As climate change makes extreme weather more frequent and more extreme, policymakers have the overwhelmingly difficult task of designing safety nets for the vulnerable while mitigating climate change. Climate risk assessment is crucial to inform the efforts of policy making. Climate scientists, economists and energy system experts have built a range of scenarios that examine how global society, demographic and political-economy might change over the next century. These are called “Shared Socioeconomic Pathways (SSPs)”. There are five such pathways going from SSP 1 to SSP 5.

Future flood risk assessment, for example, is particularly important for regions next to rivers or streams, called floodplains. But the damage caused from flooding depends on the population that is exposed to it. Living next to water has its pros and cons. It is hardly coincidental that property rates tend to be higher the closer you get to water bodies. But as climate change makes flooding more frequent, do we expect people not to change these preferences? Additionally, government policies such as access to flood insurance would also feature into the decision-making of households choosing to settle inland or closer to water.

The problem then becomes a two-way calibration of how population affects flood damage and how the latter affects the former in a dynamic system. But SSPs assume the vulnerability of communities to flood damage to be constant over time.

In a study using data from SSP2 (the moderately challenging scenario) on European Floodplains, Max Tesselaar and his colleagues at Institute for Environmental Studies, Vrije Universiteit Amsterdam show that population growth in European floodplains and, consequently, rising riverine flood risk are considerably higher in the presence of flood insurance.

"Some studies find that the socioeconomic side plays a potentially greater role than the climatological side in climate risk studies," says Max Tesselaar, explaining that only looking at climate risk scenarios is unlikely to give the full picture of the impact.

But the type of insurance also makes a difference. In several European countries, including France, Belgium, and Spain national flood insurance policies aim to promote solidarity among households in high- and low-risk areas. This results in something commonly referred to as “risk-pooling” where high and low risk areas end up paying for the ‘average risk’ of the country.

On the contrary, several countries, such as Germany and the UK strive to implement mechanisms that stimulate household-level adaptation, including risk-based premiums. This means that policyholders pay the premium that reflects the risk faced by their property.

Risk-pooling, in effect, passes some of the risk from high-risk properties onto low-risk ones. On the other hand, risk premiums ensure that those who choose to live in more risky areas bear all of the additional cost of this risk. This discourages settlement in more risky regions vis-a-vis the risk pooling situation. The models show that this difference could be significant when it comes to assessing future flood risk.

To measure future flood risk, the authors first measure the impact of insurance on population growth in regions where population is expected to grow. They find that compared to the currently used baseline scenario, the floodplain population growth may be more than twice as high when considering people’s behavioral changes resulting from higher flood risk as well as flood insurance availability.

They also find that population growth projections in floodplains are consistently and substantially higher in France and Belgium, where premiums are relatively inexpensive in high-risk areas. In 2050, average risk-based premiums in Sweden, Ireland, and the UK are close to €400 annually per household, flat-rate premiums in Spain, France, and Belgium are approximately €13 per household.

By contrast, introduction of flood insurance has no impact on population predictions of Austria, Slovenia and the Czech Republic since the use of risk-based premiums generally discourage settling in floodplains, similar to the scenario without insurance availability.

These population predictions are then used to measure the additional flood risk due to insurance, using the metric of expected annual flood damage (EAD) to residential and commercial properties. EAD accounts for both flood hazard or the intensity and frequency of floods, as well as the population exposed to the hazard.

The availability of insurance causes higher increases in EAD compared to the baseline in many regions including, once again, France, Belgium, and Spain. This is largely driven by higher population growth in floodplains under a flat insurance premium structure. On average, considering insurance availability, the growth in flood risk through 2050 in each of these countries is €3.9 billion, €440 million, and €1.3 billion, respectively.

A key point made by this study is to bring up the age old question in Economics of balancing efficiency and equity. A risk premium can help deter construction in flood-prone areas. That may be a good policy outcome to prevent more sea facing condos from being built in Nice. But it may also make flood insurance expensive for rural or more vulnerable communities who cannot so easily make the choice to move.

"It might increase even social inequalities in the future, right, because if you have certain populations that can afford insurance, they are well protected. So when a disaster occurs, they recover fairly easily. And other populations that cannot afford insurance, for example, they, well, yeah, they become increasingly worse off", says Max.

The solution will need governments to think innovatively. The insurance market alone is unlikely to provide a comprehensive solution. A uniform risk premium based approach will have to find a way to compensate vulnerable populations while also working on long term relocation plans.

"One way that I proposed also in earlier papers...is sort of a transition phase where households that are located in very risky areas and that are poorer, receive, for example, subsidies to flood proof their houses, to become less risky. And therefore, they can also reduce the premium, they can become more resilient in that sense", says Max.

While this model provides a useful framework for thinking about the role flood insurance plays in mitigating flood risks, it is not the full story. For example, the authors only look at regions that are expected to witness population growth.

Max explains models as useful starting points. But they can never be the full truth by themselves.

"All models are wrong, but some are useful... the idea of applying a model like this is not to model the reality... but it's simply showing well under these kinds of scenarios, what kind of effect can we expect on populations."

As insurance markets dry up in disaster-prone areas, it might be good to remember that they were never the complete solution to begin with. It's time we started thinking about other ways of protecting people from all the disasters in waiting that we may not yet see.