Global Report on Human Settlements 2007 - PoA-ISS

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174 Natural and human-made disasters Human-made disasters typically cause less direct loss of life than natural disasters Box 7.3 Bhopal: A deadly human-made disaster The accident at Union Carbide’s pesticide plant in Bhopal, Madhya Pradesh (India), in 1984, exposed 500,000 people, the majority living in low-income settlements close to the plant, to toxic gas. To date, assessments of the death toll vary from 4000 to 20,000. The majority of deaths have been in the years since the disaster, as its chronic health effects unfold. Even by conservative estimates, it remains the worst industrial disaster on record, and the victims are still dying. The company paid US$470 million compensation to a trust in 1989. The survivors say they received around US$500 each and claim the cleanup efforts were inadequate. The disaster was initiated when a faulty valve let nearly 1 tonne of water being used to clean pipes pour into a tank holding 40 tonnes of methyl isocyanate. The resulting runaway reaction produced a deadly cloud of toxic gas. The runaway reaction should have been contained but was not, largely because Bhopal had far more limited emergency equipment than was available, for example, in Carbide’s sister US plant. Gasses can be contained by being burned off by flare towers or filtered by a scrubber. At the time of the incident, the Bhopal plant had only one flare, shut for repairs. Bhopal’s sole scrubber was overwhelmed by the mass of liquids and gases that boiled up at a rate over 100 times for which it was designed. Bhopal’s liquid waste was also poured into open lagoons to evaporate. Recent analyses of groundwater, soil and people near the plant have found high levels of heavy metals, such as mercury and toxic organo-chlorine chemicals. Responsibility for the Bhopal incident is contested, with Dow Chemical, which took over Union Carbide, insisting that Carbide’s Indian subsidiary was wholly responsible for the design and running of the plant. In 1999, Bhopal survivors launched a class action in New York State, which led to the court forcing the company to release internal documents, some of which contradicted its claims. In the wake of the disaster, almost two dozen voluntary groups formed to cope with medical relief, supporting the families of victims and organizing a political and legal response to the disaster. This is, in part, a reflection on the lack of preparedness and response capacity that served to heighten the vulnerability of those living near the plant. Sources: Jasanoff, 1994; New Scientist, 2002 …national economic development …can both reduce and generate risk … for society ■ Human-made disasters Most human-made disasters and the highest numbers of people killed are found in Asia and Africa. Data from the Emergency Events Database, Centre for Research on the Epidemiology of Disasters (EM-DAT, CRED) for 1997 to 2006 shows that 1493 human-made disasters were recorded in Asia and 952 in Africa, compared with only 392 events in the Americas, 284 in Europe and 11 in Oceania. The mean number of deaths during this period per event is highest in Oceania (46 deaths). Asia (34 deaths) and Africa (32 deaths) also have high average deaths per event, and this is especially significant given the high numbers of human-made disasters in these two world regions. The Americas (28 deaths) and Europe (24 deaths) recorded the lowest mean number of deaths per event and also the lowest absolute mortality for this time period. Europe is most affected by economic loss, which at over US$10 billion is greater than the economic loss suffered by any other world region. This demonstrates well both the high level of capital investment in Europe and the knock-on effect this has for loss profiled with low mortality and high economic loss. A similar profile is found for natural disasters where high-income countries and regions shift loss from mortality to economic damage. Outside Europe, economic loss is higher for Asia (US$883 million) and Africa (US$830 million), with lower economic loss in the Americas (US$83 million). No economic loss was recorded for events in Oceania. 13 Human-made disasters typically cause less direct loss of life than natural disasters. Worldwide, the mean number of deaths per human-made event (2000 to 2005) is 30, while for natural disasters (excluding drought and forest fire, which are predominantly rural events) this is 225. 14 While direct human loss is lower for human-made disasters, impacts can be felt in the ecosystem and in human health many years after an event, and this loss is seldom recorded in official statistics. One of the most notorious examples of the long-term health consequences of human-made disaster has been the 1984 Bhopal disaster in Madhya Pradesh (India) (see Box 7.3). Here, the accidental release of 40 tonnes of methyl isocyanate from a factory owned by Union Carbide India caused thousands of deaths and injuries. The effects are still being recorded in babies whose parents were exposed to the released gas, so that impacts have crossed generations. National development and disaster loss The relationship between national economic development and natural disaster risk and loss is complicated. It is, however, clear that development can both reduce and generate risk for society and determine who in society carries the greatest burden of risk from natural and human-made hazard. According to an analysis of the influence of development on natural disasters by the United Nations Development Programme (UNDP), countries with a high Human Development Index (HDI) experience low absolute and proportional disaster mortality rates (see Figure 7.5). Small island states such as Vanuatu and St Kitts and Nevis show relatively low absolute mortality, but high mortality as a proportion of population, reflecting the low total populations of these small states. Countries that had experienced a catastrophic disaster during the period for which data were collected (1980 to 2000), such as Armenia and Honduras, also show high losses as a proportion of population.
Disaster risk: Conditions, trends and impacts 175 Deaths per million population (annual mean) 1000 500 100 50 10 5 1 0.5 0.1 0.05 0.01 0.005 Guinea Bissau Norway St Kitts and Nevis Seychelles Bahamas Mauritius Trinidad & Tobago Cyprus Bulgaria Netherlands Iraq Vanuatu Canada Germany Swaziland Djibouti United Kingdom Nigeria Honduras Japan United States Mexico Mozambique Armenia Venezuela Iran India China Sudan DP Korea Ethiopia Bangladesh Low human development Medium human development High human development Figure 7.5 National development status and natural disaster mortality (1980–2000) Source: UNDP, 2004 Note: HDI ranking for Afghanistan, Democratic People’s Republic of Korea, Iraq, Liberia and Yugoslavia are from UNDP Human Development report 1996, all others from UNDP Human Development <strong>Report</strong> 2002. 0.001 0.05 0.1 0.5 1 5 10 50 100 Deaths (annual mean) 500 1000 5000 10,000 50,000 100,000 The UNDP also developed the Disaster Risk Index, a pioneer tool for assessing variations in disaster vulnerability according to levels of development. The index tests 24 socioeconomic variables against disaster mortality for earthquakes, flooding and windstorm at the national level to identify those variables that most explained patterns of loss. For all hazard types, exposure of human populations to hazard-prone places was found to be statistically associated with mortality. Urban growth was also found to be statistically associated with risk of death from earthquakes. This work provides statistical support for the large amount of observational data that connects rapid urban growth with disaster risk, and, in particular, with losses associated with earthquakes. Disaster risks and impacts are also differentiated by levels of development and investments in risk reduction at the city level. City-level comparisons of disaster risk There have been few studies of the global distribution of disaster risk for individual cities. Munich Re’s Natural Hazards Risk Index for Megacities is a rare example (see Table 7.5). 15 The Natural Hazards Risk Index includes 50 participating cities and is primarily designed to compare insurance risk potential. With this caveat in mind, the index database is applied here to build up a picture of disaster risk at the city level. One achievement of the Natural Hazards Risk Index is its multi-hazard approach, covering earthquake, windstorm, flood, volcanic eruption, bush fires and winter damage (frost). Reflecting Munich Re’s business focus, the conceptualization and measurement of vulnerability is restricted to built assets, with an additional measure of financial exposure. The multi-hazard approach is enabled through individual assessments of vulnerability for each hazard type (for building structures and construction and planning regulations), which are then combined with an overall assessment of the general quality of construction and building density in the city to arrive at a risk index. There is some concern over the quality of vulnerability data available for cities; but Munich Re considers the results to be plausible and reflective of expert opinion on city vulnerability and risk. Using Munich Re’s methodology, results show that greatest risk has accumulated in the cities of richer countries. Only one megacity from a non-industrial country, Manila, is in the top ten when cities are ordered by the risk index. 16 With a view to supporting decision-making within the insurance sector, the Natural Hazards Risk Index understandably identifies high exposure in cities with large physical assets and commercial interests. Hence, Tokyo, San Francisco and Los Angeles have the highest Natural Hazards Risk Index values. From a human settlements perspective, Munich Re’s Natural Hazards Risk Index is less instructive than the base data held in Table 7.5. When considering the vulnerability of cities in terms of the sum of different types of natural hazard exposure, high risk becomes associated with Manila, Tokyo, Kolkata, Osaka–Kobe–Kyoto, Jakarta and Dhaka, all cities in excess of 10 million inhabitants and with high exposure to at least two different kinds of natural hazard. There are some counterintuitive results. For example, San Francisco appears low on the list, despite high earthquake exposure, because of low exposure to other hazard types. Munich Re’s data is also useful for identifying those cities where a large natural disaster is most likely to impact negatively upon the national economy. Dhaka, with 60 per cent of national GDP produced within the city, and with high exposure to earthquakes, tropical storms and storm surges, is a strong candidate for a city whose risk has national consequences. The impact of disaster is further differentiated according to the development paths and levels of disaster The impact of disaster is … differentiated according to the development paths and levels of disaster preparedness of individual cities
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ENHANCING URBAN SAFETY AND SECURITY
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First published by Earthscan in the
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INTRODUCTION Enhancing Urban Safety
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ACKNOWLEDGEMENTS The preparation of
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x Enhancing Urban Safety and Securi
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LIST OF FIGURES, BOXES AND TABLES F
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1 CHAPTER CURRENT THREATS TO URBAN
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46 Urban crime and violence Box II.
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54 Urban crime and violence Per 100
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56 Urban crime and violence Burglar
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58 Urban crime and violence Percent
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60 Urban crime and violence Figure
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62 Urban crime and violence Table 3
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76 Urban crime and violence Abused
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78 Urban crime and violence General
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82 Urban crime and violence porate
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112 Security of tenure Box III.1 Se
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5 CHAPTER SECURITY OF TENURE: CONDI
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116 Security of tenure Table 5.1 A
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118 Security of tenure Fully legal
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120 Security of tenure Box 5.4 Secu
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122 Security of tenure Urban tenure
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330 Statistical annex Islands, Micr
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332 Statistical annex NOMENCLATURE
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334 Statistical annex Population, u
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INDEX ACHR (Asian Coalition for Hou
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Index 435 security of tenure 134 co
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Index 437 domestic abuse see intima
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Index 441 Kosovo, security of tenur
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Index 443 participation 38, 296-299
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Index 445 individual 34-35 municipa
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Index 447 gun ownership 78 Homeless
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