The Global Risk Analysis for the 2009 GAR
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The document summarizes the Global Risk Analysis project which models various natural hazards at a global level to assess risk. It involved partnerships between UNEP, the World Bank, Columbia University and other organizations. The analysis considers hazards, exposure, vulnerability and computes expected fatalities and economic losses. It finds risk is highly concentrated in specific countries and calls attention to rapidly increasing exposure due to population growth and urbanization.
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The Global Risk Analysis for the 2009 GAR
- 1. The Global Risk Analysis for the 2009 GAR Davos, 2 June 2010 Pascal Peduzzi, UNEP
- 2. UN/ISDR , Coordination UNEP/GRID-Europe : Flood and cyclones hazards, georeference of losses, computation of human and economic exposures for all hazards, vulnerability, mortality risk, mapping and Index generation, data dissemination. World Bank : GDP distribution, Risk economic analysis Columbia University: drought hazard. Norwegian Geotechnical Institute (NGI) : tsunami & landslides hazards. Contributions from many partners: - WMO : review of methodologies for hydro-met. hazards - UNESCO : review of methodologies for tectonic hazards - Dartmouth Flood Observatory : past flood events - United States Geological Survey (past earthquakes evens) Supported by: UNDP/BCPR (GRIP), UN/ISDR, UNEP, World Bank and Norway, Switzerland. A collaborative effort (2007 – 2009)
- 3. Risk Risk = Hazard x Exposure x Vulnerability Expected frequency of occurrence of different intensities and types of threats (e.g. cyclones, floods, earthquakes,…) for a specific area. Equation of risk used in the study Hazard Exposure Vulnerability The probability of “potential losses” for some particular cause, place and period. People, assets, present in the hazard area. Percentage of exposure losses should an event of a specific type and severity occur (varies between 0 and 1). In this study, also includes coping capacity.
- 4. Risk = Hazard x Exposure x Vulnerability Equation of risk used in the study * * UNDRO (1979), Natural Disasters and Vulnerability Analysis in Report of Expert Group Meeting Calibrated using past disasters Modelled based on physical and geographical datasets Population or assets as extracted using GIS. To be identified using multiple regression analysis.
- 5. Tectonic Hazards What’s new on hazard ?
- 6. New exposure at 1 x 1 km (human & economical) Population and GDP distribution Models made for every years from 1975 to 2007
- 7. 2510 topical cyclones events were processed Global coverage for the period 1977 to 2006. Using central pressure Maximum windspeed Latitude … Individual past hazardous events modelling
- 8. Nargis 2 May 2008 Myanmar Extraction of exposure and other parameters
- 9. Date Iso3 Killed Est. damages Footprints Pop. exp. GDP exp. Pop.Urb exp. GDP Urb. exp Category 1 10,500,000 43,000,000 4,800,000 32,500,000 2 1,500,000 3,500,000 1,400,000 525,000 3 400,000 800,000 375,000 150,000 Country: Myanmar Iso3: MMR Date: 02 May 2008 Date Iso3 Killed: 138,366 Damages: 4,000 US$ millions GDPcap: 1,227 US$ Voice & acc.: -2.16 Governance efficiency : -1.608 Radio/inhabitant: 99.68% HDI: 0.592 … Urban growth: 2.55% … Date Iso3 GDPcap Voice & acc. Governance efficiency Radio/inhabitant HDI … Urban growth Preview Tropical Cyclones Database EM-DAT, CRED Database Vulnerability Database 43 indicators
- 10. 43 indicators on: Economy, Demography, Environment, Development, Early Warning, Governance, Health, Education, … List of vulnerability parameters considered 1 AIDS estimated deaths, aged 0-49 (% of tot. pop.) 2 non GLC2000 bare land 3 Arable and Permanent Crops - % of non GLC2000 bare land 4 Motor vehicles in use - Passenger cars (thousand) 5 Motor vehicles in use - Commercial vehicles (thousand) 6 Physical exposure to conflicts 7 Corruption Perceptions Index (CPI) 8 Arable and Permanent Crops - Total 9 Arable and Permanent Crops - Percent of Land Area 10 Control of Corruption 11 Deforestation rate 12 % of population with access to electricity 13 Forests and Woodland (% of Land Area) 14 Gross Domestic Product - Purchasing Power Parity per Capita 15 Gross Domestic Product - Purchasing Power Parity 16 inequality (Gini coefficient) 17 Human Induced Soil Degradation (GLASOD) 18 Government Effectiveness 19 Human Development Index (HDI) 20 Per capita government expenditure on health (PPP int. $) 21 # of hospital beds per 100,000 habitants # of doctors 22 infant mortality and malnutrition (though are also factored into HDI) 23 Improved Drinking Water Coverage - Total Population 24 telecommunications (phone density per 100,000 habitants) 25 Political Stability 26 Population (Persons (in Thousands)) 27 Urban Population (% of Total Population) 28 Radio receivers (per thousand inhabitants) 29 Regulatory Quality 30 Rule of Law 31 School enrollment, primary (total) 32 % of urban population living in slums / squatter settlements 33 Physicians density (per 10 000 population) 34 Under five years old mortality rate 35 Undernourished (% of total population) 36 Urban Population Growth on past 3 years 37 Voice and Accountability 38 Motor vehicles in use - Passenger cars (per inhabitant) 39 Motor vehicles in use - Commercial vehicles (per inhabitant) 40 School enrollment, primary (per inhabitant) 41 Population growth on 3 past years 42 income-consumption poverty (from WB poverty calculator also from MDG project) 43 Transport
- 13. From hazardous events to frequency and exposure
- 14. Aggregation of human exposure at country level
- 15. Aggregation of economical exposure at country level
- 16. Landslides risk
- 17. About 2.2 million people are exposed to landslides worldwide. 55% of mortality risk is concentrated in 10 countries, which also account for 80% of the exposure. Comoros, Dominica, Nepal, Guatemala, Papua New Guinea, Solomon Islands, Sao Tome and Principe, Indonesia, Ethiopia, and the Philippines Landslides (modelled for both precipitation and earthquakes)
- 18. Flood risk
- 19. Disaster risk is intensively concentrated
- 20. Tropical cyclones risk Earthquakes risk Multiple Risk
- 21. Risk is unevenly distributed….. 22.5 million exposed per year GDPcap. US$31,267 HDI =0.953 Mortality ratio = 1 17.3 million exposed per year GDP cap. US$5,137 HDI = 0.771 Mortality ratio = 17
- 22. Modelled fatalities per year (absolute) Modelled fatalities per million inhabitant per year (relative) Multi Mortality Risk Index (MRI) Vanuatu Mexico Bangladesh Solomon Ilands China Italy Oman Saudi Arabia Monserrat Mexico Vanuatu
- 23. Floods Mortality Risk Index (MRI) Cyclones Mortality Risk Index (MRI) Earthquakes Mortality Risk Index (MRI) Landslides Mortality Risk Index (MRI)
- 24. Is exposure increasing ? World Population 1975: 4.1 billion 1990: 5.3 billion 2007: 6.7 billion Population distribution change between 1975 and 2007 More than 50% of world population is now urban… … and about a third of urban population lives in slums
- 25. +100% + 80% + 60% + 20% 0 - 20% - 40% Rate in reducing vulnerability Cannot compete with the rate of increasing exposure Hazard Exposure Vulnerability Risk Even if hazard supposed constant… + 40% Mortality Economic Flood risk trend, 1990 - 2007 ? ?
- 27. The PREVIEW Global Risk Data Platform http://www.grid.unep.ch/preview How to access the data ?
Notas do Editor
- The first finding of the report is that the risk of both mortality and economic loss in disasters is highly concentrated in a very small portion of the earth’s surface. Countries with large populations exposed to severe natural hazards account for a very large proportion of global disaster risk. For example, 75% of global flood mortality risk is concentrated in only three countries: India, China and Bangladesh. The map shows concentrations of such risk in Bangladesh and India and in Vietnam Similarly most mortality and economic loss are concentrated in a very small number of disasters. Between 1975 and 2008, 23 mega-disasters killed 1,786,084 people, meaning that 0.26% of the events accounted for 78.2% of the mortality in that period. Economic loss is also highly concentrated. However, small island developing states (SIDS) and other small countries have far higher levels of relative risk with respect to the size of their populations and economies. For example, in the case of tropical cyclones, Vanuatu has the highest mortality risk per million inhabitants in the world, followed by St. Kitts and Nevis. Countries such as Lao and Cambodia have as high relative flood mortality risk as China or Bangladesh, even though they have far smaller populations.
- Disaster risk is not evenly distributed. Developing countries concentrate a hugely disproportionate share of the risk. T he map shows cyclone mortality risk in Japan and Philippines. In Japan, approximately 22.5 million people are exposed annually, compared to 16 million people in the Philippines. However, the estimated annual death toll from cyclones in the Philippines is almost 17 times greater than that of Japan. This uneven distribution of risk is also true for groups of countries. F or the same number of people exposed to tropical cyclones, mortality risk in low-income countries is approximately 200 times higher than in OECD countries. Poorer countries also experience higher economic losses in relation to the size of their economies. OECD countries, including Japan, the United States of America and Australia, account for almost 70% of estimated global annual economic losses to tropical cyclones – approximately 90 times more than the losses in exposed countries in sub-Saharan Africa. However, the sub-Saharan African countries experience almost three and a half times more economic loss, with respect to the size of their exposed GDP. Similarly, Latin America and the Caribbean experience over six times more economic loss than OECD countries, relative to their exposed GDP. In the case of floods, South Asia experiences approximately 15 times more economic losses with respect to the size of its GDP, than the OECD countries.
- The central message of the report is that investing in disaster risk reduction will help to reduce poverty, safeguard development and aid climate change adaptation with favourable impacts on global security, sustainability and stability Disaster risk is intimately interconnected with poverty. Poor communities in hazard prone developing countries are disproportionately at risk. The over-riding message of the Report is that reducing disaster risk helps in reducing poverty, safeguarding development and adapting to climate change. Given that disaster risk is interlocked with other global threats ( such as food and energy security, climate change and conflict) reducing risk will have beneficial effects on broader global security, stability and sustainability. Given the urgency posed by climate change, the Report forcefully argues the case for taking action now .