Catastrophic Climate Risks and Insurance
Arthur Charpentier (UQAM & Univ. Rennes)
Groupama, September 2022

September 15, 2022

@freakonometrics

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Arthur Charpentier
Université du Québec à Montréal@freakonometrics
freakonometrics
freakonometrics.hypotheses.org
Predictive Modeling, Actuarial Science,
Mathematical Economics, Risk, Inequalities,
Econometrics, Statistics, Machine Learning
Climate Modeling, Extremes, Fairness@freakonometrics

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Dynamics of Natural Catastrophes (incl. Climate) [1][1]

A. Charpentier. “On the return period of the 2003 heat wave”. In: Climatic change 109.3 (2011),pp. 245–260.

[2]

A. Charpentier and D. Sibaı̈. “Dynamic flood modeling: combining Hurst and Gumbel’sapproach”. In: Environmetrics 20.1 (2009), pp. 32–52.

@freakonometrics

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Dynamics of Natural Catastrophes (incl. Climate) [2]@freakonometricsfreakonometrics

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Dynamics of Natural Catastrophes (incl. Climate) [3]“seismic gap hypothesis” / dynamic of flood events / heat wave persistence@freakonometrics

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Flood Risk in France [1][1]

A. Charpentier, L. Barry, and M. James. “Insurance against Natural Catastrophes: Balancing
Actuarial Fairness and Social Solidarity”. In: Geneva Papers on Risk & Insurance (2021). doi:10.1057/s41288-021-00233-7.

[2]

France Info. “Seine-et-Marne : la ville de Thoméry se remet progressivement des inondations”.
In: (2018). url: http://tinyurl.com/mtc9tprm.

@freakonometrics

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Flood Risk in France [2][1]A. Charpentier, L. Barry, and M. James. “Insurance against
Natural Catastrophes: Balancing Actuarial Fairness and Social Solidarity”. In: Geneva Papers on Risk & Insurance(2021). doi: 10.1057/s41288-021-00233-7.

On fairness & solidarity
French Constitution (1946)
12. La Nation proclame la solidarité et l’égalité detous les Français devant les charges qui résultent
des calamités nationales.
82-600 Law (1982)
régime d’indemnisation des catastrophes naturelles@freakonometrics

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Flood Risk in France [3]source: https://www.ccr.fr/en/-/indemnisation-des-catastrophes-naturelles-en-france@freakonometrics

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Flood Risk in France [4]Two different flood perils : overflow vs. coastal
PPRIs (plan de prévention du risque inondation) in Roquebrune-sur-Argens, Puget andSaint-Raphaël. The plain area (in blue) is the risky area.

Areas clearly identified as risky, from documented (historical) floods.

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Flood Risk in France [5][1]France 3. “Tempête Xynthia: la responsabilité de La Faute-sur-Mer pourrait être alourdie”. In:(2019).

@freakonometrics

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Flood Risk in France [6]PPRLs (plan de prévention des risques littoraux) in Vendée. The dashed area is therisky area. Areas with possible coastal risk.

See  https://github.com/freakonometrics/floods@freakonometrics

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Flood Risk in France [7]10% of households represent 73.6% of the losses... who lives in those risky areas ?
“ventes de biens fonciers” dataset, https://cadastre.data.gouv.fr/dvf, 2014-2018,(possible bias on those 5-year notarial transactions...)@freakonometrics

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Flood Risk in France [8]E.g. in 4 “departements” (Loire-Atlantique, Vendée, Oise, Isère)• sold houses / apartments, ■ PPRI-PPRL areas@freakonometrics

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Flood Risk in France [9]Pays-Loire

Vendée

Table 1: coastal risk areas vs. Table 2: overflow / non-costal risk areas

Non risky
Risky
Non risky
Risky

Apartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
4293
2928
3302
10253
4399
3019
6784
3245

Difference(%)-23%+250%+54%+7%Maximum
Price
21840
65909
9773
71483
79913
75472
68478
22895

Number
329
2795
39637
8411
12678
1001
765

Proportion
(%)
9%
74%
1%
17%
37%
55%
4%
3%

Welcht test

Table 1: Prices (e per m2 ) of houses sold (2014-2018) for Vendée - Western part of France,with PPRL (coastal risk). The Difference is the relative difference between average prices (perm2 ) between the risky and the non-risky zones, either for apartments or houses.

@freakonometrics

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1.0
-60.1

-8.6
-2.7

Flood Risk in France [10]Var

Non risky
Risky

Seine etMarne

HauteLoire

Non risky@freakonometrics

Risky
Non risky
Risky

freakonometricsApartments
HousesApartments
HousesApartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
5392
5957
4190
4172
2399
1314
2163
1247
6260
3356
4333
2693

Difference(%)

MaximumPrice-22%-30%-11%-5%-30%-20%

38333
20625
28125
7432
79710
79167
40000
54096

Number
9874
6913
1471
226
3403
8857
319
272
82133
98824
2177
1784

freakonometrics.hypotheses.org- Arthur Charpentier, UQAM, September 2022

Proportion
(%)
53%
37%
8%
1%
27%
69%
2%
2%
44%
53%
1%
1%

15 / 27Welch
t value

6.45.2

1.6
0.9

8.0
7.5

Flood Risk in France [11]IsèreNon risky
Risky

Oise

Non risky
Risky

Apartments
HousesApartments
HousesApartments
HousesApartments
HousesAverage
Price
4960
2429
3252
2543
6170
3126
5725
2866

Difference(%)-3%+5%-7%-8%

Maximum
Price
79800
69375
35714
14067
79963
78214
50000
62184

Number
27982
24600
885
435
24613
44737
1385
1640

Proportion
(%)
52%
45%
2%
1%
34%
62%
2%
2%

Welch
t value

Table 2: Prices (e per m2 ) of houses sold (2000-2020) for several départements in France, withPPRI (overflow risk, or non-costal).

@freakonometrics

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6.1
-1.2

2.1
4.6

Subsidence Risk in France [1][1]

A. Charpentier, M. R. James, and H. Ali. “Predicting Drought and Subsidence Risks in France”.
In: Natural Hazards and Earth System Sciences 22 (2022), pp. 2401–2418. doi:
10.5194/nhess-22-2401-2022.

[2]

France Bleu. “La sécheresse coûte de plus en plus cher en assurances”. In: (2019). url:
http://tinyurl.com/yeyr67xu.

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Subsidence Risk in France [2]Joint work with Hani Ali (Willis Re) and Molly James (EURIA / ACPR).
[1]

A. Charpentier, M. R. James, and H. Ali. “Predicting Drought and Subsidence Risks in France”.
In: Natural Hazards and Earth System Sciences 22 (2022), pp. 2401–2418. doi:
10.5194/nhess-22-2401-2022.

“Subsidence is caused by the shrinkage and swelling of clay soils”
Geotechnical factor: Area of municipalities at medium or high risk > 3%.
(categories based on clay concentration in the soil and historical statements)
Meteorological factor: Standardized soil moisture index (SSWI), if an indicator of
the season is lower than a return period of 25 years, then the whole season iseligible for the commune concerned.

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Subsidence Risk in France [3]Data 1989-2018@freakonometrics

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Subsidence Risk in France [4]Indicators 2020: ESPI (precipitation) ESSTI (soil temperature) & ESSWI (soilhumidity), ERA5-Land 9 km × 9 km

(via ESDAC (European Soil Data Centre) for soil concentration)@freakonometrics

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Subsidence Risk in France [5]Regression models for frequencies: binomial, Poisson, negative binomial & zero-inflated
Poisson, zero-inflated negative binomial,@freakonometrics

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Subsidence Risk in France [6]Random forest models for frequencies@freakonometrics

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Subsidence Risk in France [7]2017, random forest Poisson, zero inflated, observed, Nat Cat recognition@freakonometrics

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Subsidence Risk in France [8]2018, random forest Poisson, zero inflated, observed, Nat Cat recognition@freakonometrics

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Wildfire Canada [1][1]

A. Benchallal, Y. Bouroubi, and A. Charpentier. “Human-caused wildland fire occurrence
prediction over the province of Quebec using mahine learning algorithms and free geospatial
datasets”. In: 10th International Conference on Agro-Geoinformatics and 43rd Canadian
Symposium on Remote Sensing (2022).

[2]

France Info. “Canada : le ”dôme de chaleur” provoque de violents incendies à Lytton”. In: (2021).

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Wildfire Canada [2]@freakonometrics

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Wildfire Canada [3]@freakonometrics

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