write the complete components of a research report about natural disaster

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How To Write A Natural Disasters Research Report

From tsunamis to earthquakes, tornados to volcanos, landslides to avalanches, natural disasters happen. They teach us about geology and geography, weather and climate science. They are powerful and fascinating, whether they hit close to home or pop up in far away news.

Researching natural disasters give kids a chance to dig deeper into a topic of interest. They can learn about the research and report writing process and about a particular natural disaster.

Through this process, students

• Understand that reports are used to describe
• Use information texts to collect facts about natural disasters
• Write a report to describe a natural disaster.

Steps to writing a research report

Like any other writing, writing a research report is a process. Students need to learn how to conduct research and then how to put their findings together in a well organized report.

• Brainstorm topics and research questions

Start by brainstorming what you know about a topic. Then ask, what else would I like to know about this topic? This is a great group activity as one student’s idea may spark another. Students can use some of the wonderings or what else they would like to know to help focus their research.

•  Consult informational resources

Students can use informational texts such as nonfiction books and internet resources to gather information. Students may also draw from news accounts of natural disasters or talk to eye witnesses or experts. Students should learn about choosing appropriate sources in this phase. This includes understanding primary and secondary sources, and also looking at the age of the information and who produced it.

There are several skills students need for effective note taking. They need to understand what information is relevant to their report. For example, if they are writing about how hurricanes form, lots of details about the effects of historic hurricanes aren’t relevant. They should know how to record sources so that they can go back to a source for clarification or cite the source as needed. Perhaps most important, they need to understand paraphrasing, direct quoting, and plagiarism.

• Organize your ideas

An outline is appropriate for organizing ideas in a report. It may be a formal outline or a graphic organizer that helps get ideas in order. Once students have an outline, they can organize their notes based on the outline.

• Use notes to write report

Using their outline and notes, students now actually draft their report, including introduction, body paragraphs, and conclusion. While students may be used to writing from beginning to end, many writers find writing the body paragraphs first and then writing the introduction and conclusion a useful process.

• Edit report

Students can share reports with a partner or small group to get feedback on areas where ideas may need clarification or where readers might have questions. You can also use editing checklists to make sure all the pieces of the report are done, that students have checked for run-on sentences, spelling errors, punctuation, and the like.

• Share report and reflect on learning

Students can read reports or parts of them aloud. You could host a museum of natural disasters featuring the reports. You could create a library of natural disasters for students to read at some point during the day. In addition, have students reflect on their learning.

I’ve put together a unit on writing research reports about natural disasters that takes you from brainstorming through to reflection. This series of lessons introduces students to informational texts and how to use these texts to find information about natural disasters in order to write a report. This unit contains 5 individual activities and a final project s uitable for grades 4 – 7.

You can learn more and get your Natural Disasters Report Writing pack here.

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2021 Global Natural Disaster Assessment Report

• Govt. China

Attachments

Executive Summary

Compared to the average over the last 30 years (1991-2020), the total frequency of global natural disasters in 2021 was 13% higher, with 81% lower in deaths, 48% less in the affected population, and 82% more in direct economic losses. Global flood disasters in 2021 were the most frequent, 48% more than the historic levels, causing 4,393 deaths, which was more than the death toll from other natural disasters but 35% less than the historical average of flood-related deaths; the direct economic losses caused by storm disasters were the largest, reaching USD 137.7 billion, 133% more than the historical average; there were fewer strong earthquakes and their disaster losses were relatively small; the number of deaths from wildfires decreased, but the population affected rose by 219% and the direct economic losses were 109% higher than the historic levels. Regionally, Asia has seen the highest frequency of natural disasters in 2021, followed by North America; among all continents, Asia had the largest number of deaths due to disasters, followed by North America; North America has seen the highest economic losses due to disasters, followed by Europe; compared with developed countries, developing countries were more severely affected by natural disasters, mostly floods, storms, and extreme temperatures.

In 2021, deaths from natural disasters in China were at an above-average level in the world, basically consistent with the level of its economic development; the proportion of direct economic losses in GDP was at a lower-middle level, which was largely consistent with the level of its economic development. The flood losses in China were higher than those from other disasters and accounted for a large proportion of the global flood losses.

In 2021, China faced a complicated natural disaster situation , with extreme weather and climate events occurring frequently. The natural disasters mainly included flood, strong wind and hail, drought, typhoon, earthquake, geological disasters and cold wave, while sand and dust storm, forest and grassland fires and marine disasters also hit to varying degrees. On the whole, however, natural disaster situation in China was relatively moderate.

The report analyzes the characteristics of global extreme weather disasters from 2000 to 2021. During this period, annual direct economic losses from extreme disasters in Asia, America, Europe and Africa showed an increasing trend. The frequency of such disasters was far higher in Asia than on other continents, and the total losses in Asia from 2011 to 2021 were twice those of Asia from 2000 to 2010. The report also summarizes the characteristics of global climate, and the major weather and climate events in 2021, coupled with an analysis of the causes of typical major weather and climate events, including rainstorm-in- duced flood, drought, tropical cyclone, heat wave and wildfire, cold wave and severe convection. The report calls for greater world attention to tackling increasingly frequent extreme weather and climate events, and boosting collaborative research toward that end.

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• Library of Congress
• Research Guides
• Science & Technology

Natural Disasters: A Resource Guide

Introduction.

• Avalanches and Landslides
• Earthquakes
• Avalanches, Landslides, and Mudslides
• Historical Events
• Databases and Journal Articles
• Internet Resources
• Disaster Data
• Using the Library of Congress

Author: Nathan Smith, Reference and Research Specialist, Science, Technology & Business Division

Created: April 14, 2020.

Last Updated:  June 29, 2023.

Science & Technical Reports : Ask a Librarian

Have a question? Need assistance? Use our online form to ask a librarian for help.

Get connected to the Library’s large and diverse collections related to science, technology, and business through our Inside Adams Blog. This blog also features upcoming events and collection displays, classes and orientations, new research guides, and more.

Hurricanes. Tornadoes. Earthquakes. Floods. Avalanches. Wild fires. These events have been happening for millennia and have affected humans throughout every part of the globe. According to the International Journal of Disaster Risk and Reduction External , natural disasters are defined as "...catastrophic events with atmospheric, geological, and hydrological origins (e.g., droughts, earthquakes, floods, hurricanes, landslides) that can cause fatalities, property damage and social environmental disruption."

Arguably the most famous earthquake in U.S. history is that of San Francisco in 1906. It occurred on April 18 and had an estimated magnitude of 7.9 (estimated because the Richter Scale, which is used to measure the magnitude of earthquakes, was not invented until 1935 by Charles F. Richter). Though it lasted less than a minute, the damage was extensive and the death toll, though uncertain, was up to 3,000. The earthquake and subsequent fires caused by ruptured gas mains, which lasted for four days, destroyed about 80% of city. The earthquake, one of many for this region, occurred due to the tectonic activity along the San Andreas Fault, which forms the boundary between the Pacific and North American plates.

In 1900, a hurricane made landfall near Galveston, Texas. Not only was it the deadliest hurricane in U.S. history, but it was the deadliest natural disaster in U.S. history! This hurricane made landfall on the night of September 8 and was estimated as a category 4 with a storm surge of over 15 feet that devastated the city. There has been speculation on the total number of fatalities, but the most cited number is 8,000, which is a significant portion of the nearly 38,000 in total population at the time.

The 2019-2020 bushfire season in Australia was the worst on record; 46 million acres were burned by hundreds of fires which caused dozens of fatalities. The fires' effect on air quality was demonstrated by the Air Quality Index putting several parts of the country into the hazardous zone, including areas around Sydney. The toll of the fires could also be seen in the wildlife populations. It is estimated that over 1 billion animals died during the course of the fires, including many endangered species.

The resources in this guide provide information on how and why these events occur and what people can do to better prepare for the next occurrence.

About the Science Section

Part of the  Science & Business Reading Room  at the Library of Congress, the Science Section is the starting point for conducting research at the Library of Congress in the subject areas of science, medicine and engineering. Here, reference specialists in specific subject areas of science and engineering  assist patrons in formulating search strategies and gaining access to the information and materials contained in the Library's rich collections of science, medicine, and engineering materials.

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A review of risk analysis methods for natural disasters

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• Published: 21 December 2019
• Volume 100 , pages 571–593, ( 2020 )

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• Ruiling Sun 1 , 2 ,
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Between 1998 and 2017, 1.3 million people were killed and another 4.4 billion were left injured, homeless, displaced, or in need of emergency assistance due to climate-related and geophysical disasters. A risk analysis of natural disasters is helpful not only for disaster prevention and reduction, but also in reducing economic and social losses. Currently, there are many methods for natural disaster risk analysis. Based on the uncertainty, unfavorable and future characteristics of natural disaster risk, this paper summarizes the methods for disaster risk analysis based on the scope of application, research results, and focus; it also clarifies the advantages and disadvantages of various methods, as well as the scope of application, to provide a reference for selecting and optimizing methods for future disaster risk analysis.

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Global Standards for Disaster Risk Reduction

Scenario-based risk framework selection and assessment model development for natural disasters: a case study of typhoon storm surges.

Exposure to natural hazard events unassociated with policy change for improved disaster risk reduction

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• Artificial Intelligence

Ahmad N, Hussain M, Riaz N, Subhani F, Haider S, Alamgir KS, Shinwari F (2013) Flood prediction and disaster risk analysis using GIS based wireless sensor networks, a review. J Basic Appl Sci Res 3(8):632–643

Google Scholar  

Ai NS (1987) Comentropy in erosional drainage-system. Acta Conservat Soli et Aquae Sinica 1(2):1–8

Akgün İ, Gümüşbuğa F, Tansel B (2014) Risk based facility location by using fault tree analysis in disaster management. Omega 52:168–179

Article   Google Scholar  

Almeida S, Holcombe EA, Pianosi F, Wagener T (2017) Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change. Nat Hazards Earth Syst Sci 17(2):225–241

Apel H, Thieken AH, Merz B, Blöschl G (2004) Flood risk assessment and associated uncertainty. Nat Hazards Earth Syst Sci 4(2):295–308

Bouwer LM, Bubeck P, Aerts JCJH (2010) Changes in future flood risk due to climate and development in a Dutch polder area. Glob Environ Change 20:463–471

Büchele B, Kreibich H, Kron A, Thieken A, Ihringer J, Oberle P, Merz B, Nestmann F (2006) Flood-risk mapping: contributions towards an enhanced assessment of extreme events and associated risks. Nat Hazards Earth Syst Sci 6(4):485–503

Chen YM, Fan KS, Chen LC (2010) Requirements and functional analysis of a multi-hazard disaster-risk analysis system. Hum Ecol Risk Assess Int J 16(2):413–428

Chen L, Huang YC, Bai RZ, Chen A (2017) Regional disaster risk evaluation of China based on the universal risk model. Nat Hazards 89(2):647–660

CRED (2018) Natural disaster in 2017. CRED, Brussels, pp 162–170

Criado M, Martínez-Graña A, Román JSS, Santos-Francés F (2019) Flood risk evaluation in urban spaces: the study case of Tormes River (Salamanca, Spain). Int J Environ Res Public Health https://doi.org/10.3390/ijerph16010005

Davidson RA, Lambert KB (2001) Comparing the hurricane disaster risk of US coastal counties. Nat Hazards Rev 2:132–142

Davidson RA, Shah HC (1997) An urban earthquake disaster risk index. Stanford University: John A. Blume Earthquake Engineering Center

De Almeida LQ, Welle T, Birkmann J (2016) Disaster risk indicators in Brazil: a proposal based on the world risk index. Int J Disast Risk Re 17:251–272

Deng JL (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294

Deng G, Wang AS, Li SK, Zhou YS (2001) Risk theory and method and its initial application in grain yield. J Nat Resour 16(3):221–226

Deng G, Chen HL, Zhou YS (2006) Application of ensemble forecast technique to rainstorm risk analysis. J Nat Disasters 15(1):115–122

Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M (2005) Natural disaster hotspots: a global risk analysis, The World Bank, Washington

Ding Y, Shi PJ (2002) Fuzzy risk assessment model of typhoon hazard. J Nat Disasters 11(1):34–43

Ding JX, Yang ZF, Shang YJ, Niu YQ, Liu Y (2006) Quantitative risk analysis of regional debris flow hazards. Rock Soil Mech 27(7):1071–1076

Domeneghetti A, Carisi F, Castellarin A, Brath A (2015) Evolution of flood risk over large areas: quantitative assessment for the Po River. J Hydrol 527:809–823

Donner W, Rodríguez H (2008) Population composition, migration and inequality: the influence of demographic changes on disaster risk and vulnerability. Soc Forces 87(2):1089–1114

Du YY, Peng J, Zhao SQ, Hu ZC, Wang YL (2016) Ecological risk assessment of landslide disasters in mountainous areas of southwest China: a case study in Dali Bai autonomous prefecture. Acta Geogr Sin 71(9):1544–1561

Eldeen MT (1980) Predisaster physical planning: integration of disaster risk analysis into physical planning-a case study in Tunisia. Disasters 4(2):211–222

Fang GH, Gao YQ (2009) Construction of evaluation index system and evaluation model of flood disaster risk. Advances in water resources and hydraulic engineering. Springer, Berlin, pp 130–135

Chapter   Google Scholar  

Fuchs S (2009) Susceptibility versus resilience to mountain hazards in Austria-paradigms of vulnerability revisited. Nat Hazards Earth Syst Sci 9(2):337–352

Fuchs S, Heiss K, Hübl J (2007) Towards an empirical vulnerability function for use in debris flow risk assessment. Nat Hazards Earth Syst Sci 7(5):495–506

Gao J (2016) Analysis and assessment of the risk of snow and freezing disaster in China. Int J Disast Risk Reduct 19:334–340

Giannakidou C, Diakoulaki D, Memos CD (2019) Implementing a flood vulnerability index in urban coastal areas with industrial activity. Nat Hazards. https://doi.org/10.1007/s11069-019-03629-w

Gissing A, Blong R (2004) Accounting for variability in commercial flood damage estimation. Aust Geogr 35(2):209–222

Godfrey A, Ciurean RL, Van Westen CJ, Kingma NC, Glade T (2015) Assessing vulnerability of buildings to hydro-meteorological hazards using an expert based approach: an application in Nehoiu Valley, Romania. Int J Disast Risk Reduct 13:229–241

Gong ZW, Forrest JYL (2013) Special issue on meteorological disaster risk analysis and assessment: on basis of grey systems theory. Nat Hazards 71(2):995–1000

Goyal MK, Sharma A (2016) A fuzzy c-means approach regionalization for analysis of meteorological drought homogeneous regions in western India. Nat Hazards 84(3):1831–1847

Gu HB, Ma XL (2016) Agricultural drought vulnerability and grey correlation analysis of rural poverty. Arid Land Geogr 39(6):1358–1364

Guikema SD (2009) Natural disaster risk analysis for critical infrastructure systems: an approach based on statistical learning theory. Reliab Eng Syst Safe 94(4):855–860

Guo EL, Zhang JQ, Ren XH, Zhang Q, Sun ZY (2014) Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province China. Nat Hazards 74(2):947–965

Hao L, Zhang XY, Liu SD (2011) Risk assessment to China’s agricultural drought disaster in county unit. Nat Hazards 61(2):785–801

Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S (2013) Global flood risk under climate change. Nat Clim Change 3(9):816–821

Hohl R, Schiesser HH, Aller D (2002a) Hailfall: the relationship between radar-derived hail kinetic energy and hail damage to buildings. Atmos Res 63:177–207

Hohl R, Schiesser HH, Knepper I (2002b) The use of weather radars to estimate hail damage to automobiles-an exploratory study in Switzerland. Atmos Res 61(3):215–238

Hong H, Shahabi H, Shirzadi A, Chen W, Chapi K, Ahmad BB, Roodposhti MS, Hesar AY, Tian YY, Bui DT (2019) Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Nat Hazards 96(1):173–212

Horita FEA, De Albuquerque JP, Marchezini V, Mendiondo EM (2017) Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in Brazil. Decis Support Syst 97:12–22

Hu GH, Xia J (2001) Grey-stochastic risk method for risk analysis. J Hydraul Eng-Asce 4:1–6

Huang CF (2009) A discussion on basic definition of natural disaster. J Nat Disasters 18(5):41–50

Huang CF (2011) Discussion on basic methods of risk analysis. J Nat Disasters 20(5):1–10

Huang M, Jin S (2019) A methodology for simple 2-D inundation analysis in urban area using SWMM and GIS. Nat Hazards. https://doi.org/10.1007/s11069-019-03623-2

Huang CF, Shi PJ, Zhang YM (1994) One level model of risk evaluation on city natural disaster. J Nat Disasters 3(1):3–8

Huang CF, Liu XL, Zhou GX, Li XJ (1998) Agricultural natural disaster risk assessment method according to the historic disaster data. J Nat Disasters 7(2):1–9

Huang CF, Liu AL, Wang Y (2010) Discussion on basic definition of disaster risk. J Nat Disasters 19(6):7–15

Huang CF, Guo J, Ai FL, Wu W (2013) The basic paradigm of flood disaster risk analysis and its application. J Nat Disasters 22(4):11–23

ISDR (2004) Living with risk: a global review of disaster reduction initiatives (2004 Version—Volume II Annexes). United Nations: BioMed Central Ltd, Geneva

Islam MS, Swapan MSH, Haque SM (2013) Disaster risk index: how far should it take account of local attributes? Int J Disast Risk Re 3:76–87

Ji YW, Li C, Gao S, Chen JP, Zheng MM (2018) Risk assessment of geological hazard of Hancheng city in Shaanxi Province. J Catastrophol 33(3):194–200

Jülich S (2014) Development of a composite index with quantitative indicators for drought disaster risk analysis at the micro level. Hum Ecol Risk Assess Int J 21(1):37–66

Kawasaki A, Yamamoto A, Koudelova P, Acierto R, Nemoto T, Kitsuregawa M, Koike T (2017) Data integration and analysis system (DIAS) contributing to climate change analysis and disaster risk reduction. Data Sci J 16:41

Koks EE, Bočkarjova M, De Moel H, Aerts JCJH (2015) Integrated direct and indirect flood risk modeling: development and sensitivity analysis. Risk Anal 35(5):882–900

Kostyuchenko YV (2015) Geostatistics and remote sensing for extremes forecasting and disaster risk multiscale analysis. In: Numerical methods for reliability and safety assessment, pp 439–458

Krzysztofowicz R (1998) Probabilistic hydrometeorological forecasts: toward a new era in operational forecasting. B Am Meteorol Soc 79(2):243–252

Kumar B, Bhaduri S (2018) Disaster risk in the urban villages of Delhi. Int J Disast Risk Reduct 31:1309–1325

Li H, Zhang PY, Cheng YQ (2008) Concepts and assessment methods of vulnerability. Progress Geogr 27(2):18–25

Li LF, Wang JF, Leung H, Jiang CS (2010) Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial data. Risk Anal 30(7):1157–1175

Li XW, Du JG, Long HY, Sun GL (2018) Characteristics and risk analysis of hydrological disaster events from 1949 to 2015 in Urumqi. Theor Appl Climatol China https://doi.org/10.1007/s00704-018-2630-2

Liu XL, Shang ZH (2014) Risk analysis methods of natural disasters and their applicability. Progress Geogr 33(11):1486–1497

Liu SF, Yang YJ (2015) Advances in grey system research (2004–2014). J Nanjing Univ Aeronaut Astronaut 47(1):1–18

Liu H, Zhang DL (2012) Analysis and prediction of hazard risks caused by tropical cyclones in Southern China with fuzzy mathematical and grey models. Appl Math Model 36(2):626–637

Liu XP, Zhang JQ, Cai WY, Tong ZJ (2010a) Information diffusion-based spatio-temporal risk analysis of grassland fire disaster in northern China. Knowl-Based Syst 23(1):53–60

Liu YB, Liu LM, Xu D, Zhang SH (2010b) Risk assessment of flood and drought in major Grain-Producing areas based on information diffusion theory. Trans Chin Soc Agric Eng 26(8):1–7

Liu YL, Chen ZL, Wang J, Hu BB, Ye MW, Xu SY (2012) Large-scale natural disaster risk scenario analysis: a case study of Wenzhou City China. Nat Hazards 60(3):1287–1298

Liu SF, Cai H, Yang YJ, Cao Y (2013) Advances in grey incidence analysis modelling. Syst Eng Theory Pract 33(8):2041–2046

Liu YL, Feng GR, Xue Y, Zhang HM, Wang RG (2015) Small-scale natural disaster risk scenario analysis: a case study from the town of Shuitou, Pingyang County, Wenzhou China. Nat Hazards 75(3):2167–2183

Lu C, Zhou J, He Z, Yuan S (2018) Evaluating typical flood risks in Yangtze River economic belt: application of a flood risk mapping framework. Nat Hazards 94(3):1187–1210

Luo D, Mao W, Sun H (2017) Risk assessment and analysis of ice disaster in Ning-Meng reach of Yellow River based on a two-phased intelligent model under grey information environment. Nat Hazards 88(1):591–610

Luo RH, Huang JL, Tang ZZ (2018) Study on freshet disaster risk zonation at small mountainous watershed base on AHP and GIS. J Catastrophol 33(2):64–69

Ma BC (2015) Definition and expression methods for natural disaster risk. J Catastrophol 30(3):016–020

Ma LY, Xu XG (2010) Ecological risk assessment of Qingdao based on meteorological disasters. Urban Environ Urban Ecol 23(4):1–5

Mao XY, Meng JJ, Kang YF (2012) Natural disasters risk assessment based on information diffusion model: application and improvement. Acta Scientiarum Naturalium Universitatis Pekinensis 48(3):513–518

Michel-Kerjan E, Hochrainer-Stigler S, Kunreuther H, Linnerooth-Bayer J, Mechler R, Muir-Wood R, Ranger N, Vaziri P, Young M (2013) Catastrophe risk models for evaluating disaster risk reduction investments in developing countries. Risk Anal 33(6):984–999

Misra KB, Weber GG (1990) Use of fuzzy set theory for level-I studies in probabilistic risk assessment. Fuzzy Set Syst 37(2):139–160

Muis S, Güneralp B, Jongman B, Aerts JCJH, Ward PJ (2015) Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data. Sci Total Environ 538:445–457

Ni CJ, Wang J (2012) Further discussion on the definition of natural disaster risk. J Catastrophol 27(3):1–5

Ning X, Xueqin L, Shuai Y, Yuxian M, Wenqi S, Weibin C (2018) Sea ice disaster risk assessment index system based on the life cycle of marine engineering. Nat Hazards 95(3):445–462

Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district India. Nat Hazards 84(3):1905–1920

Papadrakakis M, Papadopoulos V, Lagaros ND, Oliver J, Huespe AE, Sanchez P (2008) Vulnerability analysis of large concrete dams using the continuum strong discontinuity approach and neural networks. Struct Saf 30(3):217–235

Peduzzi P, Dao H, Herold C, Mouton F (2009) Assessing global exposure and vulnerability towards natural hazards-the Disaster Risk Index. Nat Hazards Earth Syst Sci 9:1149–1159

Pelling M, Maskrey A, Ruiz P, Hall P, Peduzzi P, Dao QH, Kluser S (2004) Reducing disaster risk a challenge for development. Vol. 32. United Nations, New York

Penning-Rowsell EC, Chatterton JB (1977) The benefits of flood alleviation: a manual of assessment techniques. Gower Technical Press, Aldershot

Qi P, Li MJ, Hou YJ (2010) Risk assessment of storm surge disaster in the coastal areas of bohai sea and yellow sea based on information diffusion principle. Oceanologia et Limnologia Sinica 41(4):628–632

Qin NX, Jiang T (2005) GIS-based risk zoning and assessment of flood disaster in the middle and lower reaches of the Yangtze river basin. J Nat Disasters 14(5):1–7

Quan R (2014) Risk assessment of flood disaster in Shanghai based on spatial–temporal characteristics analysis from 251 to 2000. Environ Earth Sci 72(11):4627–4638

Rahman MT, Aldosary AS, Nahiduzzaman KM, Reza I (2016) Vulnerability of flash flooding in Riyadh Saudi Arabia. Nat Hazards 84(3):1807–1830

Ranger N, Hallegatte S, Bhattacharya S, Bachu M, Priya S, Dhore K, Rafique F, Mathur P, Naville N, Henriet F, Herweijer C, Pohit S, Corfee-Morlot J (2010) An assessment of the potential impact of climate change on flood risk in Mumbai. Clim Change 104(1):139–167

Rong QB, Zhang HJ, Wang FS (2012) Entropy-based set pair analysis model on geological disaster risk for military engineering. Engineering 04(02):76–82

Shannon CE (2001) A mathematical theory of communication. Acm Sigmobile Mobile Comput Commun Rev 5(1):3–55

Shi PJ (2011) Atlas of natural disaster risk of China. Science Press, Beijing

Shih BJ, Chang CH (2006) Damage survey of water supply systems and fragility curve of PVC water pipelines in the Chi-Chi Taiwan earthquake. Nat Hazards 37:71–85

Singhal A, Kiremidjian AS (1996) Method for probabilistic evaluation of seismic structural damage. J Struct Eng 122(12):1459–1467

Singhal A, Kiremidjian AS (1998) Bayesian updating of fragilities with application to RC frames. J Struct Eng 124(8):922–929

Skakun S, Kussul N, Shelestov A, Kussul O (2014) Flood hazard and flood risk assessment using a time series of satellite images: a vase study in Namibia. Risk Anal 34(8):1521–1537

Smith K (2003) Environmental hazards: assessing risk and reducing disaster. Routledge, London, 8–9

Sui YL, Qu ZF (2014) Probability and statistics. Tsinghua University Press, Beijing

Sultana S, Chen Z (2007) Modeling of flood-related interdependencies among critical infrastructures. Springer, Berlin

Book   Google Scholar  

Sultana S, Chen Z (2009) Modeling flood induced interdependencies among hydroelectricity generating infrastructures. J Environ Manage 90(11):3272–3282

Tamura H, Yamamoto K, Tomiyama S, Hatono I (2000) Modeling and analysis of decision making problem for mitigating natural disaster risks. Eur J Oper Res 122(2):461–468

Tarbotton C, Dall'osso F, Dominey-Howes D, Goff J (2015) The use of empirical vulnerability functions to assess the response of buildings to tsunami impact: comparative review and summary of best practice. Earth-Sci Rev 142:120–134

Trigg MA, Birch CE, Neal JC, Bates PD, Smith A, Sampson CC, Yamazaki D, Hirabayashi Y, Pappenberger F, Dutra E, Ward PJ, Winsemius HC, Salamon P, Dottori F, Rudari R, Kappes MS, Simpson AL, Hadzilacos G, Fewtrell TJ (2016) The credibility challenge for global fluvial flood risk analysis. Environ Res Lett 11(9):094014

Tsai CH, Chen CW (2011) The establishment of a rapid natural disaster risk assessment model for the tourism industry. Tourism Manag 32(1):158–171

Van Aalst MK (2006) The impacts of climate change on the risk of natural disasters. Disasters 30(1):5–18

Velasquez CA, Cardona OD, Mora MG, Yamin LE, Carreno ML, Barbat AH, (2014) Hybrid loss exceedance curve (HLEC) for disaster risk assessment. Nat Hazards 72(2):455–479

Wallemacq P (2018) Economic losses, poverty and disasters: 1998–2017. Centre for Research on the Epidemiology of Disasters, CRED

Wang D, Pan SM, Wu JC, Zhu QP (2006) Review of risk analysis on flood. J Nat Disasters 15(1):103–109

Wang J, Chen ZL, Xu SY, Hu BB (2013) Medium-scale natural disaster risk scenario analysis: a case study of Pingyang County, Wenzhou China. Nat Hazards 66(2):1205–1220

Ward PJ, De Moel H, Aerts JCJH (2011) How are flood risk estimates affected by the choice of return-periods? Nat Hazards Earth Syst Sci 11(12):3181–3195

Wu JD, Fu Y, Zhang J, Li N (2014) Meteorological disaster trend analysis in China: 1949–2013. J Nat Resour 29(9):1520–1530

Wu CG, Wei YM, Jin JL, Huang Q, Zhou YL, Liu L (2015) Comprehensive evaluation of ice disaster risk of the Ningxia-Inner Mongolia Reach in the upper Yellow River. Nat Hazards 75(S2):179–197

Wu L, Li CS, Wan L, Sun CF (2016) Risk analysis of spring low temperature disasters in southern early rice based on information diffusion method. In: 34th annual meeting of chinese meteorological society s12 promote the level of meteorological science and technology, and protect the agricultural disaster reduction and efficiency, pp 454–459

Wu JD, Li Y, Li N (2018) Development of an asset value map for disaster risk assessment in China by spatial disaggregation using ancillary remote sensing Data. Risk Anal 38(1):17–30

Xie YJ, Yi SZ, Cao Y (2011) Research on multi- source geographic information fusion method in flood risk analysis. Water Resour Power 29(9):157–160

Xie NM, Xin JH, Liu SF (2013) China’s regional meteorological disaster loss analysis and evaluation based on grey cluster model. Nat Hazards 71(2):1067–1089

Xu SY, Wang J, Shi C, Yan JP (2006) Research on natural disaster risk on coastal cities. Acta Geogr Sin 61(2):127–138

Xu K, Xu XY, Li AH, Yang DW (2013) Assessing agricultural drought disaster risk in Chengde city using stochastic method. Trans Chin Soc Agric Eng 29(14):139–146

Yin ZE, Yin J, Xu SY, Wen JH (2011) Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging. J Geog Sci 21(2):274–284

Yin X, Hu Y, Zhou QQ, Li Z, Yang ZK, Wu YB (2017) The thunder disaster risk analysis and zoning of earthquake high incidence area in Yunnan Province. J Catastrophol 32(4):67–72

Yonson R, Noy I, Gaillard JC (2018) The measurement of disaster risk: an example from tropical cyclones in the Philippines. Rev Dev Econ 22(2):736–765

Yu HX, Qian R (2006) A scenario analysis method for interpreting uncertainty in future development. Future Dev 2:12–15

Yue Z, Lai MS (2006) Research progress abroad on scenario analysis. J Inf 25(7):59–60

Zadeh LA (1999) Fuzzy sets as a basis for a theory of possibility. Fuzzy Set Syst 100(1):9–34

Zhang JX, Li PR, Huang GQ, Zhang HO (2007) Risk assessment of swingeing storm surge disaster in coastal area of China induced by typhoon based on information diffusion method. Trop Geogr 27(1):11–14

Zhao SJ (2012) Experimental study of fan development process of viscous debris flow. J Catastrophol 27(2):1–18

Zhao SJ, Huang CF, Guo SJ (2012) Scenario-driven risk analysis of regional natural disasters. J Nat Disasters 21(1):9–17

Zhao LN, Liu Y, Bao HJ, Wang BY, Bai XM, Li XM, Li YT (2017) The probabilistic flood prediction based on implementation of the Schaake Shuffle Method over the Huaihe Basin. J Appl Meteorol Sci 28(5):544–554

Zhou Y, Wang JA (2012) A review on development of vulnerability curve of natural disaster. Adv Earth Sci 27(4):435–442

Zhu LF, Yin KL, Zhang L, Li M (2002) Risk analysis system of geo-hazards supported by GIS. J Yangtze River Sci Res Inst 19(5):42–45

Zhu Y, Lu WK, Yu LX, Zhu Y, Hu XQ (2017) Risk assessment of lightning disaster in Yuxi city Chinese. J Agric Resour Reg Plann 38(11):159–164

Zou Q, Zhou J, Zhou C, Guo J, Deng WP, Yang MQ, Liao L (2012a) Fuzzy risk analysis of flood disasters based on diffused-interior-outer-set model. Expert Syst Appl 39(6):6213–6220

Zou Q, Zhou JZ, Zhou C, Song LX, Guo J, Yang XL (2012b) Flood disaster risk analysis based on variable fuzzy set theory. Trans Chin Soc Agric Eng 28(5):126–132

Zuo QT, Wu ZN, Zhao W (2003) Uncertainties in water resources system and risk analysis method. Arid Area Geogr 26(2):116–121

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Acknowledgements

This research is partially supported by the National Natural Science Foundation of China (71971121, 71571104), NUIST-UoR International Research Institute, the Major Project Plan of Philosophy and Social Sciences Research in Jiangsu Universities (2018SJZDA038), the 2019 Jiangsu Province Policy Guidance Program (Soft Science Research) (BR2019064), and the Spanish Ministry of Economy and Competitiveness with FEDER funds (Grant number TIN2016-75850-R).

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Sun, R., Gao, G., Gong, Z. et al. A review of risk analysis methods for natural disasters. Nat Hazards 100 , 571–593 (2020). https://doi.org/10.1007/s11069-019-03826-7

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Valuing Human Impact of Natural Disasters: A Review of Methods

Aditi kharb.

1 Institute of Health and Society (IRSS), Universite Catholique de Louvain, 1200 Brussels, Belgium

Sandesh Bhandari

2 Department of Medicine, University of Oviedo, 3204 Oviedo, Spain

Maria Moitinho de Almeida

Rafael castro delgado, pedro arcos gonzález, sandy tubeuf.

3 Institute of Economic and Social Research (IRES/LIDAM), Universite Catholique de Louvain, 1200 Brussels, Belgium

Associated Data

Not applicable.

This paper provides a comprehensive set of methodologies that have been used in the literature to give a monetary value to the human impact in a natural disaster setting. Four databases were searched for relevant published and gray literature documents with a set of inclusion and exclusion criteria. Twenty-seven studies that quantified the value of a statistical life in a disaster setting or discussed methodologies of estimating value of life were included. Analysis highlighted the complexity and variability of methods and estimations of values of statistical life. No single method to estimate the value of a statistical life is universally agreed upon, although stated preference methods seem to be the preferred approach. The value of one life varies significantly ranging from USD 143,000 to 15 million. While an overwhelming majority of studies concern high-income countries, most disaster casualties are observed in low- and middle-income countries. Data on the human impact of disasters are usually available in disasters databases. However, lost lives are not traditionally translated into monetary terms. Therefore, the full financial cost of disasters has rarely been evaluated. More research is needed to utilize the value of life estimates in order to guide policymakers in preparedness and mitigation policies.

1. Introduction

Since 1960, more than 11,000 disasters triggered by natural hazards have been recorded. The number has steadily increased from an annual total of 33 disasters in 1960 to a peak of 441 disasters in 2000 [ 1 ]. Hazards such as storms, floods, heatwaves, droughts and wildfires have increased in number, intensity and variability in recent years [ 2 ]. Between 2000 and 2019, there were 510,837 deaths and 3.9 billion people affected by 6681 natural disasters [ 3 ]. This rising death rate highlights the continued vulnerability of communities to natural hazards, especially in low- and middle-income countries. The Analysis of Emergency Events Database(EM-DAT) shows that, on average, more than three times as many people died per disaster in low-income countries than in high-income nations [ 1 ]. A similar pattern was evident when low- and lower-middle-income countries were grouped together and compared to high- and upper-middle-income countries. Taken together, higher-income countries experienced 56% of disasters but lost 32% of lives, while lower-income countries experienced 44% of disasters but suffered 68% of deaths [ 1 ].

Disasters datasets usually report the human impact of disasters fairly precisely, and also include the economic impact mainly related to damages to insured goods; for example, EM-DAT, NatCatservice, MunichRe [ 1 , 4 ]. While economic damages of disasters are available in monetary terms, the human impact is measured in different natural units (lost lives, lost life years, disability-adjusted life years (DALY), etc.). Transforming those human impacts into monetary terms is not straightforward. However, it is of great importance in disaster contexts, as it could serve as a vital tool for a multitude of purposes, not limited to informing policy decision making.

Reinsurance companies could utilize this value to generate risk assessments, calibrate loss-estimation models and validate compensation claims; investors and international organizations could make use of it to advise strategic risk mitigation plans; and academic institutions could use it to measure inequalities and identify research gaps. Additionally, for individuals, the perceived disaster severity and knowledge of disaster-related risks might be limited and can be supplemented by providing monetary value to the physical and psychological health risks they might face [ 5 ]. Similarly, as the principal focus of health, safety and environmental regulations and many public health-related policies is to enhance individual health, where the most consequential impacts often pertain to reductions in mortality risks, policymakers seeking to assess society’s willingness to pay for expected health improvements need some measures of the associated benefit values to monetize the risk reductions and to facilitate comparison of benefits and costs. In this context, evaluating the global impact of a disaster would rely on using a unique metric to translate both the human and the economic costs of disasters.

Providing a monetary value to lost lives or health losses relies on the value of statistical life literature. The economics and disaster literature today has shown that although it is difficult to ‘put a price on life’, observation of individual and group behaviors seem to indicate otherwise. People regularly weigh risks and make decisions through a cost–benefit analysis framework, where they weigh the willingness to pay for risk reduction and the marginal cost of enhancing safety [ 6 , 7 ]. According to Kniesner and Viscusi (2019) [ 8 ], the value of statistical life can be defined as the local trade-off rate between fatality risk and money. The utility associated with reducing a risk must compensate for the disutility associated with the cost of reducing that risk. This argument is further strengthened by the cost assessment of intangible effects of natural disasters in the literature in welfare economics [ 9 , 10 ]. Individuals derive welfare from non-market goods such as environmental and health assets in more ways than only direct consumption [ 11 ]. For example, does the cost of reinforcing and strengthening buildings in a seismically active zone and ensure earthquake resistance save enough lives and prevent enough injuries that, in the long run, individual productivity for the state overshoot the costs exhausted by the state [ 12 ]?

This review aims to provide an overview of the methodologies used to evaluate the value of life in a natural disaster context and to present the differences in values of statistical life calculated using these alternative methodologies. The review also highlights the areas in the literature where more research is needed. To this end, the first section of this review reports the methodology for the selection and analysis of the literature. The second section explains the results of the analysis. Finally, we discuss the results and shortcomings of the current literature and draw conclusions from the study.

2. Methodology

We conducted a review of the literature reporting on the value of life in disasters adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 13 ]. The research question was formulated with the collaboration of the co-authors and the search strategy was then developed following extensive discussion.

2.1. Search Strategy

Several databases were used to search for literature, including PubMed MeSH, EMBASE and ECONLIT. In addition to this, the search was also performed in SCOPUS and Google Scholar so as not to miss any relevant papers, but only the first 200 results sorted by relevance were picked up from the two latter databases. We then screened the references of included full texts to identify any potential misses from our initial search strategy.

Various keywords synonymous to the two concepts “Value of Life” and “Disasters” were identified to undertake the search for literature. For “Value of life”, words and phrases such as cost of life, value of statistical life, VSL, willingness to pay, value of life lost and economic value of life were identified as relevant. Similarly, for “Disasters”, two additional terms, i.e., natural disasters and hazards, were used. The two concepts were searched separately as one and two, and then the combination of one and two was searched to obtain the results. More details about the search strategy are available in Appendix A .

2.2. Eligibility Criteria

The primary inclusion criteria for the search were peer-reviewed articles or gray literature such as conference papers, dissertation and discussion papers on disasters and value of life written in English from 2000 to 2020. We included studies that primarily quantified the value of life in a disaster setting and studies discussing methodology of estimating the value of life without providing a value by itself. No geographical limitations were set.

2.3. Data Collection and Analysis

The hits from different databases were exported onto Mendeley citation manager (Mendeley version 1.19.8) for subsequent screenings. Duplicates were excluded first. Titles and abstracts were then screened, and finally, full texts were screened for the papers included after abstract screening, excluding papers clearly outside the scope of this study. All uncertainties about eligibility were discussed between three co-authors (SB, MMA, ST) in all steps of the selection process.

Several papers were excluded in subsequent screening steps. Papers only talking about environmental pollution and climate change without a reference to natural disasters were excluded, as these topics are quite broad and, if not a cause for natural disasters, fall outside the scope of this study. Additionally, articles mainly concerned with terrorism, conflicts and landmines were not included in the final selection. Other categories of papers that were excluded were coal mine accidents, traffic accidents and forest fires. Papers solely talking about housing insurance and policy recommendations were also excluded. A total of five papers were requested directly from the authors as they could not be accessed online.

A data extraction form was developed for this review after consultation with the authors. The data extraction form recorded the descriptive aspect of all the studies included in the review, including methodology used to calculate the value of statistical life (VSL), results, strengths and limitations. This form was then pilot tested to ensure all the information was covered. The excluded studies were also tested against the form to check why they did not fit the form and revised as needed in subsequent steps. More details about the form are available in the Appendix A .

We first provided a descriptive overview of the included studies in terms of disaster types, the year in which studies were published, distribution of studies among countries according to the level of income as classified by the World Bank, simple geographical distribution and methodologies mentioned in the studies which were used to calculate the VSL. We then synthesized the information provided according to major predefined themes, such as methods of estimation of VSL, calculated VSL, and variations in VSL by geographical regions. These were identified before the analysis following discussions within the research team. Additionally, the possibility of emerging themes was considered and actively looked for during identification and processing of predefined themes.

3.1. Descriptive Overview of Included Studies

The initial search yielded a total of n = 2121 articles, coming down to n = 2084 after duplicates were removed. After screening titles and abstracts, n = 115 papers were considered for full text screening. Subsequently, a further n = 87 articles were excluded and two additional papers were excluded during the data extraction process. In addition to the remaining n = 26 papers for the review, one article was included from the reference screening, making the final count of papers for the review n = 27. The detailed process of article selection is presented in a PRISMA flow diagram ( Figure 1 ) [ 13 ].

PRISMA flow chart of search, inclusion and exclusion screening and accepted studies of the review. Source: Authors.

The biggest proportion of the included papers (n = 8, 29.6%) focused on value of life lost due to floods. This was closely followed by papers discussing unspecified disasters or disasters in general (n = 5, 18.5%). Five articles (18.5%) focused on earthquakes specifically, followed by three papers (11.1%) examining the value of life in the context of avalanches and rockfalls. Two articles (7.4%) discussed tornadoes and three papers (11.1%) dealt with a group of disasters consisting of four types of disasters, namely flood, drought, alpine and coastal hazards. One article (3.7%) was about heatwaves ( Figure 2 ).

Numbers of included studies by type of disaster. Source: Author.

Most studies (n = 16, 59%) concerned countries classified as high-income countries by the World Bank, including four papers (15%) from the United States of America (USA), three (11%) from the Netherlands and two each (7%) from Switzerland and Australia. Germany, Austria, Russia, Italy, New Zealand and Japan also had one article each in the final pool. Four studies (15%) were from upper-middle-income countries, including two studies from China and one each from Russia and Iran. Only one paper considered a lower-middle-income country, namely Vietnam. Four papers (15%) were not specific to any country and discussed the value of statistical life in general, without geographical consideration. Finally, one paper (3.7%) talked about developing countries in general while talking about value of life and reconstruction costs resulting from earthquakes.

Regarding where the articles were published, all but 4 out of 27 articles (85%) were published in peer-reviewed journals. As we included gray literature, two out of the four were discussion papers, one was a conference proceedings and the remaining one was a doctoral dissertation. The included studies were published in a variety of disaster-related, economics, policy and environmental journals.

3.2. Methods Used to Estimate Value of Life

A number of methods used to estimate the value of life were highlighted after reviewing the literature. Table 1 summarizes the different methods used in the included literature.

Value of statistical life estimation methods.

• (a) Revealed preference methods.

The revealed preference method utilizes observed behavior among the individuals that has already occurred and makes use of this to approximate suggested willingness to pay for a change in mortality risk. This method has an advantage over the stated preference approach in that if a person pays a certain amount for a commodity, it is known with conviction that the same person’s WTP for that commodity is at least the amount he/she is willing to pay. The four methods used to reveal preferences include: (a) the hedonic pricing method; (b) the travel cost method; (c) the cost of illness approach; (d) the replacement cost method [ 14 , 15 , 16 ].

• (b) Stated preference methods.

In contrast with revealed preference methods, the stated preferences method creates a hypothetical market in a survey. It parallels a market survey and estimates a willingness to pay for hypothetical reduction in mortality risks, since it resembles market behavior. In addition, stated preference methods incorporate both active and passive use of a commodity by the consumer. Direct or active values arise when an individual physically experiences the commodity, while passive or indirect values entail that an individual does not directly experience the commodity. The three methods used for stated preferences include: (a) the contingent valuation method; (b) the choice modeling method; (c) life satisfaction analysis [ 17 , 18 , 19 ].

• (c) Non-behavioral methods

Non-behavioral methods are not necessarily based on human choices and cognitive biases which affect the choices subconsciously. They include the human capital method (HCM) [ 20 ] and life quality index method (LQI) [ 21 ] to estimate the valuation of statistical life, and they are used to elicit the value of an individual in a society in the absence of a possibility to conduct a survey pre- or post- disaster.

In the selected literature, 7 papers out of 16 used stated preference methods. Within stated preference methods, two papers used choice modeling, while the other five used a contingent valuation method.

Papers using choice modeling method included Bockarjova et al. (2012) [ 22 ] and Rheinberger (2011) [ 23 ]. While Bockarjova et al. (2012) [ 22 ] carried out a choice modeling experiment via an internet-based questionnaire and elicited responses from people living in flood prone areas in the Netherlands in two separate studies, Rheinberger (2011) [ 23 ] undertook a choice experiment by recruiting respondents via a phone call prior to a mail survey.

For contingent valuation method, Leiter et al. (2010) [ 24 ] used face-to-face interviews and elicited people’s willingness to pay to prevent an increase in the risk of dying in a snow avalanche. Similarly, Hoffmann et al. (2017) [ 26 ] used a computerized payment card method to estimate the willingness to pay to reduce mortality risk in Chinese population living in four different cities in China. In contrast, Ozdemir (2011) [ 25 ] used a contingent valuation method as well, but used a mail survey to elicit willingness to pay to reduce the risks from tornadoes in the USA.

For non-behavioral methods, Dassanayake et al. (2012) [ 35 ] used a quality of life index method to evaluate intangible flood losses and integrate them into a flood risk analysis.

Other papers used one or a combination of methods. For example, Porfiriev (2014) [ 31 ] approached the economic valuation of human losses resulting from natural and technological disasters in Russia using the theory of welfare and an international comparative approach. Cropper and Sahin (2009) [ 12 ] used the comparative approach, along with transferring the VSL from USA to a whole list of countries classified by income groups by the OECD to estimate VSL.

3.3. Values Provided in the Literature

There was a wide range of VSL values in the literature, ranging from ISD 143,000 to 15 million for one life [ 12 , 25 ]. Table 2 summarizes the estimated value of statistical lives in the articles included in the review. Disaster types range from natural disasters to technological disasters with some disaster types appearing more often than others in the literature, with earthquakes and floods being the most common. The VSLs appeared to increase over the years: while it was estimated to be USD 0.81 million in 2005 in Switzerland in the context of avalanches [ 34 ], it was evaluated between USD 6.8 and 7.5 million in 2011 [ 23 ].

Estimated values of statistical life in included articles.

* Values were converted into United States Dollars (USD) in respective years. Source: Authors [ 37 ].

4. Discussion

Disasters are complex events, and the assessment of losses they have caused is a compounded task. This review’s exploration of literature estimating the value of statistical life with regard to disasters highlighted the complexity and variability of the estimation of values of statistical life and the methods involved.

The geographical locations of studies included in the review showed the parts of the world where most of the studies were focused. An overwhelming majority of studies estimated the value of statistical life in high-income countries. The main reasons for this are related to the data availability and the investment made by developed countries in research and development for the advancement of science in general [ 38 ]. Low- and middle-income countries often experience several disasters occurring year round, and become trapped in a loop of disaster recovery and management annually. Amid ever-present financial constraints, disaster risk reduction and management planning to deal with disasters and their impact in the country therefore becomes much more demanding [ 39 ].

The estimation of economic damages due to disaster in a low-resource setting can also be challenging. Not all the houses, agricultural land, crops and other assets are insured in low- and middle-income countries. The insurance coverage is relatively small if not non-existent in these countries [ 40 ] and the data to quantify the impacts of disasters, such as the number of deaths, missing, affected population as well as reconstruction costs, are often incomplete and not well recorded. So, the unavailability of appropriate information becomes a big challenge in the first step of conducting research. This might be the reason why low- and middle-income countries are not well represented in studies estimating the value of life in disasters. As a result, the lack of studies in low- and middle-income countries can lead to a certain degree of extrapolation of results found in VSL calculation in high-income-country-based studies.

Furthermore, we note that the majority of articles measuring the value of life were about floods. Floods are indeed the most common type of disasters. In an analysis of disasters recorded in the EM-DAT database from 2000 to 2019, nearly half (n = 3254) of all recorded events (n = 7348) were floods [ 41 ]. However, there are many other types of disasters, and it is important to rely on such studies where those disasters were considered when measuring the value of a statistical life.

Methods used for VSL estimations showed significant diversity among the articles included in this review. Although the stated preferences method is the most frequent, it is closely followed by the adaptation method. There could be various reasons for this difference in methodologies across the literature. For instance, non-marketed good with no complementary or substitute market good may not have readily available individual data, and hence may lead the researchers to undertake stated preference methods with which to elicit people’s willingness to pay to reduce a hypothetical disaster risk through surveys [ 19 ]. The scope of the study and the budgetary constraints may also explain why a researcher chooses one method over the other. Additionally, the characteristics of the survey participants are another important factor, as they influence the type of survey that can be conducted and the methodology adopted. For example, if the target population is old and poor, face-to-face interviews in respondents’ private homes might be more suitable than internet-based questionnaires [ 42 , 43 ].

There was a wide range of monetary values of the VSL in the literature. These differences could be due to the level of income of the country where the disaster occurred [ 40 ]. The method of calculation could be another reason for such differences, for example, as consumers optimize their lifetime utility, thus neglecting intergenerational (long-term) utility, using willingness to pay (WTP) methods for a reduction of risk can often lead to overestimated values [ 44 , 45 ]. It could also simply be due to the differences in cultural norms between countries [ 40 ]. Furthermore, the context and the aim of the research and its evolution over the years might also explain variations across the studies. Further studies are required to establish a concrete cause for this observation. It should also be highlighted that low VSL estimates in low-income countries do not inherently mean that a human life is worth less. It could simply reflect individual income, the cost of commodities and the value of currency [ 8 , 46 ].

This study presents a number of limitations. First, the review only included articles published in English, and some studies may exist in other languages. Second, papers that did estimate a VSL considered a range of different methods, and therefore direct comparison of estimated values was not straightforward. Papers referring to economic impact in terms of natural environment or animals were also excluded, as they do not refer to value of statistical life; however, they can be important for calculating overall economic cost of disasters [ 47 , 48 ].

5. Conclusions

This study aims to explore literature estimating the value of statistical life with regard to disasters through a systematic review. After applying the inclusion criteria on the 2121 articles found in the initial keywords search, only 27 articles were included for final review. In the included literature, several attempts at estimating the value of statistical lives in disasters were identified; however, there was no consensus on the method used, and few investigations were carried out in a low- and middle-income country context. This review therefore provides a limited view of the value of statistical life calculations in disaster settings, which may become useful when implementing disaster risk reduction policies and calculating global losses incurred due to disasters. It reveals that an agreed, robust and multi-sectoral approach for the disaster and economics community remains to be defined.

Appendix A. Search Strategy Description

For PubMed MeSH, terms such as sanctity of life, life sanctity, life sanctities, respect for life, economic life valuation, life valuation/s, economic valuation/s and economic life were used. In addition to this, the search was performed in SCOPUS and GOOGLE SCHOLAR.

The data extraction form recorded the descriptive aspect of all the studies included in the review, the including methodology used to calculate VSL, results, strengths and limitations. A total of 16 categories of information were extracted:

(1) Author, (2) Title, (3) Year published, (4) Journal, (5) Study location, (6) Aim of the study, (7) Disaster type, (8) Type of study (Theoretical/Empirical), (9) Study data source, (10) Study participants, (11) Method of VSL estimation, (12) VSL if given, (13) Strengths, (14) Limitations, (15) Relevant references and (16) Study design.

Funding Statement

We are grateful to the European Commission for providing the Erasmus Mundus Grant for completing the Erasmus Mundus Master Course in Public Health in Disasters (EMPHID). We also thank USAID/DCHA/OFDA [ref no. 72OFDA20CA00072] for funding the research at Centre for Research on the Epidemiology of Disasters at the Universite catholique de Louvain.

Author Contributions

A.K.: Formal analysis, investigation, writing—review & editing; S.B.: Formal analysis, investigation, writing—original draft; M.M.d.A.: Conceptualization, methodology, project administration, supervision, writing—original draft, writing—review & editing; R.C.D.: Funding acquisition, supervision, review & editing; P.A.G.: Funding acquisition, supervision, review & editing; S.T.: Conceptualization, funding acquisition, methodology, project administration, supervision, validation, writing—original draft, writing—review & editing. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Disaster Resilience: A National Imperative (2012)

Chapter: summary.

No person or place is immune from disasters or disaster-related losses. Infectious disease outbreaks, acts of terrorism, social unrest, or financial disasters in addition to natural hazards can all lead to large-scale consequences for the nation and its communities. Communities and the nation thus face difficult fiscal, social, cultural, and environmental choices about the best ways to ensure basic security and quality of life against hazards, deliberate attacks, and disasters. Beyond the unquantifiable costs of injury and loss of life from disasters, statistics for 2011 alone indicate economic damages from natural disasters in the United States exceeded $55 billion, with 14 events costing more than a billion dollars in damages each.

One way to reduce the impacts of disasters on the nation and its communities is to invest in enhancing resilience. As defined in this report, resilience is the ability to prepare and plan for, absorb, recover from, and more successfully adapt to adverse events . Enhanced resilience allows better anticipation of disasters and better planning to reduce disaster losses—rather than waiting for an event to occur and paying for it afterward.

However, building the culture and practice of disaster resilience is not simple or inexpensive. Decisions about how and when to invest in increasing resilience involve short- and long-term planning and investments of time and resources prior to an event. Although the resilience of individuals and communities may be readily recognized after a disaster, resilience is currently rarely acknowledged before a disaster takes place, making the “payoff” for resilience investments challenging for individuals, communities, the private sector, and all levels of government to demonstrate.

The challenge of increasing national resilience has been recognized by the federal government, including eight federal agencies and one community resilience group affiliated with a National Laboratory who asked the National Research Council (NRC) to address the broad issue of increasing the nation’s resilience to disasters. These agencies asked the NRC study committee to (1) define “national resilience” and frame the main issues related to increasing resilience in the United States; (2) provide goals, baseline conditions, or performance metrics for national resilience; (3) describe the state of knowledge about resilience to hazards and disasters; and (4) outline additional information, data, gaps, and/or obstacles that need to be addressed to increase the nation’s resilience to disasters. The committee was also asked for recommendations

about the necessary approaches to elevate national resilience to disasters in the United States.

This report confronts the topic of how to increase the nation’s resilience to disasters through a vision of the characteristics of a resilient nation in the year 2030. The characteristics describe a more resilient nation in which

•   Every individual and community in the nation has access to the risk and vulnerability information they need to make their communities more resilient.

•   All levels of government, communities, and the private sector have designed resilience strategies and operation plans based on this information.

•   Proactive investments and policy decisions have reduced loss of lives, costs, and socioeconomic impacts of future disasters.

•   Community coalitions are widely organized, recognized, and supported to provide essential services before and after disasters occur.

•   Recovery after disasters is rapid and the per capita federal cost of responding to disasters has been declining for a decade.

•   Nationwide, the public is universally safer, healthier, and better educated.

The alternative, the status quo, in which the nation’s approaches to increasing disaster resilience remain unchanged, is a future in which disasters will continue to be very costly in terms of injury; loss of lives, homes, and jobs; business interruption; and other damages.

Building resilience toward the 2030 future vision requires a paradigm shift and a new national “culture of disaster resilience” that includes components of

(1) Taking responsibility for disaster risk;

(2) Addressing the challenge of establishing the core value of resilience in communities, including the use of disaster loss data to foster long-term commitments to enhancing resilience;

(3) Developing and deploying tools or metrics for monitoring progress toward resilience;

(4) Building local, community capacity because decisions and the ultimate resilience of a community are driven from the bottom up;

(5) Understanding the landscape of government policies and practices to help communities increase resilience; and

(6) Identifying and communicating the roles and responsibilities of communities and all levels of government in building resilience.

A set of six actionable recommendations (see Box S-1 at the close of the Summary) are described that will help guide the nation toward increasing national resilience from the local community through to state and federal levels. The report has been informed by published information, the committee’s own

expertise, and importantly, by experiences shared by communities in New Orleans and the Mississippi Gulf Coast, Cedar Rapids and Iowa City, Iowa, and Southern California, where the committee held open meetings.

UNDERSTANDING, MANAGING, AND REDUCING DISASTER RISK

Understanding, managing, and reducing disaster risks provide a foundation for building resilience to disasters. Risk represents the potential for hazards to cause adverse effects on our life; health; economic well-being; social, environmental, and cultural assets; infrastructure; and the services expected from institutions and the environment. Risk management is a continuous process that identifies the hazard(s) facing a community, assesses the risk from these hazards, develops and implements risk strategies, reevaluates and reviews these strategies, and develops and adjusts risk policies. The choice of risk management strategies requires regular reevaluation in the context of new data and models on the hazards and risk facing a community, and changes in the socioeconomic and demographic characteristics of a community, as well as the community’s goals. Although some residual risk will always be present, risk management strategies can help build capacity for communities to become more resilient to disasters.

A variety of tools exist to manage disaster risk including tangible structural (construction-related) measures such as levees and dams, disaster-resistant construction, and well-enforced building codes, and nonstructural (nonconstruction-related) measures such as natural defenses, insurance, zoning ordinances, and economic incentives. Structural and nonstructural measures are complementary and can be used in conjunction with one another. Importantly, some tools or actions that can reduce short-term risk can potentially increase long-term risk, requiring careful evaluation of the risk management strategies employed. Risk management is at its foundation a community decision, and the risk management approach will be effective only if community members commit to use the risk management tools and measures made available to them.

THE CHALLENGE OF MAKING INVESTMENTS IN RESILIENCE

Demonstrating that community investments in resilience will yield measurable short- and long-term benefits that balance or exceed the costs is critical for sustained commitment to increasing resilience. The total value of a community’s assets—both the high-value structural assets and those with high social, cultural, and/or environmental value—call for a decision-making framework for disaster resilience that addresses both quantitative data and qualitative value assessments. Ownership of a community’s assets is also important; ownership establishes the responsibility for an asset and, therefore,

the need to make appropriate resilience investments to prepare and plan for hazards and risks. Presently, little guidance exists for communities to understand how to place meaningful value on all of their assets. Particularly during times of economic hardship, competing demand for many societally relevant resources (education, social services) can be a major barrier to making progress in building resilience in communities.

Accessing and understanding the historical spatial and temporal patterns of economic and human disaster losses in communities in the United States are ways for communities to understand the full extent of the impact of disasters and thereby motivate community efforts to increase resilience. Historical patterns of disaster losses provide some sense of the magnitude of the need to become more disaster resilient. The geographic patterns of disaster losses—e.g., human fatalities, property losses, and crop losses—illustrate where the impacts are the greatest, what challenges exist in responding to and recovering from disasters, and what factors drive exposure and vulnerability to hazards and disasters. Although existing loss databases in the United States are useful for certain kinds of analyses, improvement in measurements, accuracy, and consistency are needed. Furthermore, the nation lacks a national repository for all-hazard event and loss data, compromising the ability of communities to make informed decisions about where and how to prioritize their resilience investments.

MEASURING PROGRESS TOWARD RESILIENCE

Without some numerical means of assessing resilience it would be impossible to identify the priority needs for improvement, to monitor changes, to show that resilience had improved, or to compare the benefits of increasing resilience with the associated costs. The measurement of a concept such as resilience is difficult, requiring not only an agreed-upon metric, but also the data and algorithms needed to compute it. The very act of defining a resilience metric, and the discussions that ensue about its structure, helps a community to clarify and formalize what it means by the concept of resilience, thereby raising the quality of debate. The principles that resilience metrics can entail are illustrated by some existing national and international indicators or frameworks that address measurement of the resilience of different aspects of community systems. The Leadership in Energy and Environmental Design for developers, owners, and operators of buildings is one example. Comparison of the strengths and challenges of a variety of different frameworks for measuring resilience suggests that the critical dimensions of an encompassing and consistent resilience measurement system are

•   Indicators of the ability of critical infrastructure to recover rapidly from impacts;

•   Social factors that enhance or limit a community’s ability to recover, including social capital, language, health, and socioeconomic status;

•   Indicators of the ability of buildings and other structures to withstand earthquakes, floods, severe storms, and other disasters; and

•   Factors that capture the special needs of individuals and groups, related to minority status, mobility, or health status.

Presently, the nation does not have a consistent basis for measuring resilience that includes all of these dimensions. Until a community experiences a disaster and has to respond to and recover from it, demonstrating the complexity, volume of issues, conflicts and lack of ownership are difficult. A national resilience scorecard, from which communities can then develop their own, tailored scorecards, will make it easier for communities to see the issues they will face prior to an event and can support necessary work in anticipation of an appropriate resilience-building strategy. A scorecard will also allow communities to ask the right questions in advance of a disaster.

BUILDING LOCAL CAPACITY AND ACCELERATING PROGRESS: RESILIENCE FROM THE BOTTOM UP

National resilience emerges, in large part, from the ability of local communities with support from all levels of government and the private sector to plan and prepare for, absorb, respond to, and recover from disasters and adapt to new conditions. Bottom-up interventions—the engagement of communities in increasing their resilience—are essential because local conditions vary greatly across the country; the nation’s communities are unique in their history, geography, demography, culture, and infrastructure; and the risks faced by every community vary according to local hazards. Some universal steps can aid local communities in making progress to increase their resilience and include:

•   Engaging the whole community in disaster policymaking and planning;

•   Linking public and private infrastructure performance and interests to resilience goals;

•   Improving public and private infrastructure and essential services (such as health and education);

•   Communicating risks, connecting community networks, and promoting a culture of resilience;

•   Organizing communities, neighborhoods, and families to prepare for disasters;

•   Adopting sound land-use planning practices; and

•   Adopting and enforcing building codes and standards appropriate to existing hazards.

Community coalitions of local leaders from public and private sectors, with ties to and support from federal and state governments, and with input from the local citizenry, become very important in this regard. Such coalitions can be charged to assess the community’s exposure and vulnerability to risk, to educate and communicate risk, and to evaluate and expand the community’s capacity to handle such risk. A truly robust coalition would have at its core a strong leadership and governance structure, and people with adequate time, skill, and dedication necessary for the development and maintenance of relationships among all partners in the community.

THE LANDSCAPE OF RESILIENCE POLICY: RESILIENCE FROM THE TOP DOWN

Strong governance at all levels is a key element of resilience and includes the making of consistent and complementary local, state, and federal policies. Although resilience at its core has to be carried forward by communities, communities do not exist under a single authority in the United States, and function instead under a mix of policies and practices implemented and enforced by different levels of government. Policies that make the nation more resilient are important in every aspect of American life and economy, and not just during times of stress or trauma. A key role of policies designed to improve national resilience is to take the long-term view of community resilience and to help avoid short-term expediencies that can diminish resilience.

Certain policies of the federal Executive Branch, including Presidential Directives and Executive Orders, policies initiated by federal agencies, and policies of the Legislative Branch can and do function to help strengthen resilience. Presidential Policy Directive-8 (PPD-8) calls upon the Department of Homeland Security to embrace systematic preparation against all types of threats, including catastrophic natural disasters. Because the scope of resilience is sometimes not fully appreciated, some who contemplate national resilience policy think first of the Stafford Act and its role in disaster response and recovery. Although the Stafford Act does provide guidance for certain responsibilities and actions in responding to a disaster incident, national resilience transcends the immediate impact and disaster response and therefore grows from a broader set of policies. Many of the critical policies and actions required for improved national resilience are also enacted and implemented at the state and local levels.

Policies at all levels of governance do exist to enhance resilience; however, some government policies and practices can also have unintended consequences that negatively affect resilience. Furthermore, gaps in policies

and programs among federal agencies exist for all parts of the resilience process—including disaster preparedness, response, recovery, mitigation, and adaptation, as well as research, planning, and community assistance. Although some of these gaps are the result of the legislative authorization within which agencies are directed to operate, the roles and responsibilities for building resilience are not effectively coordinated by the federal government, either through a single agency or authority, or through a unified vision.

Community resilience is broad and complex, making it difficult to codify resilience in a single comprehensive law. Rather, infusing the principles of resilience into all the routine functions of the government at all levels and through a national vision is a more effective approach.

LINKING COMMUNITY AND GOVERNANCE TO GUIDE NATIONAL RESILIENCE

Increased resilience cannot be accomplished by simply adding a cosmetic layer of policy or practice to a vulnerable community. Long-term shifts in physical approaches (new technologies, methods, materials, and infrastructure systems) and cultural approaches (the people, management processes, institutional arrangements, and legislation) are needed to advance community resilience. Resilience to disasters rests on the premise that all aspects of a community—its physical infrastructure, its socioeconomic health, the health and education of its citizens, and its natural environment—are strong. This kind of systemic strength requires that the community members work in concert and in such a way that the interdependencies among them provide strength during a disaster event.

Communities and the governance network of which they are a part are complex and dynamic systems that develop and implement resilience-building policies through combined effort and responsibility. Experience in the disaster management community suggests that linked bottom-up and top-down networks are important for managing risk and increasing resilience. Key interactions within the nation’s resilience “system” of communities and governance can be used to help identify specific kinds of policies that can increase resilience and the roles and responsibilities of the actors in government, the private sector, and communities for implementing these policies. For example, to understand hazards or threats and their processes, research and science and technology policies allow federal and state agencies to coordinate efforts on detection and monitoring activities that can be used by regional and local governing bodies, the private sector, and communities to evaluate and address their hazards and risks. Identifying resilience policy areas, identifying those in community and government responsible for coordinating activities in those areas, and identifying the recipients of the information or services resulting from those activities reveal strengths and gaps in the nation’s resilience “system.”

Advancing resilience is a long-term process, but can be coordinated around visible, short-term goals that allow individuals and organizations to measure or mark their progress toward becoming resilient and overcoming these gaps. However, as a necessary first step to strengthen the nation’s resilience and provide the leadership to establish a national “culture of resilience,” a full and clear commitment to disaster resilience by the federal government is essential.

BUILDING A MORE RESILIENT NATION: THE PATH FORWARD

No single sector or entity has ultimate responsibility for improving national resilience. No specific federal agency has all of the authority or responsibility, all of the appropriate skill sets, or adequate fiscal resources to address this growing challenge. An important responsibility for increasing national resilience lies with residents and their communities. Input, guidance, and commitment from all levels of government and from the private sector, academia, and community-based and nongovernmental organizations are needed throughout the entire process of building more resilient communities. The report frames six recommendations (Box S-1) that can help guide the nation in advancing collective, resilience-enhancing efforts in the coming decades.

BOX S-1 Summary Recommendations

Recommendation 1: Federal government agencies should incorporate national resilience as a guiding principle to inform the mission and actions of the federal government and the programs it supports at all levels .

Recommendation 2: The public and private sectors in a community should work cooperatively to encourage commitment to and investment in a risk management strategy that includes complementary structural and nonstructural risk-reduction and risk-spreading measures or tools. Such tools might include an essential framework (codes, standards, and guidelines) that drives the critical structural functions of resilience and investment in risk-based pricing of insurance.

Recommendation 3: A national resource of disaster-related data should be established that documents injuries, loss of life, property loss, and impacts on economic activity. Such a database will support efforts to develop more quantitative risk models and better understand structural and social vulnerability to disasters.

Recommendation 4: The Department of Homeland Security in conjunction with other federal agencies, state and local partners, and professional groups should develop a National Resilience Scorecard.

Recommendation 5: Federal, state, and local governments should support the creation and maintenance of broad-based community resilience coalitions at local and regional levels.

Recommendation 6: All federal agencies should ensure that they are promoting and coordinating national resilience in their programs and policies. A resilience policy review and self-assessment within agencies and strong communication among agencies are keys to achieving this kind of coordination.

Increasing disaster resilience is an imperative that requires the collective will of the nation and its communities. Although disasters will continue to occur, actions that move the nation from reactive approaches to disasters to a proactive stance where communities actively engage in enhancing resilience will reduce many of the broad societal and economic burdens that disasters can cause.

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One way to reduce the impacts of disasters on the nation and its communities is to invest in enhancing resilience—the ability to prepare and plan for, absorb, recover from and more successfully adapt to adverse events. Disaster Resilience: A National Imperative addresses the broad issue of increasing the nation's resilience to disasters. This book defines "national resilience", describes the state of knowledge about resilience to hazards and disasters, and frames the main issues related to increasing resilience in the United States. It also provide goals, baseline conditions, or performance metrics for national resilience and outlines additional information, data, gaps, and/or obstacles that need to be addressed to increase the nation's resilience to disasters. Additionally, the book's authoring committee makes recommendations about the necessary approaches to elevate national resilience to disasters in the United States.

Enhanced resilience allows better anticipation of disasters and better planning to reduce disaster losses-rather than waiting for an event to occur and paying for it afterward. Disaster Resilience confronts the topic of how to increase the nation's resilience to disasters through a vision of the characteristics of a resilient nation in the year 2030. Increasing disaster resilience is an imperative that requires the collective will of the nation and its communities. Although disasters will continue to occur, actions that move the nation from reactive approaches to disasters to a proactive stance where communities actively engage in enhancing resilience will reduce many of the broad societal and economic burdens that disasters can cause.

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• Published: 16 September 2022

Human and economic impacts of natural disasters: can we trust the global data?

• Rebecca Louise Jones   ORCID: orcid.org/0000-0002-2099-6461 1 , 2 ,
• Debarati Guha-Sapir 3 &
• Sandy Tubeuf   ORCID: orcid.org/0000-0001-9001-1157 1 , 2  

Scientific Data volume  9 , Article number:  572 ( 2022 ) Cite this article

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• Natural hazards

Reliable and complete data held in disaster databases are imperative to inform effective disaster preparedness and mitigation policies. Nonetheless, disaster databases are highly prone to missingness. In this article, we conduct a missing data diagnosis of the widely-cited, global disaster database, the Emergency Events Database (EM-DAT) to identify the extent and potential determinants of missing data within EM-DAT. In addition, through a review of prominent empirical literature, we contextualise how missing data within EM-DAT has been handled previously. A large proportion of missing data was identified for disasters attributed to natural hazards occurring between 1990 and 2020, particularly on the economic losses. The year the disaster occurred, income-classification of the affected country and disaster type were all significant predictors of missingness for key human and economic loss variables. Accordingly, data are unlikely to be missing completely at random. Advanced statistical methods to handle missing data are thus warranted when analysing disaster data to minimise the risk of biasing statistical inferences and to ensure global disaster data can be trusted.

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Introduction.

As the global effects of climate change are felt more intensively, so too are the human and economic consequences of catastrophic disaster events. In 2020 alone, disaster events attributed to natural hazards affected approximately 100 million people, accounted for an estimated 190 billion US$ of global economic losses and resulted in 15,082 deaths 1 , 2 . In light of COP-26 and recent topical events including, but certainly not limited to, the Haitian Earthquake (2021), Central European Floods (2020) and Australian Bushfires (2020), a renewed urgency has been granted to the research of, preparedness to and mitigation of disasters attributed to natural hazards.

Comprehensive historical data held in disaster databases are central for numerous purposes across both the public and private domain to inform emergency disaster relief management; configure catastrophe risk assessment models; and conduct cost-benefit analyses of disaster risk reduction policies 3 . However, inconsistencies in the reporting of disaster events and methodological difficulties quantifying their impacts, mean that disaster databases are prone to gaps in data availability 3 , 4 . As a result, the scope and reliability of statistical inferences which can be made from disaster data are reduced 5 . Systematic reporting of disaster events is required to minimise missing data. However, to achieve this is a major challenge. Instead, the use of valid and often complex statistical methods to account for missing data are required 6 , 7 .

Missing data is a common issue across all research areas. Standard practices are adopted in randomised clinical trials to combat patient attrition 8 , 9 and in survey-based observational studies to handle unit- and item-non response 10 , 11 . However, within the disaster literature, there is little insight as to how missing data should be handled.

To date, there are six disaster databases which have global coverage: the Emergency Events Database (EM-DAT); NatCatSERVICE; Sigma; GLIDE; GFDRR; and BD CATNAT Global 12 . This analysis utilises EM-DAT data alone, as it is the only publicly available, global disaster database and is widely cited; an initial search of the terms: ‘EM-DAT’, ‘CRED’, ‘Emergency Events Database’ and ‘International Disaster Database’ in Google Scholar returned 21,000 search results spanning numerous disciplines. EM-DAT was founded in 1988 by the Université catholique de Louvain (Belgium) with support from the United States Agency for International Development (USAID), the World Health Organisation (WHO) and the Belgium Government 13 . Data are collated from sources including the United Nations, reinsurance firms, research institutions and the press. As well as reporting the occurrence of major disastrous events attributed to natural, technological and complex hazards, its meta-data captures the human and economic impacts to assist national and international humanitarian action.

In this study, we conduct a missing data diagnosis to assess the extent of missing data in EM-DAT and to identify observable factors associated with missingness. We restrict our analysis to disaster events attributed to natural hazards occurring between the years 1990 and 2020. In addition, through a review of highly-cited empirical literature utilising EM-DAT data, we illustrate how missing data has previously been dealt with. We conclude by providing a discussion on the potential methods to handle missing data within disaster databases.

The state of missing data in EM-DAT

The advent of digital technologies in academia and research from the 1980s initiated a proliferation in the collection of data and subsequently, the adoption of data governance and data quality tools to ensure its reliability 14 . In the disaster space, technological advances in disaster surveillance and a progressive global agenda towards standardising disaster reporting, embedded in the 2015–2030 Sendai framework 15 , favour a climate for complete data reporting. Despite this, there was a high proportion of missing data in EM-DAT for disaster events attributed to natural hazards occurring between 1990 and 2020 (Fig.  1 ). This was particularly evident for the reporting of economic losses: data were missing for 96.2% of disaster events on reconstruction costs, for 88.1% on insured damages and 41.5% on total estimated damages. In the three months following a disaster event, when EM-DAT collates the majority of its data, precise information on reconstruction costs and insured damages is likely to be sparse. Reporting of human losses were more complete, with proportions of missing data ranging from 1.3% to 22.3%. Given that the volume of external disaster aid hinges predominantly on death tolls and injury counts, this finding is unsurprising. Of note, there were substantial inconsistencies in the reporting of both human and economic losses, where in both cases, aggregate variables (total deaths and total estimated damages) were better informed. In particular, missing data on total deaths were negligible (1.3%). Hence, in this case, there is little risk of missing data biasing statistical inferences.

Missing data patterns and proportions of missing data for key human and economic loss variables in EM-DAT across all disaster events attributed to natural hazards and occurring between 1990 and 2020. Black shading denotes missing data for one or more disaster events; grey shading denotes observed data. Disaster events are ordered along the x-axis by the STATA default: from the least to the most missing across the variables analysed. The proportion of missing data for each variable, given as a percentage of the total data, is shown to the right-hand side of the figure. Variables are presented in descending order of missingness. Human loss variables include No. of Affected, defined as the number of people requiring immediate assistance during a period of emergency; No. of Missing, defined as the number of people whose whereabouts is unknown and who are presumed dead; No. of Deaths, defined as the number of people who lost their lives as a result of the disaster event; and Total Deaths, defined as the sum of No. of Affected and No. of Deaths. Economic loss variables include Reconstruction Costs, defined as the costs incurred due to the replacement of lost assets and the implementation of disaster mitigation measures; Insured Damages, defined as economic losses born by the insurance sector; and Total Estimated Damages, defined as a value of all economic losses directly or indirectly related to the disaster event.

It is important to distinguish not only the extent, but the pattern of missing data to inform the complexity of missingness 16 . For a small proportion of disaster events, data were missing across all variables analysed, denoted in Fig.  1 by the black, continuous vertical band along the right-hand side. More commonly, data were observed for at least one human loss variable and one economic loss variable. For human losses, missing information may be informed through the interdependence of human loss variables; EM-DAT defines total deaths as the sum of the number of people affected and the number of deaths 17 . Whereas for economic losses, there is no such interdependence across economic loss variables. EM-DAT defines total estimated damages as a value of all the economic losses directly or indirectly related to the disaster event, which does not necessarily include reconstruction costs. Missing data occurred sporadically throughout the dataset for each human loss variable, demonstrating an intermittent missing data pattern. However, across the economic loss variables, missing data patterns were less sporadic. For instance, data on reconstruction costs and insured damages were largely missing throughout, even when data on total estimated damages were informed.

Explaining missing data

Logistic regression analysis was used to identify whether the probability of data to be missing was dependent on observed data within EM-DAT 5 , 18 . Accordingly, it informs the nature of the missing data mechanism, specifically whether the mechanism of missing data deviates from Missing Completely At Random (MCAR). Data which are MCAR, that is, their probability to be missing is independent of observed and unobserved data, can be excluded from analyses with little risk of introducing bias. Conversely, if the missing data mechanism deviates from MCAR, the deletion of missing values may bias statistical inferences. In this case, more advanced methods are necessary to account for data gaps 6 . Here, we restricted our logistic regression analysis to model the probability of missingness within the variables: total estimated damages, the number of people affected, the number of people missing and the number of deaths (Supplementary Table  1 ). Methods to account for missing data are viable only for partially incomplete data 5 . In the case of reconstruction costs and insured damages, where data were almost entirely incomplete in our dataset (96.2% and 88.1% missing respectively) little can be done during data analysis to account for missingness. Hence, these variables were not investigated in the logistic regression analysis. The opposite was true for total deaths; the proportion of missing data on total deaths was negligible (1.3%). This precludes logistic regression analysis due to insufficient variation in the outcome variable, the probability to be missing. As a result, the variable total deaths was not investigated in the logistic regression analysis.

The observable data partially explained the probability of total estimated damages to be missing, revealed by a sizeable pseudo-R 2 value (pseudo-R 2  = 0.416) (Supplementary Table  1 ). Missing data on the number of people affected and the number of deaths were explained less by the observed data (pseudo-R 2  = 0.206 and pseudo-R 2  = 0.188 respectively) (Supplementary Table  2 ). This agrees with the intermittent missing data pattern identified for these variables (Fig.  1 ). The observed data explained much of the probability to be missing for the variable, the number of people missing (pseudo-R 2  = 0.621) (Supplementary Table  2 ). However, a large number of disaster events (n = 6,218) were omitted from the analysis by STATA, by default, due to occurring in years which perfectly predicted the probability to be missing. As a consequence, the precision of the parameter estimates is reduced, which requires the results to be interpreted with caution.

A number of observable factors in EM-DAT were statistically significantly (p < 0.05) associated to the probability of total estimated damages to be missing: the year the disaster occured, the income-group classification of the affected country, disaster severity and disaster type (Supplementary Table  1 ). Disaster events occurring after the year 2002 and in lower-income countries were positive predictors of the probability of data on total estimated damages to be missing. In addition, disaster severity, estimated by the natural logarithm of total deaths, and disaster types having prolonged effects including droughts, epidemics and extreme temperature events were also positive predictors of missingness.

The observed predictors of missingness differed for human loss variables. In contrast to missingness within the variable total estimated damages, the probability of data to be missing for the variables: the number of people affected and the number of deaths were statistically significantly (p < 0.01) lower for disasters which occurred in lower-income countries relative to high-income countries. Lower-income countries are the predominant recipients of international disaster aid. This result suggests that disaster aid incentivises more complete reporting of human losses. The influence of disaster type on the probability to be missing was largely heterogeneous across each variable analysed. In contrast, associations between the year the disaster occurred and the probability of data to be missing were similar across human and economic loss variables, with a higher probability of missingness for disaster events occurring after 2002.

Shortfalls of the current literature

The absence of published, standard procedures to account for missing data in disaster databases has set a precedent for a general lack of consideration of the issue in the empirical literature. We exemplify this by reviewing the top-20 most cited empirical studies utilising EM-DAT as a primary or secondary data source (Supplementary Table  3 ).

Eight studies neglected to mention missing data. Of the studies that did, missing data were often considered more broadly under the remit of data availability. With the exception of Brooks, Adger and Kelly 19 who assessed missing data in-depth in an accompanying paper, mention of missing data or data availability more generally, was limited to one or two sentences.

All disaster events meeting at least one of the following inclusion criteria are included in EM-DAT: i) ten or more people reported dead; ii) 100 or more people affected, injured or homeless; iii) a declaration of a state of emergency by the affected country, or an appeal for international assistance. Accordingly, for disaster events which meet these criteria, data on disaster occurrences are entirely observed in EM-DAT. As such, the extent to which missing data need be considered is dictated by the type of EM-DAT data utilised in empirical studies, namely whether data are utilised on disaster occurrence alone, or on the consequences.

Statistical methods utilised during data analysis typically presume complete information for all variables specified in the model. Therefore, assumptions on the mechanism of missing data are made, whether explicitly or not. Neither the publication year, nor the number of times a paper had been cited affected the approach taken to handle missing data. Of the studies reviewed, half attempted to account for missing data. However, the approaches employed were mostly ad-hoc with no statistical grounding. In six studies, authors restricted their analysis to variables or subsets of data with improved data availability. In addition to restricting the scope of their analysis, Brooks, Adger and Kelly 19 imputed zero values in place of missing values. However, this approach may heavily skew the distribution of data. Dilley et al . 20 aggregated mortality estimates over all disaster events attributed to a natural hazard between 1981 and 2000. This method intends to reduce the relative impact of missing data. Nevertheless, it also compromises the precision of analyses. Barredo 21 and Doocy et al . 22 supplemented EM-DAT data with data from alternative sources in an attempt to generate a more complete dataset. However, merging of datasets can be time-consuming and can itself introduce ‘file matching’ biases if datasets are heterogeneous in their structure or content 16 . Yang 23 utilised mean imputation whereby missing values are imputed with a single, unconditional mean of the observed values. Nevertheless, this method is generally not recommended, as the uncertainty in the predicted values due to missing data are not adequately reflected 24 .

Handling missing data in disaster databases

Identifying a suitable approach to handle missing data will depend on the characteristics of the dataset, the variables of interest and the intended data analysis. Regardless, the choice of missing data method should be grounded on plausible assumptions about the missing data mechanisms as informed through a diagnosis of the missing data. In Supplementary Table  4 , we describe conventional and advanced missing data methods which are relevant to handling missing data within disaster databases. An exhaustive review of the various missing data methods is beyond the scope of this paper and can be found elsewhere, see Allison 24 , Graham 25 and Graham, Patricio and Allison 26 .

Two broad approaches to handle missing data exist: deletion and imputation. Deletion methods, which are so-called ‘quick-fix’ methods, include column deletion, Complete Case Analysis (CCA) and Available Case Analysis (ACA). However, these methods can exclude a large proportion of the original dataset when a high proportion of missing data exists. In addition, deletion methods generally rely on missing data to be MCAR. Hence, when the mechanism of missingness deviates from this assumption, these methods can do more harm than good, biasing statistical inferences reported by studies 7 . Imputation-based methods are of two main varieties: single imputation and multiple imputation. Single imputation methods, such as mean imputation and regression-based imputation replace missing values with a single, predicted value estimated from the observed data. In contrast, multiple imputation generates a range of predicted values through multiply imputed datasets and thus, adequately reflects the uncertainty in the predicted values due to missing data 26 . Other advanced missing data methods include inverse probability weighting, maximum likelihood and Bayesian simulation. In addition, missing data methods can be combined. For instance, Bayesian techniques can be incorporated into the first step of multiple imputation.

Neglecting the issue of missing data exposes studies which inform regional, national and international disaster policies to biases and compromises their effectiveness. The complete, peer-reviewed reporting of disaster events through strengthened field-level data quality remains the eventual goal. In the meantime, given the data available, we argue that to achieve reliable evidence-based disaster policy, it is crucial that missing data is appropriately diagnosed and accounted for in the empirical literature.

This analysis is one of the first to reveal deviations from the assumption of MCAR for missing data on key human and economic loss variables in EM-DAT. Consequently, methods to handle missing data which are MAR should be considered. Deviations from the assumption of MAR, namely that missing data are Missing Not At Random (MNAR) and are thus dependent on unobserved data, cannot be explicitly tested. Instead, sensitivity analyses should be performed to test the robustness of parameter estimates to different assumptions about the mechanisms of missing data. Advanced missing data methods such as multiple imputation, maximum-likelihood and Bayesian simulation can be adapted to account for missing data which are MNAR.

In addition, this analysis highlights key limitations of EM-DAT data. Despite technological advances in disaster surveillance and general progress in data collection, an increase in the proportions of missing data since 2002 suggest shortfalls in current data quality procedures. More-so, the large disparities in the reporting of human losses compared to economic losses points to a wider issue in the types of data collected. Count data in physical units (number of deaths, number of people affected etc.) is principally used to portray human losses, whereas monetary estimates (reconstruction costs, insured damages etc.) are used to denote economic losses. Direct inference of monetary estimates is not straightforward. A two-stage valuation approach may therefore be more suitable and limit the extent of missing data on economic losses. In this case, count data, for instance the number of buildings damaged, the number of days without electricity, would primarily be used to evaluate economic losses in disaster databases. Standard economic valuation methods could then be applied, where necessary, to transform these values into monetary estimates.

By examining the global disaster database EM-DAT, this paper highlights shortfalls in the quality and use of disaster data which are rarely considered. Limitations in the quality of disaster databases arising from data collection procedures have been explored in previous literature 3 , 4 , 27 , 28 . In particular, limitations in the availability of data on low-intensity disaster events due to the inclusion criteria specified by CRED are well known. Instead, this paper contributes a different perspective to the existing literature; given the data collected, to what extent is there an issue of missing data and how should it be handled to ensure global disaster data can be trusted? In this respect, we aim to prompt the reader to apply a critical eye when assessing global disaster data, rather than implicitly trust its reliability.

The extent, complexity and mechanisms of missing data will vary according to the data analysed. Because of this, we are unable to prescribe a single approach to handle missing data within disaster databases. Instead, we advocate for analysts to consider missing data on an individual study basis. Although this article is a step in the right direction, a considerable research effort is still required to inform how missing data within disaster databases should be handled. In particular, the mechanisms of missing data in EM-DAT, and other disaster databases, should be more clearly identified. This would allow data limitations to be better informed and the performance of statistical methods, grounded on plausible assumptions about the mechanisms of missing data, to be tested.

Missing data diagnosis

As per procedures initially employed by Faria et al . 18 and Gabrio et al . 29 , but adapted to the context of disaster databases, this missing data diagnosis comprised three steps; i) a description of the proportions of missing data; ii) an analysis of the patterns of missing data; and iii) a logistic regression analysis to assess associations between the probability to be missing and observed values in the dataset. The dataset was restricted to disasters attributed to natural hazards and occurring between the years 1990 and 2020. Data analysis was carried out in STATA (version 16.1) and graphics were produced using the scheme ‘cleanplots’ 30 . The code used for the data analysis can be found in the supplementary material ( Supplementary Material ).

The proportion of missing data was computed across all variables using the command ‘mdesc’ 31 . Missing data patterns were produced using the command ‘misspattern, novarsort’ 32 . Due to computational limits attributed to a large number of observations for each variable, the option ‘noidsort’ could not be applied. Accordingly, observations were arranged by the default: from the least to the most missing across the variables of interest. Missing data patterns were produced for all human loss variables in the dataset (number of people affected, number of people missing, number of deaths and total deaths) and select economic loss variables (reconstruction costs, insured damages and total estimated damages). While individual direct, indirect, sectoral and infrastructural losses were included in the dataset, these were omitted from visualisations of missing data patterns as they were rarely informed.

Statistical analyses, including the t-test, Pearson’s chi-squared test, Little’s MCRA test and binary probability models, are used to test the assumption that missing data is MAR. That is, missing data can be explained by observed values in the dataset 18 . Logistic regression analysis was used in this study to model the probability of missingness for a given disaster event i , conditional on a vector of observable predictors of missingness ( X i ) (Eq.  1 ). The outcome variable ( y i ) was therefore a dichotomous missing data indicator taking value 1 if data were missing, or 0 if data were informed. Missing data indicators were generated for the variables: total estimated damages, number of people affected, number of people missing and number of deaths.

with ε it denoting the error term and F (·) denoting the cumulative distribution function (cdf) which takes a standard logistic distribution (Eq.  2 ) in logistic regression analysis.

Observed predictors of missingness ( X i ) included disaster type, a categorical variable denoting the type of natural disaster, omitting the disaster type flood as the reference to control for multi-collinearity; income-classification of the affected country, omitting high-income as the reference category; and the year the disaster occurred, between 1990 and 2020, included in the model as a categorical variable with the year 1990 omitted as a reference. Reference categories were selected due to being the most frequently observed in the dataset. The year the disaster occurred was included in the model as fixed effects to assess both the trend in missing data over time and the individual threshold effects across each year. Disaster severity, estimated by the logarithm of total deaths, was also included in the model specification when the probability of total estimated damages to be missing was assessed. Otherwise, disaster severity was excluded from the model due to being highly correlated with the variables: number of people affected, number of people missing and number of deaths. Total deaths was log transformed to account for the left-skewed, binomial distribution, arising from the high proportion (60%) of zero reported deaths in the dataset. Observed predictors of missingness were selected based on a step-wise approach.

Logistic regression analysis was estimated by maximum likelihood estimation (Eq.  3 ) using the command ‘logistic’ for all disaster events having complete information on the predictors of missingness. Marginal effects were computed at the mean using the command ‘mfx’ to enable interpretation of the magnitude of effect in the probability scale.

The robustness of our results was assessed using a probit model and a conditional (fixed-effect) logit model. The latter was used to test for potential ‘incidental parameter’ bias arising from the inclusion of individual fixed effects.

Review of the literature

Disaster databases are highly prone to missingness. Despite this, little insight exists as to how missing data should be handled in this context. To inform previous approaches to handling missing data in disaster databases, empirical studies utilising EM-DAT as a primary or secondary data source were reviewed. As this review was conducted for illustrative purposes only, we sought only prominent, highly-cited studies since these typically shape the standards for subsequent research. Studies were identified through electronic database searches of Web of Science, Scopus, PubMed and Google Scholar on 01/06/2022 using the key search terms: ‘EM-DAT’, ‘Emergency Events Database’, ‘International Disaster Database’ and ‘CRED’. Only papers considered to be empirical in nature and published between 1990 and 2022 were considered. No language restrictions were applied. Of the initial 2,127 search results, 421 papers were deemed to meet the inclusion criteria. Of these, the top-20 most cited papers were identified. For each paper, data were extracted on the use of EM-DAT data, the consideration given to missing data and the approach taken to handle missing data 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 .

Data availability

The data utilised in this analysis were acquired from the Emergency Events Database (EM-DAT). Data from EM-DAT is publicly and freely available to download from: https://public.emdat.be/ . Restrictions apply to the dataset used in this analysis, which included additional data from that freely available. This was acquired by the authors on a direct request to the Centre for Research on the Epidemiology of Disasters (CRED). Data is however available from the corresponding author upon reasonable request and with the permission of CRED, which can be obtained via written request to Ms. Regina Below, the EM-DAT database manager, at [email protected].

Code availability

The code used to generate the results of this analysis are available as supplementary material. Data analysis was carried out in STATA (version 16.1).

International Federation of Red Cross and Red Crescent Societies, National Disaster Reduction Centre of China & Academy of Disaster Reduction and Emergency Management. 2020 Global Natural Disaster Assessment Report . https://www.preventionweb.net/publication/2020-global-natural-disaster-assessment-report (2021).

Bevere, L. Natural catastrophes in 2020. Swiss RE sigma https://www.swissre.com/institute/research/sigma-research/sigma-2021-01.html (2021).

Wirtz, A., Kron, W., Löw, P. & Steuer, M. The need for data: natural disasters and the challenges of database management. Nat. Hazards. 70 , 135–157 (2012).

Article   Google Scholar  

Guha-Sapir, D. & Below, R. Quality and accuracy of disaster data: A comparative analyse of 3 global data sets. CRED Work. Pap . 1–18 (2002).

Rubin, D. B. Inference and Missing Data. Oxford Univ. Press 63 , 581–592 (1976).

ADS   MathSciNet   MATH   Google Scholar  

Harrell, F. E. Regression Modeling Strategies 2nd edn (Springer International Publishing, 2015).

Schafer, J. L. & Graham, J. W. Missing Data: Our View of the State of the Art. Psychol. Methods. 7 , 147–177 (2002).

Schulz, K. F., Altman, D. G. & Moher, D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. PLoS Med. 7 , 1–7 (2010).

Little, R. J. et al . The Prevention and Treatment of Missing Data in Clinical Trials. N. Engl. J. Med. 367 , 1355–1360 (2012).

Article   CAS   Google Scholar  

Young, R. & Johnson, D. R. Handling Missing Values in Longitudinal Panel Data With Multiple Imputation. J. Marriage Fam. 77 , 277 (2015).

Vandenbroucke, J. P. et al . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLOS Med. 4 , 297 (2007).

Mazhin, S. et al . Worldwide disaster loss and damage databases: A systematic review. J. Educ. Health Promot. 10 , 329 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Cummings, W. S. In Economic Effects of Natural Disasters (ed. Chaiechi, T.) Ch.1 (Academic Press, 2020).

Foote, K. A Brief History of Data Quality. DATAVERSITY https://www.dataversity.net/a-brief-history-of-data-quality/# (2021).

United Nations Office for Disaster Risk Reduction (UNDRR). Sendai Framework for Disaster Risk Reduction 2015–2030. United Nations https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030 (2015).

Little, R. J. A. & Rubin, D. B. Statistical analysis with missing data (Wiley, 2002).

Centre for Research on the Epidemiology of Disasters (CRED). EM-DAT Glossary https://www.emdat.be/Glossary (2021).

Faria, R., Gomes, M., Epstein, D. & White, I. R. A Guide to Handling Missing Data in Cost-Effectiveness Analysis Conducted Within Randomised Controlled Trials. Pharmacoeconomics. 32 , 1157–1170 (2014).

Brooks, N., Adger, W. N. & Kelly, P. M. The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Glob. Environ. Chang. 15 , 151–163 (2005).

Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L. & Arnold, M. Natural Disaster Hotspots: A Global Risk Analysis. World Bank https://openknowledge.worldbank.org/handle/10986/7376 (2005).

Barredo, J. I. Major flood disasters in Europe: 1950-2005. Nat. Hazards. 42 , 125–148 (2007).

Doocy, S., Daniels, A., Murray, S. & Kirsch, T. D. The Human Impact of Floods: a Historical Review of Events 1980-2009 and Systematic Literature Review. PLoS Curr . 5 (2013).

Yang, D. Coping with disaster: The impact of hurricanes on international financial flows, 1970–2002. B.E. J. Econ. Anal. Policy . 8 (2008).

Allison, P. D. In The Sage handbook of quantitative methods in pyschology (ed. Millsap, R. E.) Ch. 4 (Springer Publications Ltd., 2009).

Graham, J. W. Missing Data Analysis: Making It Work in the Real World. Annu. Rev. Psychol. 60 , 549–576 (2009).

Graham, J. W., Cumsille, P. E. & Shevock, A. E. In Handbook of Psychology Vol. 2 (ed. Weiner) Ch. 4 (Wiley, 2012).

Osuteye, E., Johnson, C. & Brown, D. The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities. Int. J. Disaster Risk Reduct. 26 , 24–33 (2017).

Lin, Y. C., Khan, F., Jenkins, S. F. & Lallemant, D. Filling the Disaster Data Gap: Lessons from Cataloging Singapore’s Past Disasters. Int. J. Disaster Risk Sci. 12 , 188–204 (2021).

Gabrio, A., Mason, A. J. & Baio, G. Handling Missing Data in Within-Trial Cost-Effectiveness Analysis: A Review with Future Recommendations. Pharmacoeconomics. 1 , 79–97 (2017).

Mize, T. D. Source code for: Cleanplots: Stata graphics scheme. Trenton Mize https://www.trentonmize.com/software/cleanplots (2018).

Medeiros, R. A., Blanchette, D., Medeiros, R. A. & Blanchette, D. Source code for: MDESC: Stata module to tabulate prevalence of missing values. Statistical Software Components https://econpapers.repec.org/software/bocbocode/s457318.htm (2011).

White, I. Network meta-analysis. Stata J. 15 , 951–985 (2015).

Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature. 529 , 84–87 (2016).

Article   ADS   CAS   Google Scholar  

Kahn, M. E. The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions. Rev. Econ. Stat. 87 , 271–284 (2005).

Noy, I. The macroeconomic consequences of disasters. J. Dev. Econ. 88 , 221–231 (2009).

Alcántara-Ayala, I. Geomorphology, natural hazards, vulnerability and prevention of natural disasters in developing countries. Geomorphology. 47 , 107–124 (2002).

Article   ADS   Google Scholar  

Toya, H. & Skidmore, M. Economic development and the impacts of natural disasters. Econ. Lett. 94 , 20–25 (2007).

Jonkman, S. N. Global Perspectives on Loss of Human Life Caused by Floods. Nat. Hazards. 34 , 151–175 (2005).

Strömberg, D. Natural Disasters, Economic Development, and Humanitarian Aid. J. Econ. Perspect. 21 , 5–222 (2007).

Kovács, G. & Spens, K. Identifying challenges in humanitarian logistics. Int. J. Phys. Distrib. Logist. Manag. 39 , 506–528 (2009).

Barredo, J. I. Normalised flood losses in Europe: 1970-2006. Nat. Hazards Earth Syst. Sci. 9 , 97–104 (2009).

Nadim, F., Kjekstad, O., Peduzzi, P., Herold, C. & Jaedicke, C. Global landslide and avalanche hotspots. Landslides. 3 , 159–173 (2006).

Kellenberg, D. K. & Mobarak, A. M. Does rising income increase or decrease damage risk from natural disasters? J. Urban Econ. 63 , 788–802 (2008).

Deressa, T., Hassan, R. M. & Ringler, C. Measuring Ethiopian farmers’ vulnerability to climate change across regional states . IFPRI Discussion Paper 806 (International Food Policy Research Institute, 2008).

Raschky, P. A. Institutions and the losses from natural disasters. Nat. Hazards Earth Syst. Sci. 8 , 627–634 (2008).

Mohapatra, S., Joseph, G. & Ratha, D. Remittances and natural disasters: Ex-post response and contribution to ex-ante preparedness. Environ. Dev. Sustain. 14 , 365–387 (2012).

Kvaløy, B., Finseraas, H. & Listhaug, O. The publics’ concern for global warming: A cross-national study of 47 countries. J. Peace Res. 49 , 11–22 (2012).

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Acknowledgements

The authors would like to thank the Université catholique de Louvain for their support on this research paper and the University of York who permitted R.L.J. to undertake part of this research as part of her Master’s dissertation. We would also like to thank the reviewers and editors at Scientific Data, who helped to shape the final version of this paper.

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The study was conceptualised by S.T. and D.G.S. Data analysis was performed by R.L.J. under the supervision of S.T. All authors contributed to the writing and revision of the manuscript.

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Results of logistic regression analysis to test associations between the probability of data to be missing on total estimated damages and observable data in the Emergency Events Database (EM-DAT)

41597_2022_1667_MOESM3_ESM.pdf

Results of logistic regression analysis to test associations between the probability of data to be missing on select human loss variables and observable data in the Emergency Events Database (EM-DAT)

Consideration of missing data in pivotal empirical studies utilising the Emergency Events Database (EM-DAT)

Reference list, glossary of conventional and advanced missing data methods, rights and permissions.

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Jones, R.L., Guha-Sapir, D. & Tubeuf, S. Human and economic impacts of natural disasters: can we trust the global data?. Sci Data 9 , 572 (2022). https://doi.org/10.1038/s41597-022-01667-x

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Launching Research of Natural Disasters

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These are the CCS Standards addressed in this lesson:

• W.5.7: Conduct short research projects that use several sources to build knowledge through investigation of different aspects of a topic.
• W.5.8: Recall relevant information from experiences or gather relevant information from print and digital sources; summarize or paraphrase information in notes and finished work, and provide a list of sources.
• I can cite evidence from a source to support answers to my research questions. ( W.5.7, W.5.8 )
• Natural Disasters Research Note-catcher ( W.5.7, W.5.8 )

• Expert Group Natural Disaster signs by writing the name of each expert group natural disaster on a piece of paper: earthquakes, hurricanes, tornadoes, volcanoes, and tsunamis. Post in separate areas of the room.
• Group the Infer the Topic Resources as follows and post by the Expert Group Natural Disaster signs:
• Earthquakes: Resources 4, 5, 6, 17,
• Hurricanes: Resources 1, 2, 3, 18
• Tornadoes: Resources 7, 8, 15, 19
• Volcanoes: Resources 9, 10, 14, 21
• Tsunamis: Resources 11, 12, 13, 20
• Technology necessary for students to access the links provided on the Natural Disaster Video Links sheet (see Materials).
• Review the Independent Reading: Sample Plans in preparation for launching independent reading in the Closing (see the Tools page ).
• Post: Learning targets and applicable anchor charts (see Materials).

Tech and Multimedia

• Continue to use the technology tools recommended throughout Modules 1-3 to create anchor charts to share with families, to record students as they participate in discussions and protocols to review with students later and to share with families, and for students to listen to and annotate text, record ideas on note-catchers, and word-process writing.
• Work Time C: Students use web research to answer research questions. There is a page of links (Natural Disaster Video Links) provided for them to quickly locate the videos.
• Consider that YouTube, social media video sites, and other website links may incorporate inappropriate content via comment banks and ads. Although some lessons include these links as the most efficient means to view content in preparation for the lesson, preview links and/or use a filter service, such as www.safeshare.tv , for viewing these links in the classroom.
• Supports guided in part by CA ELD Standards 5.I.C.10 Important points in the lesson itself
• The basic design of this lesson supports ELLs by allowing them to choose which natural disaster they will research, develop their own research questions, and work closely with an expert group to conduct their research. The offering of choice and supportive group work will increase students' motivation and level of engagement as they research their natural disaster during this unit and across the module.
• ELLs may find it challenging to generate research questions before they have chosen a natural disaster to research. Remind them of the research they conducted in Module 2, and guide the process for developing questions for this module as much as possible. Additionally, ELLs may find it challenging to identify relevant information in their expert group video to answer the research questions (see Levels of Support and the Meeting Students' Needs column)

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For lighter support:

• After adding unfamiliar vocabulary words to the Academic Word Wall during Work Time A, invite students to use each word in a sentence with context. This will support their understanding of each word, as well as provide additional context for each word for students who need heavier support.

For heavier support:

• Consider introducing students to the natural disasters and allowing them to decide which one to research prior to the lesson. Allow students to view the videos and review their notes before deciding. Invite them to prioritize two natural disasters to allow for flexibility when strategically grouping students during Work Time B.
• Multiple Means of Representation (MMR): In order to facilitate effective learning during this lesson, ensure that all students have access to the directions in each activity, and feel comfortable with the expectations. Vary the ways in which you convey expectations for each activity or task. Consider engaging in a clarifying discussion about the directions, or creating an outline of the steps for each activity.
• Multiple Means of Action & Expression (MMAE): Continue to support a range of fine motor abilities and writing need by offering students options for writing utensils. Alternatively, consider supporting students' expressive skills by offering partial dictation of student responses. Recall that varying tools for construction and composition supports students' ability to express information gathered from the text.
• Multiple Means of Engagement (MME): Throughout this lesson, students have opportunities to share ideas and thinking with classmates. Some students may need support for engagement during these activities, so encourage self-regulatory skills by helping them anticipate and manage frustration by modeling what to do if they need help from their partners. Consider offering sentence frames to strategically selected peer models. Recall that offering these supports for engagement promotes a safe learning space for all students

Key: Lesson-Specific Vocabulary (L); Text-Specific Vocabulary (T); Vocabulary Used in Writing (W)

credible, affect, experience, relevant (L)

• Module Guiding Questions anchor chart (begun in Lesson 1)
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• Performance Task anchor chart (begun in Lesson 1)
• Natural Disasters Research note-catcher (one per student and one to display)
• Natural Disasters Research note-catcher (example, for teacher reference)
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• Domain-Specific Word Wall (begun in Lesson 1)
• Vocabulary log (from Module 1; one per student)
• Expert Group Natural Disaster signs (to display; see Teaching Notes)
• Infer the Topic resources (from Lesson 1; to display)
• Natural Disaster video links (one per student and one to display)
• Independent Reading: Sample Plans (for teacher reference; see the Tools page )

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