Catastrophic Climate Risks and Insurance
Arthur Charpentier (UQAM)
Workshop on Impacts of Climate Change
on Economics, Finance, and Insurance, September 2022

September 17, 2022

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Published & on-going work on climate related risks
Flood (Hurst / Gumbel, 2008)
doi: 10.1002/env.909
Windstorm dynamics (2006)
doi: 10.1007/s00477-005-0029-y
Insurability of climate risks (2008)
doi: 10.1057/palgrave.gpp.2510155
Public intervention ? (2014)
doi: 10.1016/j.jpubeco.2014.03.004
Earthquake dynamics (2015)
doi: 10.1007/s10950-015-9489-9@freakonometrics

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Heat wave and return period (2011)
doi: 10.1007/s10584-010-9944-0
Floods & fairness (2021)
doi: 10.1057/s41288-021-00233-7
Subsidence & heat waves (2022)
doi: 10.5194/nhess-2021-214
Wildfires (2022)
ICAG-CSRS Conference 2022
Public intervention with RL (2022)
arXiv: 2207.01010

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Flood Risk in France, and solidarity [1][1]France Info. “Seine-et-Marne : la ville de Thoméry se remet progressivement des inondations”.
In: (2018). url: http://tinyurl.com/mtc9tprm.

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Flood Risk in France, and solidarity [2]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[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.

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

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Flood Risk in France, and solidarity [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, and solidarity [5]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, and solidarity [6]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, and solidarity [7]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, and solidarity [8]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
39
637
8411
12678
1001
765

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

Welch
t test

1.0
-60.1

-8.6
-2.7

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.

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Flood Risk in France, and solidarity [9]VarNon risky
Risky

Seine et
Marne

Haute
Loire

Non risky@freakonometrics

Risky
Non risky
Risky

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

Difference(%)

Maximum
Price

-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

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

Welch
t value

6.4
5.2

1.6
0.9

8.0
7.5

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Flood Risk in France, and solidarity [10]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

6.1
-1.2

2.1
4.6

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

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Flood Risk in France, and solidarity [11]2 zone model, α ∈ [0%, 100%],
zone 1, proportion α, less riskyzone 2, proportion 1 − α, more risky
so called “Will Rogers phenomenon”,
When the Okies left Oklahoma and moved to California,
they raised the average intelligence level in both states.
α = 90%, less risky,26.4% of losses, 3.5e
1 − α = 10%, more risky,
73.6% of losses, 88.5e@freakonometrics

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Vendée

Pays
Loire

Var

Flood Risk in France, and solidarity [12]Frejus
Grimaud
Puget
Assérac
Mesquer
Le Croisic
Talmont-Saint-Hilaire
Noirmoutier-en-l’Île
La Faute-sur-Mer

Country
12.0e
12.0e
12.0e
12.0e
12.0e
12.0e
12.0e
12.0e
12.0e

Uniform
Region Municipality
30.6e
15.7e
30.6e
84.3e
30.6e
133.0e
3.6e
6.7e
3.6e
10.2e
3.6e
25.9e
10.7e
4.8e
10.7e
8.5e
10.7e
275.1eTwo-Zone Modelα = 95% α = 90% α = 80%
5.1e
3.5e
52.1e
142.3e88.5e
52.1e
142.3e
88.5e
52.1e
5.1e
3.5e
2.0e
5.1e
3.5e
2.0e
5.1e
88.5e
52.1e
5.1e
3.5e
2.0e
5.1e
3.5e
2.0e
142.3e88.5e
52.1e

Table 3: Comparing premiums, in e, in nine cities, in Var, Pays-de-Loire and Vendée.

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Vendée

Pays
Loire

Var

Flood Risk in France, and solidarity [13]””Fréjus
Grimaud
Puget-sur-Argens
Assérac
Mesquer
Le Croisic
Talmont-Saint-Hilaire
Noirmoutier-en-l’Île
La Faute-sur-Mer

Country
6.9%
6.9%
6.9%
6.9%
6.9%
6.9%
6.9%
6.9%
6.9%

Uniform
Region Municipality
17.5%
9%
17.5%
48.2%
17.5%
76.1%
2%
3.8%
2%
5.8%
2%
14.8%
6.1%
2.7%
6.1%
4.9%
6.1%
157.2%

Two-Zone Modelα = 95% α = 90% α = 80%
2.9%
2.0%
29.8 %
81.3%
50.6%
29.8 %
81.3%
50.6%
29.8 %
2.9%
2.0%
1.1 %
2.9%
2.0%
1.1 %
2.9%
50.6%
29.8 %
2.9%
2.0%
1.5 %
2.9%
2.0%
1.1 %
81.3%
50.6%
29.8 %

Table 4: Comparing premiums, in percent of the household premium, in nine cities, in Var,
Pays-de-Loire and Vendée.

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Vendée

Pays
Loire

Var

Flood Risk in France, and solidarity [14]Fréjus
Grimaud
Puget-sur-Argens
Assérac
Mesquer
Le Croisic
Talmont-Saint-Hilaire
Noirmoutier-en-l’Île
La Faute-sur-Mer

Hierarchical Model γ = 20%
β = 10% β = 20% β = 50%
14.7%
13.7%
12%
17.8%
21.5%
27.7%
20.1%
27.1%
38.8%
3.2%
3.4%
3.7%
3.3%
3.8%
4.5%
4.0%
5.6%
8.1%
6%
5.6%
4.9%
6.2%
6.0%
5.8%
18.3%
36.5%
66.7%

Hierarchical Model γ = 40%
β = 10% β = 20% β = 50%
12.7%
12.0%
10.7 %
15.1%
17.8%
22.5 %
16.8%
22.0%
30.8 %
4.1%
4.2%
4.5 %
4.2%
4.5%
5.1 %
4.7%
5.9%
7.8 %
6.2%
5.9%
5.4 %
6.3%
6.2%
6.0 %
15.5%
29.1%
51.7 %

Table 5: γ: national, (1 − γ)(1 − β): département, (1 − γ)β: municipality.

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Flood Risk in France, and solidarity [15]Tradeoff: risk vs. welfare / wealthPrevention cannot be done at the individual level, even cities...
Hierarchical approach: city / region / country
too small granularity might cause market failure, Charpentier & Le Maux (2014)[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]

A. Charpentier and B. Le Maux. “Natural catastrophe insurance: How should the government
intervene?” In: Journal of Public Economics 115 (2014), pp. 1–17.

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Subsidence Risk in France [1][1]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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Subsidence Risk in France [9]Models for costs and destruction rates@freakonometrics

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Subsidence Risk in France [10]Risk map

zone 0: 41% contracts, 0% losses
zone 1: 47% contracts, 67% losses
zone 2: 12% contracts, 33% losses@freakonometrics

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Subsidence Risk in France [11]Risk map for 2017

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Subsidence Risk in France [12]Risk map for 2019

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Subsidence Risk in France [13]Risk map for 2020

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Subsidence Risk in France [14]@freakonometrics

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Subsidence Risk in France [15]

Quite good in predicting which municipality will claim a loss
More difficult to understand which one will be recognize by the government“It is considered abnormal if the indicator presents a return period greater than orequal to 25 years”
Difficult task in the context of climate change[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.

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Wildfire in Québec (Canada) [1]
Joint work with Amirouche Benchallal (UQAM) and Yacine Bouroubi (Sherbrooke).

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Wildfire in Québec (Canada) [2][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).

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Wildfire in Québec (Canada) [3]Theoretical issues on creating a balanced from largely unbalanced data

[1]

David Hand. Dark Data: Why What You Don’t Know Matters. Princeton University Press, 2020.

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On Government Intervention [1]Joint work with Nouri Sakr (Columbia) and Mennatalla Mohamed Hassan (American
University in Cairo).
2 reward rt
3 state st+1 ∈ S

st−1 transition st
agent

environment

1 action at ∈ A[1]actionat−1

T (st , at , ·)st+1

actionatactionat+1

M. Hassan, N. Sakr, and A. Charpentier. “Government Intervention in Society Under
Catastrophe Risk: A Reinforcement Learning Approach”. In: ArXiv 2207.01010 (2022).

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On Government Intervention [2]For governments, the reward is the Marginal Value of Public Funds (MVPF ) (similar
to social cost-benefit approaches),
MVPF =

Willingness to pay for policy by beneficiaries (WTP)
Net cost of Government Spending (G)[1]

Amy Finkelstein and Nathaniel Hendren. “Welfare analysis meets causal inference”. In: Journal of
Economic Perspectives 34.4 (2020), pp. 146–67.

[2]

Nathaniel Hendren and Ben Sprung-Keyser. “A unified welfare analysis of government policies”.
In: The Quarterly Journal of Economics 135.3 (2020), pp. 1209–1318.

The government can take various actions• Offer government-provided insurance• Increase premium regulations• Ease Insurer Solvency Requirements• Increase Reinsurance Funds• Subsidies on insurance premiums• Increase Disaster Prevention@freakonometrics

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On Government Intervention [3]Those actions have some impact on decisions of individuals-policyholders andinsurance companies (multiple feedbacks)
individuals behaviors• dynamic consumption-saving-wealth approach,• disaster impact their wealth and their risk perception• individual’s optimism and amnesia biases• if not compulsory, they can purchase insurance, or notinsurer behaviors• initial capital, can add loading to risk premiums• possible bias in risk perception@freakonometrics

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On Government Intervention [4]We discuss stylized facts on catastrophe markets,• Free catastrophe insurance markets generate inadequate coverages• Purchases of catastrophe insurance tend to increase right after the society
witnesses a catastrophe experience and while the memory is still there• Policyholders tend to cancel their insurance coverages as the memory of pastcatastrophe fades and the time since the last catastrophe exceeds 5 years• After a catastrophe experience, many insurers respond by restricting supply andraising premium rates• Many insurers exit the market right after a catastrophe experience. For example,
many private insurers cut their terrorism insurance supply right after 9/11 attacks• Many individuals are willing to purchase catastrophe insurance but they areunable to find insurance at an affordable price@freakonometrics

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On Government Intervention [5]@freakonometrics

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On Government Intervention [6]@freakonometrics

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On Government Intervention [7]@freakonometrics

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References[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]

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.

[3]

A. Charpentier, R. Élie, and C. Remlinger. “Reinforcement Learning in Economics and Finance”.
In: Computational Economics (2021). doi: 10.1007/s10614-021-10119-4.

[4]

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.

[5]

M. Hassan, N. Sakr, and A. Charpentier. “Government Intervention in Society Under
Catastrophe Risk: A Reinforcement Learning Approach”. In: ArXiv 2207.01010 (2022).

@freakonometrics

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