This document summarizes a study that analyzed multi-risk to human life at the county level in the Yangtze River Delta region of China. The study developed a method to assess multi-hazard risk, vulnerability, exposure, and overall multi-risk to human life. It calculated multi-hazard, vulnerability, and exposure indices for 140 county-level cities using indicators like historical hazard impacts, medical access, infrastructure, demographics. The results showed the highest multi-risk areas were in the northeast, south, and Shanghai area.

1. Analysis of multi-risk to human life at county level in the Yangtze River Delta Baoyin LIU, Wei XU Beijing Normal University baoyin@ires. cn Funded by the Project in the National Science & Technology Pillar Program (2008BAK50B07)

3. 1. Introduction Global natural catastrophes 1980 – 2008 Overall and insured losses with trend Munich Re, 2009

5. Flood Earthquake Typhoon Other hazards Based on single-hazard risk assessment, put different types of hazards into a system for comprehensive evaluation. Establish the Multi-risk insurance system Enhancing the risk awareness of local government

6. Multi-risk assessment It required a lot very detailed data which were hard to collect; It calculated the multi-risk by aggregating all single-hazard risk with equal weight, the results were not so reliable. TEMRAP(The European Multi-Hazard Risk Assessment Project) There is no attention to exposed elements and vulnerability. DDRM It required a lot very detailed data which were hard to collect. JRC It calculated mortality and economic losses in grid cell; It calculated the multi-risk by aggregating all single-hazard risk with equal weight, the results were not so reliable. World Bank Methodology -Natural Disaster Hotspots The analysis is limited by availability and quality of historical data on the incidence of hazards; It considers only a limited number of hazards. FEMA It calculated the multi-risk by aggregate all single-hazard risk with equal weight; It suits for a small-scale analysis due to the data collection; It took probability of spatial impact and probability of seasonal occurrence into account University of Bonn It calculated the multi-hazard index by aggregate all hazards with equal weight. Calculation of the Total Place Vulnerability Index in the State of South Carolina It required a lot very detailed data which were hard to collect; The analysis is limited by issues of scale Munich Re – Natural Hazard Index for Mega Cities It used Delphi method to determine the weight and required a lot very detailed data which were hard to collect ESPON 2006 Assessment model Remarks Approaches

7. Yangtze River Delta Area: 99,600 sq. km Population: 85 million 18.7% of GDP, 22% of financial revenue, and 18.4% of export trade (as of 2004) Main hazards: floods, typhoon, earthquake

8. 2.Multi-risk assessment to human life at county level in the Yangtze River Delta 2.1 The multi-risk assessment method Typhoon Flood Earthquake Gender ratio Age structure Traffic condition Telecommunication Medical condition Population density Multi-hazard Vulnerability Exposure Multi -risk to human life

10. Weight of evaluation system for Multi-hazard 0.0033 55 Earthquake 0.4141 5659 Flood 0.5826 7049 Typhoon Weight( w a ) decided by t he average historical annual percent of death Death toll Hazards

11. Multi-hazard Index(1949-2000) Higher Area: south-eastern Affected frequently by Typhoon: Taizhou**, Zhoushan, Ningbo Affected frequently by Flood: Shanghai, Hangzhou, Suzhou.

12. Evaluation Indicators for Vulnerability 2.3 Vulnerability analysis Per medical institution coverage area No. of hospital beds per 10,000 persons No. of doctors per 10,000 persons Medical condition No. of mobile phone per capita No. of internet per capita Telecom- munication Road length (km) per square kilometer Road length (km) per 10,000 persons Traffic condition Over 15 but under 65 Age structure Ratio of male to female Gender Indicator Factor

13. The weight ( w i ) of each indicators are calculated through the entropy method 0.4182 0.0615 0.0420 0.8658 0.9803 0.9865 Per medical institution coverage area No. of hospital beds per 10,000 persons No. of doctors per 10,000 persons Medical condition 0.0977 0.2525 0.9687 0.9190 No. of mobile phone per capita No. of internet per capita Telecom- munication 0.0700 0.0576 0.9775 0.9815 Road length (km) per square kilometer Road length (km) per 10,000 persons Traffic condition 0.0002 0.99994 Over 15 but under 65 Age structure 0.0003 0.99991 Ratio of male to female Gender Weight( w i ） Entropy ( e j ) Indicator Factor

14. Human Life Vulnerability Index Higher Area: northern and southern Locate far away from the metropolitans, with low traffic network, telecommunication cover rate, and poor medical condition.

15. 2.4 Exposure analysis The population density of 140 county-level city are taken into normalization to get the exposure index. Human Life Exposure Index Higher Area: north-eastern Highest Area: Shanghai

16. 2.5 Multi-hazard risk analysis H V E

17. Multi-hazard risk index of Human Life Higher Area: northeast southern Shanghai Lower Area: northwest

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