# Recommendations and challenges regarding the construction of climate change impact scenarios in health and life insurance

Stéphane Loisel, Adeline Stephan, Rayane Vigneron

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Stéphane Loisel, Adeline Stephan, Rayane Vigneron. Recommendations and challenges regarding the construction of climate change impact scenarios in health and life insurance. 2025. hal-04983930

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# Recommendations and challenges regarding the construction of climate change impact scenarios in health and life insurance

Stéphane Loisel¹, Adeline Stephan², and Rayane Vigneron³

¹Conservatoire National des Arts et Métiers, Efab, Lirsa, Paris, France

²Guy Carpenter, Paris, France

³UCBL,LSAF, Lyon, France

March 7, 2025

## Abstract

Following initial pilot stress test exercises proposed by insurance and banking regulators (see e.g. [ACPR, 2024]), insurance and reinsurance companies face the challenge to design climate change impact scenarios up to time horizons ranging from 2050 to 2100, much beyond the traditional 3 to 5-year view usually provided in their Own Risk and Solvency Assessment. We provide general recommendations for the construction of such scenarios and illustrate them in the context of health and mortality risks of a Metropolitan France insurer.

**Keywords:** Climate change risk, Scenario analysis, Insurance industry, Enterprise Risk Management, Health impacts, Mitigation measures, Resilience.

## 1 Introduction

The insurance prudential regulation is often blamed for being too short-view oriented and to influence risk management towards myopy. The Solvency Capital Requirement is calculated with a 1-year time horizon in Solvency II. More generally, most insurers and reinsurers in the world have been using time horizons ranging from 3 to 5 years for the Own Risk and Solvency Assessment (ORSA). Recently, several regulators, including ACPR in France (see [ACPR, 2024]), encouraged insurance companies to study climate change impacts thanks to the design of scenarios with time horizon 2050 or 2100. This paper provides recommendations on desirable properties of those scenarios for life and health insurance, as well as supplementary material that should be given to decision makers to avoid misuse of those scenarios.

Climate change impacts in insurance, both in general and for health and mortality in particular, has received a lot of attention from actuarial associations (see for example [Ranasinghe, 2022]), insurance federations, regulators and their advisors (see for example [Drif et al., 2020b] and [Drif et al., 2020a]) as well as researchers from different fields, including actuarial science. For the sake
of brevity, we refer to [Raynal and Loisel, 2025] and [Zhou et al., 2024] as well as to references therein.

The first difficulty to mention as a scientific disclaimer in the supplementary material corresponds to the (Knigthian) uncertainty that makes the design of the scenarios very complex. Even if one ignored climate change, there is already a strong uncertainty on future longevity improvements up to the end of the century (due for example to potential effects of antibiotics resistance (see for example [Hodbert et al., 2025]), pandemics, social and interstate conflicts, obesity (see for example [Grandl et al., 2024]), medical science improvements, impact of forever pollutants, and so on...).

There is of course some uncertainty on climate evolutions, that can be reflected by diverse RCP's and SSP's designed by IPCC experts (see for example [Raynal and Loisel, 2025]), and enhanced by taking into account additional tipping points not present in those average representative scenarios. For example, if working at the 2100 horizon, it is important to mention in the scientific disclaimer that one does not consider scenarios where ocean circulation currents are modified and where more severe cold waves affect mortality in Europe in winter (see for example [Barcikowska et al., 2018]).

There is also uncertainty on potential correlations and combined affects, on threshold effects and saturation effects, as well as adaptation capacities and observance of adaptation and mitigation measures (see for example [Casey et al., 2020]). It is however important to represent these key phenomena in some way in the scenarios, in order to inform the decision makers of their existence, because it makes risk response much more difficult to achieve, and scenarios much less smooth. It is also important to mention the main types of above phenomena that are not considered in the scenario (risk of lack of safe water and the associated correlations with other risks for example).

For an insurer or reinsurer board, there is already a lot of uncertainty on the perimeter and the environment of the company at the 3-year or 5-year time horizon when discussing the last years of ORSA scenarios, so it is very hard to reflect on time horizons where this uncertainty is much worse, for time horizons 2050 or 2100.

All of this makes the exercise very difficult, and these sources of uncertainty make it very tempting to "do the minimum" and continue to steer the company with a myopic view and a few sustainability initiatives. This is why one should come back to the fundamental question: what are the main objectives of these scenarios? From our point of view, it is not about trying to predict the future, because those sources of uncertainty makes it impossible. The scenarios and their supplementary material should inform decision makers of the main challenges to design the appropriate risk response, without submerging them with too many scenarios. We now present six main recommendations that apply to the insurance industry for most developed countries and illustrate how they can be implemented for a representative life and health insurance portfolio concentrated in Metropolitan France. Each recommendation is developed in a separate Section.
## 2 Scope, data, imagination and actuarial judgment

The first challenge concerns the determination of the scope of the set of scenarios. In our context, the goal is to build scenarios about climate change impacts on the French life and health portfolio. In order to be able to present a few variations of the proposed scenarios, one must carefully select the risks to illustrate in each scenario. In its pilot exercise, the French regulator had considered pollution and vectorial diseases. Another candidate is the risk of more frequent and severe heat waves, documented by several actuarial studies, in particular because the 2003 heat wave caused more than 14000 extra deaths in France. Some other top emerging risks like safe water scarcity for which there is awareness but less maturity in terms of risk analysis should be mentioned in the supplementary material but not in the scenarios, so that they can be monitored by an ad-hoc emerging risk committee.

Concerning the scope of the scenarios, our first recommendation is to be very selective and to prioritize risks for which the direct impact of climate change is the most obvious and for which maturity is already there. In our context, this leads to produce two scenarios: one on the impact of heat waves on health and mortality, and one on dengue epidemics. Let us explain why we have chosen to prioritize dengue and heat waves over pollution, as well as dengue over zika, malaria or chikungunya.

<figure><img src="image_2.png" /><figcaption>Figure 1: Number of heatwaves days recorded in France between 1947 and 2023 according to [Météo-France, 2024]</figcaption></figure>
The fact that climate change will increase the frequency and severity of heat waves in France is well documented and the phenomenon has already started to be observed. The number of heatwave days recorded annually by [Météo-France, 2024] in France has not only increased over time but has also shown a trend toward regularity in recent summers. As illustrated in Figure 1, in the last 25 years, there have been at least five heatwave days each year (with only 2014 and 2021 experiencing none), while between 1947 and 1997—a 50-year period—only seven years experienced more than five heatwave days. Another way to illustrate this increase is that since 2010, there have been 22 heatwaves, exceeding the total recorded between 1947 and 2000.

Similarly, the "tiger mosquito" *aedes albopictus* is already present in Metropolitan France and has already caused several (small and localized) dengue autochtonous epidemics in France during the previous years, as shown in Figure 2.

<figure><img src="image_3.png" /><figcaption>Figure 2: Previous autochtonous outbreaks of dengue (blue), zika (red) and chikungunya (grey) in France (source [Eurosurveillance, 2023])</figcaption></figure>

To get the attention of decision makers, it is easier to start with phenomena that have already appeared, even if their importance might be magnified by climate change impacts in the future.

Besides, the climate change impacts are quite direct and easy to understand by non specialists: warmer years lead to more heat waves and to longer and wider episodes of presence of tiger mosquitos, which increases both considered risks. The situation is more complex for pollution. While climate change may lead to higher sensitivity of a given pollution level (due to higher tem-
perature for example), climate change mitigation actions are likely to decrease pollution levels for top pollutants (including PM2.5, PM10, NO2), and there is already data showing evidence of such recent improvements. The situation is more complex for ozone, for which there is more uncertainty. However, any decision maker who connects on copernicus website (providing data on climate scenarios including projections of tiger mosquito suitability index and of pollution concentration levels for different RCP's) would notice that tiger mosquito suitability index maps become more and more red (meaning that risk level deteriorates), while most pollution concentration become more and more blue (meaning that the risk level improves). If one has to choose only two top risks (which is our case) to avoid losing the attention of decision makers, then it makes sense to retain the two top risks for which the impact is the most direct, the least questionable, and has started to mildly appear.

Excluding pollution from the scenarios and sending it to supplementary material also questions the relevance of pilot exercises proposed by regulators and their adoption by insurers and reinsurers: pollution is in fact a top risk to consider for mortality and health in the future in Metropolitan France, as the past exposure to pollutants takes time to result in health deterioration, a bit like smoking or exposure to asbestos. One can therefore fear an important increase of diseases and deaths attributable to pollution in France in the future, perhaps more than deaths attributable to climate change impacts. However, the scope of the study concerns the design of scenarios about the impact of climate change on health and mortality. Another part of our first recommendation is to draw the attention of decision makers on the bias introduced by the focus on climate change impacts. An Enterprise Risk Management approach at the 2050 time horizon would probably have prioritized pollution in the top 2 risks to study.

Concerning the prioritization within vectorial diseases, malaria existed in the past in Europe and was eradicated thanks to changes in habits. There seems to be a consensus on the fact that it is unlikely to come back to Europe for the moment. Zika, chikungunya and other viruses like West-Nile virus may strike Europe in the future. However, dengue seems to be the best option for the scenario design, as the previous autochtonous outbreaks in France were dominated by dengue, and as dengue is identified as an increasing threat at global level.

The second part of our first recommendation concerns data, imagination and actuarial judgment. It is of course tempting to ground scenarios on relevant, reliable and available data. Very often, the available data do not enable us to correctly look into the future. It is therefore important to use actuarial judgment for the construction of the scenario, with systematic justification of why this dataset has been selected and why some "imagination" was necessary, and which practices or methods back actuarial judgment involved in the scenario design.

To make it less abstract, let us illustrate these concepts with the following question: which data should one use to create a scenario of autochtonous dengue epidemics in Metropolitan France? One could choose data from Singapore, as it is a developed country with long experience of dengue, which is endemic there. This would be a bad choice for several reasons. First of all, dengue epidemics in Metropolitan France restart at zero each winter, even in the most pessimistic climate change scenario during this century, while the epidemic does not stop in tropical Singapore. Besides, there are four serotypes of dengue, and one is dominant in South-East Asia. If one gets one of the four
types of dengue, the individual is likely to develop immunity to the serotype: in case of reinfection with the same serotype, the probability to have a severe form of dengue is much smaller. On the opposite, if the same individual is exposed to another serotype, then the probability to develop a severe form becomes much higher. Using reliable and available data from Singapore would be inappropriate for Metropolitan France, where most subjects have no immunity and where we may expect outbreaks of different serotypes of dengue over time, because it all depends on the serotype of dengue of the "zero patient" for each outbreak. At the beginning, the probability of a French person to be infected sequentially by two different serotypes is very small, but this probability is going to increase over time after several autochtonous epidemics and it is important to take it into account for longer time horizons.

Data for dengue imported cases and autochtonous epidemics is of course a starting point for the first years of the scenario. It would be however very hard to extrapolate from these very small epidemics (fewer than 50 cases in each autochtonous outbreak) for longer time horizons. Besides, there are many asymptomatic cases and like for covid, this makes it very hard, if not impossible, to calibrate a variant of Susceptible-Infected-Removed model. In fact, one has to rely on actuarial judgment based on data and imagination. The most relevant data from our point of view correspond to Key West dengue epidemic data, as all subjects have been blood-tested in the community, which provides a unique information of a real-world total number of cases for a closed community exposed for the first time to a dengue outbreak in a developed region. We use therefore the rates of contamination of Key West as a final reference point for our dengue scenario.

The insurance company wants the scenario to have a spatial component. Another important question is: where to simulate the first autochtonous outbreaks? There are several options: the tiger mosquito suitability index is already quite high for the Paris region (where the highest number of travelers with imported dengue are supposed to enter the metropolitan territory); the first autochtonous outbreaks started in the South of France. We decide to dispatch imported cases proportionnally to each region forecasted population and to allow for autochtonous outbreaks in "red" zones where the tiger mosquito suitability index is high enough. To simplify this zone can be described as almost the Southern half of France in 2025, and after ten years the whole country becomes red due to climate change.

The supplementary material should contain details about the external data quality, as well as potential improvements over time: many data providers and analysts have been working for several years on the problem of downscaling, in order to have a more precise and granular view of the relevant indices. Some datasets also evolve over time: for example, regarding the heat wave scenario construction, it could be interesting to consider the extra impact on so-called Urban Heat Islands (UHI's) where temperature may be 6 or 7 seven degrees higher than in other parts of the same municipality. Up to Fall 2024, a vulnerability index for Paris region was available at the building block level, but this index mixed UHI effects with other types of vulnerabilities like the percentage of elderly people in the block (which is irrelevant for an insurer whose policyholders are mainly aged between 25 and 65). However, after the scenarios were built, a new set of indices has been made available by the same provider, with pure UHI effect focus, making this new data easier to use for insurers in the future. A prospective view of the data that could be available in the future should be mentioned in the supplementary material not only because it paves the way for scenario improvement, but also because the mere fact that this data becomes available increases the probability that politicians take mitigation or adaptation actions in the future (this will be further
discussed in Section 3).

As a summary, the first recommendation is to limit the scope of the scenarios to a few top risks in order to maximize attention from decision makers (while documenting discarded risks and reasons for prioritization in a supplementary material), and to combine data and imagination (optimally combined thanks to actuarial judgment) to simulate what will happen at different time horizons. Regarding data, sources of currently available and relevant datasets should be documented, as well as potential new relevant datasets that could be released in the future.

# 3 Constructing less smooth scenarios

IPCC RCP scenarios, central banks scenarios for GDP forecasts in case of delayed or smooth green transition and other economic quantities, as well as ACPR pilot exercise impact of climate change on health and mortality scenarios (due to pollution and vectorial diseases aspects) have one common point: they feature steady, smooth increase or decrease of the studied indicators (sometimes with a little concavity or convexity), as the average impact is represented year by year in what is presented as a stress-test or a scenario. However, the probability that the evolution of the studied quantities is smooth in the uncertain future is extremely small: we can expect shocks, reactions, and volatility. Besides, some risks are by nature, even in absence of climate change impacts, catastrophic risks for which no severe event occurs in some years and for which a very large event or a cluster of extreme events may occur during a year or a series of years. It is of course the case for epidemics. For heat waves, the situation is a bit different, and will be discussed in Section 4.

In our context, dengue epidemics evolution over time is unlikely to be smooth. Any realistic scenario should incorporate a sequence of controlled autochtonous epidemics with varied severities (not in a perfectly increasing manner), followed by a loss of control, leading to a much larger epidemic outbreak, as one can block only small epidemics (sending special squads to eliminate residual water, spray insecticide and implementing lockdown in a very localized area is only possible if the epidemics does not spread too much). Once aedes albopictus settlement is widespread and lasts longer, the probability to have an out-of-control epidemic outbreak strongly increases, which must be reflected in the scenario. A succession of years with severe epidemics and of years without major episode is a more likely scenario than an increase in number of autochtonous cases by $x\%$ each year or $x(t)\%$ each year for an increasing function $t \to x(t)$.

To back this choice to represent a non smooth phenomenon, one may refer both to existing data about epidemics or natural disasters, show typical sample paths of several classical models, or rely on extreme value theory. Note also that the number of dengue imported cases in Metropolitan France during the previous years already exhibits this feature, as observed in Figure 3.
Number of DENV-4 cases per month in France, coming from foreign country

<figure><img src="image_4.png" /><figcaption>Figure 3: Imported Dengue of serotype 4 (DENV-4) Cases in France according to [Santé publique France, 2024]</figcaption></figure>

A typical scenario for dengue epidemics is provided in Figure 4.

<figure><img src="image_5.png" /><figcaption>Figure 4: Scenario for total DENV infected people per region in France</figcaption></figure>
The total number of dengue cases (imported plus autochtonous) presents some year to year variability during the first years, as well as some peaks due to uncontrolled outbreaks in 2031 and 2033 in the city of Nice and Montpellier, in 2036 in the PACA region (South-East of France) and finally in 2039 and 2046 on the whole France population. The infections in 2049 involve a different serotype of DENV and can lead to a more severe reaction due to a mechanism known as Antibody-Dependent Enhancement (see [Teo et al., 2023]).

The non-smooth nature is crucial to illustrate, among other things, the difficulty for insurers, public authorities, regulators, and society to detect and quantify the worsening of the situation, and to provide the most appropriate response. The risk of unpreparedness and over-reaction, followed by risk of under-reaction for further episodes, as seen with avian flu and then covid-19 [Grant, 2020], should be incorporated in the scenario to raise awareness of top management. It is also necessary to include (uncertain and not necessarily optimal) management actions with major impacts due to the long term horizon (see also Section 4). Eventually, it is important to incorporate alternative versions of the considered scenario, to make it very clear that variations of uncertain key parameters can multiply the number of cases by a factor very different from 1.

# 4 Consider combined events

In the Entreprise Risk Management (ERM) approach [Hopkin and Safari, 2012], one needs to take into account correlation between risks, and consider the potential occurrence of combined events. Combined scenarios have been widely studied by insurance companies in the past, such as pandemics that lead to both increased mortality and stock market crashes [Kirti and Shin, 2020]. However, the risks associated with combined events are not limited to these scenarios.

In our context, regarding heat wave risk, it is relevant to complement traditional actuarial, data driven approaches of quantification of additional mortality associated to temperature (see e.g. [Boumezoued et al., 2022], [Titon et al., 2024] and [Raynal and Loisel, 2025]) with combined scenarios including for example power outages. Since the 2003 heat wave, prevention efforts have been made, and it is reasonable to think that, even if the situation may worsen due to climate change, French authorities should stay or become able to manage situations with more frequent and intense heat waves in absence of other difficulty. However, if a massive power outage lasted several days during a heat wave, the consequences on health and mortality could be increased tenfold.

In present times, it could be regarded as bad luck to experience a long lasting power outage exactly at the same time as a heat wave (even if excessive temperatures might in fact cause power outage in some cases, which may strongly reduce the independence between the two events). Massive power outages during heat waves have in fact already been experienced in several countries, including India and in North America, with evidence of important consequences on health and mortality if it lasts "long enough", as documented by [Casey et al., 2020], and increased consequences on fragile or less affluent sub-populations.

In the future, there is a consensus in Europe (see e.g. [Dubois and Masquelier, 2014]) that climate change will most likely make heat waves more frequent, longer and more intense in Metropolitan France. In some regions, due to climate change, heat waves will evolve from events to almost permanent circumstances in the hottest months of the year. Therefore any event (power outage, pandemics, or
other types of disorganisation of the healthcare system) will be very likely to coincide with a heat
wave in 2050 if it occurs in the (potentially extended) summer, and therefore become a combined
event from the current viewpoint.

It is therefore relevant to reuse data from the worst heatwaves (including the 2003 one) as well as
data from consequences of heat waves and power outages in other countries, to refine the approaches
of [Boumezoued et al., 2022] and [Raynal and Loisel, 2025] (which start to build on more and more
granular datasets combined climate information and spatio-temporal mortality data) with extra
deaths and health deterioration caused by events like power outages occurring during heat waves.

Besides, it is necessary to explain in the supplementary material that mortality seasonality may
change due to climate change (fewer deaths during cold waves and more deaths during heat waves,
see e.g. [Titon et al., 2024]) with some harvesting effects (like in 2003), and that this does not
correspond to the top risk of the scenario. The top risk of the scenario should from our point of
view be the risk of power outage or similar event occurring during the future almost permanent
circumstances of summer heat waves.

Correlations with potential water scarcity and drought in other lines of business should also be
mentioned. Harvesting effect (concentration of deaths of fragile individuals who were likely to die
within one or two years anyway) must be separated from other premature deaths.

5 Long-term risk and the importance of dynamic aspect and man-
agement risk actions

In order to obtain the adhesion of decision makers, and given that the time horizon (2050) is much
longer than the ORSA time horizon (3 to 5 years) for which management actions already have
to be taken into account (see e.g. [Loisel, 2012]), it is necessary to incorporate well documented
management actions in the scenarios, even if they are uncertain.

In the case of dengue epidemics, several prevention actions can reduce the risk of infection by
more than 80%. These measures include, among others, the use of insecticides even if the mosquitoes
evolve to resist them [Dahmana and Mediannikov, 2020], wearing protective clothing [Luan et al.,
2021], managing stagnant water by reducing them or trying to reduce the population of the vector
(similar to introducing fish species that feed on mosquito larvae like the Gambusia Affinis [Chan-
dra et al., 2008] in other parts of the world for example), and vaccination. However, despite their
effectiveness, these prevention measures can be challenging to implement, and appropriate commu-
nication strategies must be adopted for the general public.

In addition to the implementation costs, even if one may think that a vaccine against dengue
(for which a preliminary version has already been developed) could be developed if this vectorial
disease continued to expand, covid-19 experience has shown that it might be difficult to obtain good
vaccination rates on the short and long terms. It would be the same if one tried to impose certain
clothing that can protect against mosquitoes.
Besides, as it is a well-known issue in Enterprise Risk Management (ERM), any risk mitigation strategy may breed undesirable consequences. For example, the introduction of Gambusia Affinis fish to control mosquito populations may have negative impacts on biodiversity[Chandra et al., 2008], reducing wet zones would also impact biodiversity negatively, and so would the use of insecticides.

For heat wave risk, the uncertainty around two mitigation measures is crucial to mention and represent in the scenario. The first one is related to Urban Heat Island effects. Given the fact that data become more and more available, one may reasonably hope that vegetalisation efforts will be implemented in the future in large European cities, leading to better conditions reflected by indicators like evapotranspiration, shade, and CO2 absorption. Planting trees and adapting watering strategies is beneficial. In Göteborg (Sweden), this had led to a 5.9 degree decrease of temperature. In Mexico, cooling effect of a park is observed up to 2 kilometers. In France, the UHI effect leads to average difference of 6 degrees between UHI's in Paris region and greener parts of the same area.

The other uncertainty concerns the evolution of the air conditioning equipment rate and use rate in Metropolitan France in the future. If these rates increase and if it remains acceptable and affordable to use air conditioning (in spite of carbon emissions and short-term feedback impact on UHI's temperature (which can be more than +1 degree)), then most heat waves would have limited short term impact, but consequences of power outages would be tenfold in comparison to scenarios where more climate-friendly mitigation and adaptation techniques are used (planting trees, adapting buildings, ...). Due to climate change and green transition, one will have to optimize water and energy. Interconnecting systems and devices (water and power networks) that were not designed to be connected online in the first place makes mortality during heat waves highly correlated to increased cyber risk, in a difficult geopolitical context where tensions are likely to be exacerbated by climate change.

Longer and more intense heat waves could also have indirect effects on health and mortality. For example, people could exercise less during the extended summer, which could have some long-term consequences on health, if one does not find alternatives. While it is important to mention this kind of indirect effects, we believe that it is desirable to focus on more direct impact of combined scenarios for a first exercise.

# 6 Risk of misinterpretation of IPCC Methodology and systems thinking

Given the complexity and the level of uncertainty of climate change evolution and impacts, it is important to use the methodologies established by the Intergovernmental Panel on Climate Change (IPCC), insurance and banking regulators, and actuarial associations. However, there is a risk of misinterpretation of IPCC methodology. While it is made crystal clear in IPCC documentation that many major risks, such as biodiversity collapse, wars, cyber-warfare, epidemics, and tipping points, are averaged or neutralized in these scenarios, employing multiple Representative Concentration Pathway (RCP) scenarios, including the most extreme RCP 8.5, may give the false impression of
comprehensively addressing uncertainty [Moss et al., 2008].

When constructing stress tests for the insurance industry, it is important to consider (or at least mention clearly in a scientific disclaimer) key tipping points like changes in major ocean currents and winds, ocean acidity, biodiversity loss, and antibiotic resistance. These tipping points are well documented in ecology (for example coral-kelp tipping point) and might be represented to very diverse modeling techniques (for example [Dal Moro, 2025] approach this concept through the level crossing of the infinite mean threshold in a changing parameter generalized Pareto claim size distribution for individual disasters).

In our context of heat waves and cold waves, it is necessary to mention in the supplementary document that their frequency and severity in France could be strongly affected by a change in global ocean currents, and that this scenario is not reflected in the set of RCP average sample paths provided by IPCC, because it is not their purpose. A cyber attack leading to power outage during several days in a region is an example of tipping point that we recommend to include, in line with previous sections. In the context of vectorial diseases, we recommend to include at least one tipping point, which corresponds to the case where a dengue autochtonous outbreak becomes out of control. It becomes then more difficult or almost impossible to send specialized squads to remove stagnant water and spray insecticide when the infected zone is too large, leading to explosive contaminations. We recommend to incorporate other relevant tipping points, including the availability of a vaccine, and later on under or over-reactions from the general public and decision makers.

Theoretically, a balanced approach that considers both prevention and response measures, and is tailored to the needs and perspectives of various stakeholders, including but not limited to insurance companies, is necessary to effectively manage complex risks and achieve organizational objectives in an integrated, collaborative and sustainable manner. To address this challenge, [R. Musulin, 2024] (General Insurance forum of the IAA) recommends that actuaries use systems thinking, defined by [Wikipedia, 2024] as the "interdisciplinary study of a system, which is a cohesive conglomeration of interrelated and interdependent parts that can be both natural and human-made", and collaborate with experts in diverse fields like climate macroeconomics, ecology or social science. This requires active engagement and collaboration among different actors, both private and public (as explained in the book of [Carson III, 2023]) to ensure that risk management actions are feasible, acceptable, and aligned with the broader societal goals and values, to avoid focusing only or too much on carbon emissions and forgetting biodiversity and social aspects.

Systems thinking can be illustrated in our context by the issue of pollution. We have seen that pollution effects on health (often materializing after a delay) could be increased by climate change, even if climate action should lead to overall air pollution decrease. We have already mentioned that climate change mitigation actions feature co-benefits: using soft mobility modes decreases pollution in French cities and increases physical activity. However, less pollution unmasks warming, as previous "pollution smog" no longer masks climate change impact on temperature, whose increase seems "accelerated" due to unsmasking.

It is of course impossible for an insurance undertaking to model or represent the future of the
whole planet. However, it is important to think in terms of systems risk, to include at least one
effect of the complex inter-dependencies between "parts" of our planet system in the scenario, and
to mention several important aspects of the systems view that are relevant to the context of the
scenario.

7 Decuple risk attitudes

[Ingram and Thompson, 2011] and his coauthors have shown how different attitudes towards risk could lead individuals to favor different risk mitigation strategies. [Ingram and Loisel, 2016] have surveyed and compared attitudes with respect to risks and models in North America and Europe in the actuarial and risk communities. Results of those studies show that European actuaries tend to be more conservative than North American ones, and that individuals may change their risk attitude if outcomes vary from their expectations, which could lead to feedback loops and fast regime changes.

For uncertain, long-term risks like climate change impacts on health and mortality, risk attitudes are decuple. In some actuarial working groups, some actuaries make statements like "We'll figure it out, we already went through complex situations many times in the past and we always found a solution" while some others share the climate anxiety with some part of the general population. Many actuaries believe that the insurance sector could play a major role in climate action. This leads to the following question: should actuaries be neutral or activists?

Instead of answering this tricky question (that will have a huge impact on the type of scenario actuaries may develop), we provide recommendations on the tradeoff between complexity and adoption. If the scenario is too complex or if the number of scenarios is too large, board members and top managers are unlikely to take it into account for decision making. Conversely, a too simple scenario would be easier to understand and to interprete, but could lead to underestimate combined effects, tipping points and systems effects. In practice, we recommend to focus on two or three perils (or types of scenarios) for which there are already early warning signals, to include combined scenarios and to propose one or two variations of each scenarios with tipping points as well as successful and less optimal management actions, so that uncertainty, complexity and difficulty to react are not underestimated. As explained in previous sections, the list of assumptions and main neutralized risks should be accompanied with a striking sensitivity test. Even if this raises the risk that some persons do not trust the scenarios anymore, this is crucial for decision makers to understand that they deal with stress tests and not with forecasts. For example, in our context of vectorial diseases, it is far the more important to provide variations of the same scenario with very different levels of number of contaminations and deaths.

Conclusion

Motivated by the problem of constructing scenarios up to 2050 to investigate uncertain climate
change impacts on health and mortality for a Metropolitan French insurance portfolio, we have pro-
vided the following recommendations: if needed, prioritize the most direct and undisputable effects
with existing early warning signals; use actuarial judgment combining data, imagination and profes-
sionalism; build non smooth scenarios; embed combined scenarios, particularly for the cases where
events become almost permanent circumstances in some seasons; take into account management ac-
tions as well as potential under- and over-reactions; think in terms of systems risk; make sure that
IPCC methodology is clearly understood, and that decision makers do not mix up stress-tests with
forecasts; acknowledge the impact of risk attitudes in presence of multiple sources of (long-term)
uncertainty; provide variations of scenarios and sensitivity tests to illustrate the level of uncertainty,
as well as a list of main assumptions, ignored risks and tipping points in a short supplementary
material. One should also check that focusing of climate change impacts does not lead to neglect
top risks that should be taken into account in an Enterprise Risk Management approach. Even if
these recommendations are illustrated in this paper in the context of vectorial diseases and heat
waves in Metropolitan France, we believe that these recommendations remain relevant for other
lines of business and other countries.

**Acknowledgment:** S.Loisel and R. Vigneron would like to thank the research initiative "Sustainable actuarial science and climate risks" as well as the research chair ACTIONS for partial support.

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