Transcript Weather Index-based Insurance in China

Weather
Index-based Insurance in China:
1- Introduction
2- Data screening to determine a pilot area
3- Data collection
4- Data processing
Su Buda, [email protected]
1
Introduction (1)
• Background:
- Growing frequency and severity of extreme weather events in
China have caused very high economic losses. But Lack of
modern risk management mechanisms to cope with these perils.
direct economic losses caused by weather disaster during 1990-2010
(173.2×109 RMB per year )
2
Introduction (2)
• Developing a weather-index based
insurance implies:
• Collecting and analyzing weather/climate
and loss data to find a trigger (farmers
will receive insurance payouts once a
certain trigger ,such as a certain
precipitation or water levels for a flood
insurance is reached.)
3
Data screening to determine a
pilot area (1)
• Identification of areas in China suitable for developing
and launching weather index based insurance
• According to the following criteria:
- Weather data available for at least 30 years
- Significant number of weather stations
- Existence of significant weather risks which cause losses for the
local economy
- The risk is independent
- High population density/Large number of potential clients
• Result: Fujian province was chosen as a pilot area
4
Data screening to determine a
pilot area (2)
Percentage of population affected:
Percentage of losses in GDP
stemmed from droughts
: stemmed from heavy precipitation
Data screening to determine a
pilot area (3)
614 national meteorological stations with at
Potential target area for study :
least 30-years observational record
582 national meteorological stations with at (tropical) Cyclone: high number of
population, losses, large market,
least 40-years observational record
435 national meteorological stations with at station density, triggers ‘easy’
least 50-years observational record
Data collection: Weather data (1)
• Daily wind and daily precipitation were collected for the
period 1961-2009 for the 66 climate stations of CMA based
in Fujian province
• Other sources providing weather data: Office of Flood
Control and Drought Relief, Agricultural Bureau, Forestry
Bureau, etc.
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Data collection: Losses data (2)
• In China, damage and loss data related to weather/climateinduced disasters are recorded by CMA on county-level
• The records of damages and losses are mainly based on
official reports from county governments
• For Fujian province: record of past typhoon events over the
last 50 years compiled by the Fujian Climate Center
Types of economic losses (in 10,000 RMB)
Data availability
Direct economic losses
91.8%
Agricultural losses
35.8%
Livestock economic losses
0.57%
Economic losses related to water
14%
Industrial economic losses
4.2%
Economic losses related to forestry
1.5%
Economic losses related to fishery
4.7%
Economic losses related to traffic
12.9%
Economic losses related to electricity
5.7%
Economic losses related to communication
1.1%
Economic losses related to infrastructures
2.3%
Economic losses related to commerce
0.2%
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Data processing (1)
•
Trigger estimation
Relationship of heavy precipitation index
and losses rate
Risks of heavy precipitation on tobacco
in Longyan County
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Data processing (2)
• Challenges
- Challenge in obtaining comprehensive,
reliable and harmonized loss data
- Change of climate station’s location or
instrument might affect time-series
- Density of climate station plays a crucial
role
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Data processing (3)
• Solutions
-
Weather/climate data might have to be interpolated between stations of different
altitude；Interpolate the results and check the reliability in backup stations;The
homogeneity of data has to be checked
-
Interaction with local stakeholders important to identify local needs for an
insurance product；（Validation of triggers and thresholds；Re-adjustment of
thresholds based on insurer’s needs）
Lianjiang Station
58844
5884
5
5884
7
589
41
0.776
0.803
0.831
0.82
2
daily precipitation
1984-2007
588
48
daily maximum wind
1984-2007
588
48
0.424
0.571
0.577
0.33
0
daily precipitation
(typhoon)
588
48
0.617
0.749
0.90
4
0.69
3
daily maximum wind
(typhoon)
588
48
0.672
0.85
4
0.824
0.42
3
If a station fails, the trigger and threshold can be
determined by back-up station
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Thank you
谢谢