs et al. (2003). Where applicable, the contributions of subnational Units to national GDP estimates, the contribution ratio, are determined using data of varied origin. World Bank Development Indicators are substituted for GDP estimates of varied origin and the subnational GDP is estimated using the fore mentioned contribution ratios. A subnational, per capita GDP is derived and a final GDP estimate per grid cell is made based on grid cell population density. A raw, total economic loss is computed per grid cell using a regional economic loss rate derived from EM-DAT records. To more accurately reflect the confidence surrounding the economic loss estimate, the range of losses are classified into deciles, 10 classes of an approximately equal number of grid cells. A multihazard index is generated by summing the top three deciles of the individual hazards. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

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/