Abstract
Wildfires generating damage to assets are extremely rare in France. The peril is not covered by the French natural catastrophes insurance scheme (law of 13 July 1982). In the context of the changing climate, Caisse Centrale de Réassurance—the French state-owned reinsurance company involved in the Nat Cat insurance scheme—decided to develop its knowledge on the national exposure of France to wildfire risks. Current and future forest fires events have to be anticipated in case one of the events threatens buildings. The present work introduces the development of a catastrophe loss risk model (Cat model) for forest fires for the French metropolitan area. Cat models are the tools used by the (re)insurance sector to assess their portfolios’ exposure to natural disasters. The open-source national Promethée database focusing on the South of France for the period 1973–2019 was used as training data for the development of the hazard unit using machine learning-based methods. As a result, we observed an extension of the exposure to wildfire in northern areas, namely Landes, Pays-de-la-Loire, and Bretagne, under the RCP 4.5 scenario. The work highlighted the need to understand the multi-peril exposure of the French country and the related economic damage. This is the first study of this kind performed by a reinsurance company in collaboration with a scholarly institute, in this case EURIA Brest.
Highlights
In the world, large forest fire events are generating significant damage to natural ecosystems, human lives, and critical infrastructures [1]
In collaboration with EURIA Brest, we developed a Cat model from scratch within seven months, from data collection and hazard modelling through machine learning to exposure and damage estimates
Within the sets of available tools suggested in Jain et al [30], we focused only on the following: (i) decision trees, (ii) support vector machines, and (iii) artificial neural networks
Summary
Large forest fire events are generating significant damage to natural ecosystems, human lives, and critical infrastructures [1]. In the last few years, large events occurred especially in the United States and in Australia [2]. In 2017 and 2018, in California, wildfire events were estimated, respectively, to have caused $12 bn in damages for the Tubbs Fire and the CampFire. It has been estimated that wildfire caused $150 bn damage globally, with $27.7 bn for direct losses to buildings and houses, or 20% of the total [3,4,5]. Between 2011 and 2020, the average annual loss for the USA was $4.7 bn for forest fires [3]. We had in mind last year’s Black Summer in Australia, with the sad images of koalas and kangaroos burnt by the flames; in addition to the
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