Clustering Nations for Climate Risk Communication and Preparedness: A Model-Based Approach

Effective risk communication for severe weather events remains a critical challenge as climate change intensifies global hazard exposure, yet strategies often lack tailoring to diverse national contexts, undermining their impact. To address this, we investigate whether countries can be grouped into clusters with shared characteristics, thus identifying countries facing similar challenges or opportunities in public understanding of climate risks and preparedness behaviours and enabling stakeholders such as meteorological services to adapt strategies efficiently, leveraging insights from one country to benefit others within the same cluster. Such efficiencies can enhance the design of contextually relevant hazard warning systems, policies, and preparedness initiatives. We achieve this grouping using a model-based clustering algorithm. We generate variables summarizing risk perceptions and disaster preparedness across population segments – namely, age cohort-by-gender and income quintile – to capture important sociodemographic differences that shape vulnerability and resilience, as supported by prior research. These summaries, combined with country-level data on disasters, climate projections, wealth, and governance, feed into the clustering process, ensuring within-country heterogeneity informs our results. The clustering algorithm identifies six distinct clusters based on their preparedness for disaster, relative affluence, and concern about severe weather events: Cluster 1 features low preparedness, low affluence, and high weather concern; Cluster 2 shares low preparedness and modest affluence but exhibits low concern; Cluster 3 includes wealthier, unprepared countries with high concern; Cluster 4 comprises affluent, moderately prepared countries with low concern; Cluster 5 reflects wealthy, unprepared nations with varied concern levels; and Cluster 6 contains moderately prepared, less affluent countries with heterogeneous concern. These groupings reveal nuanced patterns in how populations perceive and respond to severe weather risks, highlighting where messaging or warning approaches may succeed or falter. For instance, Cluster 1’s high concern suggests receptivity to urgent messaging, while Cluster 2’s indifference may require awareness campaigns, while comparatively lower levels of affluence in both clusters highlights a need to identify and communication feasible risk reduction actions that can be undertaken at a household level. By pinpointing such distinctions, our approach may reduce development time for tailored interventions, drawing on shared experiences within clusters. Our novel framework should empower policymakers, meteorological services, and NGOs to craft targeted risk communication and preparedness strategies, aligning with specific regional needs and improving resilience against escalating climate threats.