How to Prepare a Climate Risk Management Model that Works

A broken aid system and the concern of urban risk accumulation call for careful and case by case design of climate risk management (CRM) plans. An adaptable risk layering approach based on the return period of natural hazards, as seen in the illustration, can open a window of opportunity to enhance a CRM plan. In a single model designed by Mechler et al. (2014), optimal cost-efficient strategies for management and funding are interlinked in a spectrum of risks.

Lower and medium risk layers encompass manageable and avoidable extensive risks (i.e. high frequency/low impact). DRR strategies such as infrastructure like river flood walls, or anti-seismic building codes, can prove to be the most cost-efficient method for a government to face that bottom layer of hazards and disasters. In cities, for example, risk accumulation calls for DRR work to be relentless and encouraged in all sectors of society. Particularly in cities of developing countries, DRR is a priority given their rapid demographic growth and importance, high poverty levels, and weak urban governance.

As cost-efficiency diminishes for DRR with higher impact events, new strategies become necessary. More intensive events are better protected with financial tools such as emergency reserves and/or contingency loans or credits offered by multi-lateral development banks, such as the World Bank and the Asia Development Bank.

As the peril’s return period increases, we begin moving into the realm of intensive risks (i.e. low frequency/high impact), those that can suddenly destabilize a government’s finances in a single extreme event. For these hazards, governments need to transfer a portion of the risk to fund excess loss in a crisis. It is here where weather index-based insurance tools, such as parametric insurance or catastrophe bonds, take the spotlight and become an optimal cost-benefit option for disaster risk financing. Any remaining gap from an event beyond the risk transfer capacity will then require the aid of the international humanitarian community. The figures below by Benson (2017) illustrates the layering of these financial tools.

The challenge of this model is defining the rules to make evidence-based decisions to determine the threshold levels for each layer and financial instrument. A common stress testing method used in the finance and insurance field is the PML, the probable maximum loss. In the insurance industry, the PML is the maximum loss

that an insurer would be expected to incur on a policy. It represents the worst-case scenario for a risk bearer.

Since many governments may start feeling high financial stress levels at relatively low return periods, an ideal way to increase those threshold levels can be done by investing in DRR. A higher threshold level also means that risk transfer instruments like insurance would be required at a higher risk retention level. This results in lower premium costs since the insurer will be facing less claims.

As a word of caution, risk transfer solutions like parametric insurance, should not be seen as the only solution for financing extreme weather events, nor should it be a substitute for DRR, adaptation or social protection systems to combat poverty and inequalities. To enhance resilience, risk transfer instruments should be part of a wider adaptation strategy, rather than as the only strategy, or as an alternative to adaptation. To work well in a CRM strategy, insurance-based solutions need to be complemented with DRR and preparedness efforts. Otherwise, governments and populations may be exposed to a false sense of security, unwise risk-taking decisions and maladaptation.

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