Adaptation to flood and drought risk in Austria - VATT
Adaptation to flood and drought risk in Austria - VATT
Adaptation to flood and drought risk in Austria - VATT
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<strong>Adaptation</strong> <strong>to</strong> <strong>flood</strong> <strong>and</strong><br />
<strong>drought</strong> <strong>risk</strong> <strong>in</strong> <strong>Austria</strong><br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
International sem<strong>in</strong>ar on Climate Change Impact<br />
Assessment & <strong>Adaptation</strong>, Hels<strong>in</strong>ki 19 & 20 May 2008<br />
Franz Prettenthaler & Chris<strong>to</strong>ph Töglhofer
<strong>Adaptation</strong> options <strong>to</strong><br />
<strong>flood</strong> <strong>and</strong> <strong>drought</strong> <strong>risk</strong> discussed<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
<strong>flood</strong>s<br />
<strong>drought</strong> & water<br />
scarcity<br />
technical - Pipe l<strong>in</strong>es<br />
f<strong>in</strong>ancial<br />
state relief,<br />
<strong>in</strong>surance<br />
weather<br />
derivatives,<br />
<strong>in</strong>surance<br />
2
Drought & water scarcity<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
3
• <strong>Austria</strong> only uses 3% of its Freshwater<br />
ClimateChange<br />
… not a general problem <strong>in</strong> <strong>Austria</strong><br />
ressources<br />
Causes<br />
Impacts<br />
• Solutions There are regions suffer<strong>in</strong>g from water scarcity<br />
• Some of which with high economic growth<br />
• Exam<strong>in</strong>ed water <strong>in</strong>tensive <strong>in</strong>dustries<br />
• Thorough hydrological model<strong>in</strong>g of the region<br />
• Calculated macroeconomic losses from Water<br />
supply disruptions<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
4
Climate regions <strong>and</strong> stations –<br />
dynamic downscal<strong>in</strong>g (MM5 – 10 km grid)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
mounta<strong>in</strong>ous<br />
region<br />
hilly region<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
5
Temperature change<br />
projection<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
temperature change [°C]<br />
• Scenario on seasonal temperature change<br />
compar<strong>in</strong>g 1981 <strong>to</strong> 1990 <strong>and</strong> 2041 <strong>to</strong> 2050<br />
3,0<br />
2,5<br />
2,0<br />
1,5<br />
1,0<br />
0,5<br />
0,0<br />
1,9<br />
1,8<br />
2,6<br />
2,4 2,4 2,4<br />
DJF MAM JJA SON annual<br />
mean<br />
value<br />
2,7<br />
2,7<br />
2,4<br />
2,3<br />
mounta<strong>in</strong>ous<br />
region<br />
hilly region<br />
DJF: December, January, February<br />
MAM: March, April, May<br />
JJA: June, July, August<br />
SON: September, Oc<strong>to</strong>ber, November<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
6
Precipitation change<br />
projection<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
change <strong>in</strong> precipitation [mm/d]<br />
• Scenario 0,60 on seasonal precipitation change<br />
compar<strong>in</strong>g 0,40 1981 <strong>to</strong> 1990 <strong>and</strong> 2041 <strong>to</strong> 2050<br />
0,20<br />
0,00<br />
-0,20<br />
-0,40<br />
-0,60<br />
-0,80<br />
-1,00<br />
-1,20<br />
-1,40<br />
0,44<br />
0,43<br />
-0,02<br />
-0,11<br />
-0,24 -0,23<br />
-0,13<br />
-0,36<br />
-1,16<br />
-0,95<br />
DJF MAM JJA SON annual<br />
mean value<br />
mounta<strong>in</strong>ous<br />
region<br />
hilly region<br />
DJF: December, January, February<br />
MAM: March, April, May<br />
JJA: June, July, August<br />
SON: September, Oc<strong>to</strong>ber, November<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
7
Precipitation change (SRES A1B<br />
as <strong>in</strong> ECHAM 5/REMO 2035<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
change <strong>in</strong> precipitation [mm/d]<br />
(source: Prettenthaler/Kirschner 2008)<br />
8
Projection of<br />
groundwater recharge reduction<br />
Scenario:<br />
1981-1990 mean temperature <strong>and</strong> relative humidity<br />
2041-2050 reduced percipitation,<br />
Reduction of groundwater recharge [%]<br />
higher Causes temperature <strong>and</strong> <strong>in</strong> general<br />
lower relative humidity* 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br />
ClimateChange<br />
Impacts<br />
Solutions<br />
year or area with low<br />
percipitation; soil with low<br />
avaiable water<br />
change <strong>in</strong> precipitation [mm/d]<br />
year or area with mean<br />
percipitation; soil with low<br />
avaiable water<br />
year or area with high<br />
percipitation; soil with low<br />
avaiable water<br />
year or area with low<br />
percipitation; soil with<br />
mean avaiable water<br />
year or area with mean<br />
percipitation; soil with<br />
mean avaiable water<br />
year or area with high<br />
percipitation; soil with<br />
mean avaiable water<br />
maize<br />
grasl<strong>and</strong><br />
(source: Prettenthaler/Dalla-Via 2007)<br />
9
Economic costs of<br />
non-adaptation<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Economic data on water <strong>in</strong>tensive <strong>in</strong>dustries<br />
• employees: 20 700 (300.000 <strong>in</strong>habitants)<br />
• annual production value: € 2.7 Billion<br />
• annual gross value added: € 1 Billion<br />
• Short run: 2-week loss of production caused by water scarcity<br />
• loss <strong>in</strong> production value: € 105 Million<br />
• loss <strong>in</strong> gross value added: € 40 Million<br />
• metal <strong>in</strong>dustry (PV: € 21 M, GVA: € 7 M)<br />
• food production (PV: € 24 M, GVA: € 8 M)<br />
• <strong>to</strong>urism <strong>in</strong>dustry (PV: € 21 M, GVA: € 9 M)<br />
• Long run: S<strong>to</strong>p of <strong>in</strong>vestment <strong>in</strong> the water <strong>in</strong>tensive <strong>in</strong>dustries<br />
• will cost about 6.000 jobs until 2020 (multiregional <strong>and</strong><br />
multisec<strong>to</strong>ral econometric model)<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
10
Economic evaluation of<br />
adaptation options (supply side)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Adaptive strategies on the supply-side<br />
• connect<strong>in</strong>g all regional pipe networks (pool<strong>in</strong>g)<br />
• <strong>in</strong>vestment <strong>in</strong> a pipel<strong>in</strong>e that connects Graz (supplied by<br />
pipel<strong>in</strong>e from abundant alp<strong>in</strong>e spr<strong>in</strong>gs) <strong>to</strong> Hartberg<br />
• Assessment of<br />
• direct<br />
• <strong>in</strong>direct<br />
• <strong>in</strong>duced<br />
effects of the <strong>in</strong>vestment on the national economy<br />
• Assessment method<br />
• MultiREG: multiregional <strong>and</strong> multisec<strong>to</strong>ral econometric model<br />
11
Economic evaluation of<br />
adaptation options (dem<strong>and</strong> side)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Assessed options<br />
• expansive water price policy<br />
• implementation of water sav<strong>in</strong>g technologies <strong>in</strong> households<br />
• implementation of water sav<strong>in</strong>g technologies <strong>in</strong> hotels<br />
• Water sav<strong>in</strong>g technologies<br />
• devices for showers<br />
• <strong>to</strong>ilettes<br />
• water taps<br />
• Method used: cost efficiency analysis<br />
• Water price elasticity of dem<strong>and</strong>: % ΔQ<br />
ε<br />
p<br />
= = −0,25<br />
% ΔP<br />
(source: Prettenthaler/Dalla-Via 2007)<br />
12
Conclusions<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Compar<strong>in</strong>g with supply-side adaptive strategies, dem<strong>and</strong>-side<br />
options reveal <strong>to</strong> be <strong>in</strong>sufficient<br />
• Rough predictions of hydrologists about additional future water<br />
dem<strong>and</strong> <strong>in</strong> times of peak load is about 200 l/s<br />
Dem<strong>and</strong>-side options are a good contribution, but can not<br />
cover expected dem<strong>and</strong>s<br />
Realization of supply-side adaptive strategies necessary<br />
• As justified by our study, 60 Million € are now be<strong>in</strong>g <strong>in</strong>vested<br />
<strong>in</strong> new pipel<strong>in</strong>es (1 major from outside the region)<br />
13
<strong>Adaptation</strong> options <strong>to</strong><br />
<strong>flood</strong> <strong>and</strong> <strong>drought</strong> <strong>risk</strong> discussed<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
<strong>flood</strong>s<br />
<strong>drought</strong>s<br />
technical - Pipe l<strong>in</strong>es<br />
f<strong>in</strong>ancial<br />
state relief,<br />
<strong>in</strong>surance<br />
weather<br />
derivatives,<br />
<strong>in</strong>surance<br />
14
F<strong>in</strong>ancial strategies for mitigat<strong>in</strong>g<br />
weather <strong>and</strong> climate <strong>risk</strong>s<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
time horizon <strong>risk</strong> strategy<br />
hours, days<br />
months, years<br />
decades<br />
extreme weather<br />
events<br />
unusual seasonal<br />
variations<br />
long-term climate<br />
variations<br />
Insurance, CATbonds<br />
weather derivatives,<br />
<strong>in</strong>surances<br />
very long-term<br />
hedg<strong>in</strong>g contracts<br />
(source: Dut<strong>to</strong>n 2002, p. 1304)<br />
15
Weather derivatives –<br />
basic pr<strong>in</strong>ciple<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• F<strong>in</strong>ancial <strong>in</strong>struments <strong>to</strong> hedge weather <strong>risk</strong>s<br />
• based on the pr<strong>in</strong>ciple of conventional derivatives,<br />
such as commodity, equity or <strong>in</strong>terest rate derivatives<br />
• Underly<strong>in</strong>g asset: weather <strong>in</strong>dex<br />
• Hedge quantity <strong>risk</strong>s <strong>in</strong>stead of price <strong>risk</strong>s<br />
• Trade: S<strong>to</strong>ck Exchange (CME etc.) vs. OTC (Over<br />
the Counter)<br />
• Advantage compared <strong>to</strong> <strong>in</strong>surances:<br />
• No costs for loss settlement<br />
• No moral hazard<br />
16
Collar – Basic Pr<strong>in</strong>ciple<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
Weather <strong>in</strong>dex Meteorological station(s) Time Period<br />
Tick Size Strike Level Cap<br />
(source: Prettenthaler/Töglhofer 2006)<br />
17
The weather market<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Notional value<br />
- 2.5 billion US-Dollars <strong>in</strong> 2000/01<br />
- 9.7 billion US-Dollars <strong>in</strong> 2004/05<br />
- 45.2 billion US-Dollars <strong>in</strong> 2005/06.<br />
• CME<br />
- trade volume exploded<br />
- entrance of hedge funds <strong>and</strong> other<br />
f<strong>in</strong>ancial <strong>in</strong>ves<strong>to</strong>rs<br />
• Over the Counter<br />
(OTC)<br />
- trade volume has not changed<br />
- shift from temperature <strong>to</strong>wards nontemperature<br />
$50,000<br />
$45,000<br />
$40,000<br />
$35,000<br />
$30,000<br />
$25,000<br />
$20,000<br />
$15,000<br />
$10,000<br />
$5,000<br />
$0<br />
100%<br />
2000/1 2001/2 2002/3 2003/4 2004/5 2005/6<br />
Total Notional Value (<strong>in</strong> millions of U.S. dollars)<br />
80%<br />
60%<br />
40%<br />
20%<br />
0%<br />
2000/1 2001/2 2002/3 2003/4 2004/5 2005/6<br />
Source: PwC 2006<br />
CME W<strong>in</strong>ter<br />
CME Summer<br />
OTC W<strong>in</strong>ter<br />
OTC Summer<br />
Other<br />
Ra<strong>in</strong><br />
Other Temp<br />
CDD<br />
HDD<br />
18
The weather market<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Energy: ma<strong>in</strong>ly <strong>to</strong> hedge dem<strong>and</strong> side weather <strong>risk</strong>s (oil, gascompanies)<br />
• Agriculture: US driven, but also delevop<strong>in</strong>g countries (India etc.)<br />
• Others: e.g. Snow Derivatives<br />
4%<br />
26%<br />
5%<br />
46%<br />
energy<br />
agriculture<br />
retail<br />
construction<br />
transport<br />
other<br />
7%<br />
12%<br />
source: PwC 2006<br />
19
Risk transfer<br />
<strong>in</strong> the agricultural sec<strong>to</strong>r<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• The role of the <strong>Austria</strong>n Catastrophe Fund<br />
• Multiple crop <strong>in</strong>surances<br />
• Public subsidies of <strong>in</strong>surance premiums<br />
• Adverse Selection<br />
• Moral Hazard<br />
20
Design<strong>in</strong>g weather derivatives <strong>to</strong><br />
hedge <strong>drought</strong> <strong>risk</strong> (grassl<strong>and</strong>)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Positive correlation between harvest <strong>and</strong> precipitation <strong>in</strong><br />
June <strong>and</strong> July<br />
• Negative correlation between harvest <strong>and</strong> temperature<br />
from Mai <strong>to</strong> August<br />
• Generate best suitable (simple!) weather <strong>in</strong>dex (us<strong>in</strong>g<br />
mean values <strong>and</strong> st<strong>and</strong>ard deviation):<br />
Index(P,T) = 0,5(P - ηP)/σP + 0,5(ηT - T)/σT<br />
21
Weather derivatives & <strong>drought</strong> <strong>risk</strong><br />
ClimateChange<br />
Example: Hedg<strong>in</strong>g Grassl<strong>and</strong> yields with weather derivatives<br />
Causes<br />
Impacts<br />
Solutions<br />
returns (per hectar)<br />
€ 400.00<br />
€ 350.00<br />
€ 300.00<br />
€ 250.00<br />
€ 200.00<br />
€ 150.00<br />
€ 100.00<br />
€ 50.00<br />
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39<br />
Cases (sorted by returns without buy<strong>in</strong>g derivatives)<br />
without buy<strong>in</strong>g derivatives precipitation derivative (1 function) precipitation derivatives (2 functions)<br />
weather <strong>in</strong>dex derivative (1 function) weather <strong>in</strong>dex derivative (2 functions)<br />
His<strong>to</strong>rical distribution of returns (under constant prices)<br />
Volatility is lowered by derivatives (put options), but basis <strong>risk</strong> still rema<strong>in</strong>s<br />
22
Drought <strong>risk</strong>:<br />
technical adaptation strategies<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Irrigation<br />
• Water supply <strong>and</strong> dem<strong>and</strong> management<br />
• Breed selection, GM<br />
• Soil management<br />
23
Weather derivatives vs.<br />
technical adaptation strategies<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Advantages<br />
• Technical adaptation strategies: Higher yields under adverse weather<br />
conditions<br />
(rema<strong>in</strong><strong>in</strong>g <strong>risk</strong>: i. e. water supply for irrigation)<br />
• Weather derivatives: Compensation payments under adverse weather<br />
conditions<br />
(rema<strong>in</strong><strong>in</strong>g <strong>risk</strong>: basis <strong>risk</strong>)<br />
• Problems<br />
• Technical adaptation strategies often face high <strong>in</strong>vestment costs<br />
• Premia for weather derivatives depend on liquidity of the market, quality<br />
of weather data etc. & transaction costs (cost for weather data etc.)<br />
• Risk premium will <strong>in</strong>crease, if (perceived) probability of adverse weather<br />
conditions <strong>in</strong>creases (climate change)<br />
• Flexibility<br />
• Low for most technical adaptation strategies (long term decisions)<br />
• High for weather derivatives (can be bought on a seasonal/annual basis)<br />
24
Weather Derivatives &<br />
Hydropower<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Weather derivatives <strong>to</strong> hedge revenues of small<br />
hydro power plants<br />
80<br />
70<br />
R 2 = 0,6702<br />
Monthly runoff rate<br />
(<strong>in</strong> mm/month)<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
€ - € 1.000 € 2.000 € 3.000 € 4.000 € 5.000 € 6.000 € 7.000<br />
monthly revenues (feed-<strong>in</strong>-tariff: 0,06€/kWh)<br />
25
Weather Derivatives &<br />
Hydropower<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Small scale hydro power<br />
• Several examples, also <strong>in</strong> <strong>Austria</strong> (large energy supply company<br />
hedge weather <strong>risk</strong> of several smaller owners of small hydro<br />
power plants)<br />
• Central issue: Transaction costs, because transaction sizes<br />
(notional values) are relatively low<br />
• Large scale hydro power<br />
• huge potential - modest dem<strong>and</strong><br />
• negative correlation between market prices <strong>and</strong> hydropower<br />
generation<br />
• diversified generation portfolios<br />
26
<strong>Adaptation</strong> options <strong>to</strong><br />
<strong>flood</strong> <strong>and</strong> <strong>drought</strong> <strong>risk</strong> discussed<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
<strong>flood</strong>s<br />
<strong>drought</strong>s<br />
technical - Pipe l<strong>in</strong>es<br />
f<strong>in</strong>ancial<br />
state relief,<br />
<strong>in</strong>surance<br />
weather<br />
derivatives,<br />
<strong>in</strong>surance<br />
27
Floods<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
28
Increas<strong>in</strong>g awareness <strong>in</strong> Europe<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Losses from <strong>flood</strong>s <strong>in</strong> Europe 2002: 13 billion €<br />
• CH, D, AT <strong>in</strong> 2005: 1,7 billion<br />
• 15 major <strong>flood</strong>s per year<br />
• ABI models predict losses from s<strong>to</strong>rm surge <strong>and</strong><br />
heavy precipitation of up <strong>to</strong> 120-150 billion $<br />
29
Trends <strong>in</strong> economic <strong>and</strong> <strong>in</strong>sured losses<br />
from natural catastrophes<br />
(<strong>in</strong>cludes also stroms & earthquake)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
Source: Munich Re<br />
30
Normalization matters !<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Pielke <strong>and</strong> L<strong>and</strong>sea (1998), pielke (2008)<br />
adjust<strong>in</strong>g Hurricane Damage (US) for<br />
• Inflation<br />
• Wealth<br />
• Population<br />
• Coll<strong>in</strong>s <strong>and</strong> Lowe (2005) adjust<strong>in</strong>g Hurricane<br />
Damage (US) for<br />
• Inflation<br />
• Wealth<br />
• Hous<strong>in</strong>g units<br />
• Prettenthaler <strong>and</strong> Albrecher (2008) adjust<strong>in</strong>g<br />
<strong>flood</strong> damages (<strong>Austria</strong>)<br />
• Inflation<br />
• Hous<strong>in</strong>g units<br />
31
Flood Zones & Hous<strong>in</strong>g<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
(source: Prettenthaler/Albrecher 2008)<br />
32
Share of HQ 30 zones<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
33
Economic value <strong>in</strong><br />
HQ 30 zones<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
34
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
35<br />
Payments from<br />
national cat fund<br />
• Compensation on private property<br />
180<br />
160<br />
140<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
-<br />
1966<br />
1968<br />
1970<br />
1972<br />
1974<br />
1976<br />
1978<br />
1980<br />
1982<br />
1984<br />
1986<br />
1988<br />
1990<br />
1992<br />
1994<br />
1996<br />
1998<br />
2000<br />
2002<br />
2004<br />
2006<br />
In Mio. €<br />
(source: Prettenthaler/Albrecher 2008)
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
36<br />
Inflation adjusted<br />
• Build<strong>in</strong>g cost st<strong>and</strong>ard adjustment<br />
2,5<br />
2,0<br />
1,5<br />
1,0<br />
0,5<br />
Schaden<br />
Gebäude<br />
200<br />
180<br />
160<br />
140<br />
120<br />
100<br />
80<br />
60<br />
40<br />
0,0<br />
1966<br />
1969<br />
1972<br />
1975<br />
1978<br />
1981<br />
1984<br />
1987<br />
1990<br />
1993<br />
1996<br />
1999<br />
2002<br />
2005<br />
Zahl der Gebäude <strong>in</strong> Mio.<br />
Schäden <strong>in</strong> Mio. €<br />
20<br />
-<br />
(source: Prettenthaler/Albrecher 2008)
Normalized damages<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
normierter<br />
Schaden<br />
1.0<br />
0.8<br />
0.6<br />
0.4<br />
0.2<br />
1970 1980 1990 2000<br />
Jahr<br />
(source: Prettenthaler/Albrecher 2008)<br />
37
Check<strong>in</strong>g for <strong>in</strong>surability (does the<br />
mean value converge)<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
Mittelwert<br />
0.25<br />
0.20<br />
0.15<br />
0.10<br />
0.05<br />
10 20 30 40 k<br />
38
Further analysis<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Heavy tails distribution (only two observations <strong>in</strong> the<br />
tail)<br />
• Fit of a change po<strong>in</strong>t distribution <strong>to</strong> the empirical data<br />
• Average annual loss 179 Mio. € (model), 183 Mio. €<br />
(empirically)<br />
• Cutt<strong>in</strong>g off the distribution @ 3 Billion. €:<br />
• Average annual loss 157 Mio. € (model), 161 Mio. € (empirically)<br />
39
RTM <strong>Austria</strong><br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• private <strong>in</strong>surance market + public compensation fund<br />
• private <strong>in</strong>surance market:<br />
• low <strong>in</strong>surance penetration<br />
• very limited coverage<br />
• extended coverage only outside <strong>flood</strong> <strong>risk</strong> areas<br />
• Premiums are <strong>risk</strong> <strong>in</strong>dependent<br />
• catastrophe fund:<br />
• tax f<strong>in</strong>anced<br />
• compensation <strong>and</strong> prevention<br />
• compensation is limited<br />
• Problems<br />
• Market failure <strong>in</strong>creased by public <strong>in</strong>tervention<br />
• Clear PPP sollution needed<br />
40
How <strong>to</strong> adapt<br />
ClimateChange<br />
Causes<br />
Impacts<br />
Solutions<br />
• Build<strong>in</strong>g dams is not enough: design better <strong>risk</strong><br />
transfer<br />
• Better quantify <strong>risk</strong> (zon<strong>in</strong>g)<br />
• Information provided by PPP<br />
• Obliga<strong>to</strong>ry elements needed<br />
• Public Private Partnerships (PPPs: F, E; plans: B, I, AT,<br />
ROM)<br />
• ART (transfer <strong>risk</strong> <strong>to</strong> f<strong>in</strong>ancial markets, cat bonds)<br />
41
Cross country comparison<br />
of RTM‘s (1)<br />
ClimateChange<br />
Germany France CH (CPI) CH (Priv.) Spa<strong>in</strong> Turkey USA<br />
Causes<br />
National<br />
private private Can<strong>to</strong>nal Private<br />
Insurance<br />
public public Flood<br />
Impacts<strong>in</strong>surance<br />
<strong>in</strong>surance Property <strong>in</strong>surance<br />
Carrier<br />
corporation corporation Insurance<br />
Solutions<br />
companies companies Insurance companies<br />
Programme<br />
Monopoly No No Yes No Yes Yes No<br />
M<strong>and</strong>a<strong>to</strong>ry<br />
<strong>in</strong>surance<br />
Obligation <strong>to</strong><br />
contract<br />
Bundle of<br />
natural<br />
hazards<br />
Role of the<br />
state<br />
No<br />
m<strong>and</strong>a<strong>to</strong>ry<br />
extension of<br />
coverage<br />
au<strong>to</strong>matic<br />
extension of<br />
coverage<br />
m<strong>and</strong>a<strong>to</strong>ry<br />
extension of<br />
coverage<br />
compulsory<br />
<strong>in</strong>surance<br />
(subsidiary)<br />
compulsory<br />
<strong>in</strong>surance<br />
No (Yes) Yes (Yes) Yes Yes Yes<br />
Yes Yes Yes Yes Yes No No<br />
ad hoc relief<br />
unlimited<br />
state<br />
guarantee<br />
for CCR<br />
unlimited<br />
Keep<br />
monopolies - state<br />
guarantee<br />
-<br />
No<br />
state<br />
guarantee<br />
(loan of up <strong>to</strong><br />
1,5 billion<br />
US$)<br />
42
Cross country comparison<br />
of RTM‘s (2)<br />
ClimateChange<br />
Germany France CH (CPI) CH (Priv.) Spa<strong>in</strong> Turkey USA<br />
Causes<br />
<strong>risk</strong>-related,<br />
Premium<br />
uniform plus<br />
Impacts <strong>risk</strong>-related uniform<br />
design<br />
<strong>risk</strong> load<strong>in</strong>g uniform uniform plus<br />
<strong>risk</strong> load<strong>in</strong>g <strong>risk</strong>-related part.<br />
Solutions<br />
subsidized<br />
Coverage<br />
re<strong>in</strong>surance re<strong>in</strong>surance<br />
state<br />
reserve fund, <strong>in</strong>surance<br />
(optionally plus <strong>in</strong>surance<br />
guarantee<br />
aga<strong>in</strong>st<br />
unlimited pool, <strong>in</strong>tern.<br />
re<strong>in</strong>surance with CCR), <strong>in</strong>surance pool plus<br />
(loan of up <strong>to</strong><br />
catastrophic<br />
losses<br />
guarantee markets<br />
state capital<br />
state pool (IRV, re<strong>in</strong>surance<br />
1,5 billion<br />
guarantee IRG)<br />
US$)<br />
Further<br />
issues<br />
low<br />
<strong>in</strong>surance<br />
penetration<br />
CCR<br />
confronted<br />
with adverse<br />
selection<br />
CPI actively<br />
<strong>in</strong>volved <strong>in</strong><br />
prevention<br />
Compatibility<br />
with EU<br />
legislation<br />
adverse<br />
selection is a<br />
big problem<br />
43
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