Prioritising humanitarian aid funding for multi-risk disasters in an era of climatic damage

• Stochastic multi-attribute analysis (SMAA) is used for the first time.
• The model accounts for multi-risk disasters and climatic damage.
• Expert Delphi panel weighting grounds the analysis.
• Results are an explainable funding allocation for complex disasters.

The intersection of multi-risk disasters is a wicked problem for resource prioritisation. How do we effectively allocate funding to humanitarian aid when disasters compound and cascade with natural and human-made hazards – especially with climatic and non-climatic factors? Our research builds on the Intergovernmental Panel on Climate Change (IPCC) recommendation to use multi-criteria decision analysis (MCDA) to examine this.

For the first time, stochastic multi-attribute analysis (SMAA), a subtype of MCDA, is used to compare and prioritise funding for 26 fragile countries that were encountering a humanitarian crisis in 2023. The model integrates field data from the INFORM Severity dataset and expert weighting preferences from the United Nations, European Union, World Bank, research and public sectors, and civil society. Finally, we compared the prioritisation with the official funding requirements of the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA).

Our descriptive analysis broadly aligns with the current humanitarian funding requirements, except for countries like the Democratic Republic of Congo and Myanmar, which should receive a higher allotment, and Ukraine and Syria, which seem to be provided with undue support. The results confirm that a probabilistic multi-risk assessment combined with expert weighting produces a tangible and explainable funding allocation for policymaking and operational activities. These findings provide important insights in distributing scarce resources transparently yet effectively - particularly considering the funding freeze of the United States Agency for International Development (USAID).