2697. Multilevel Modeling and Clustering of Risk Perceptions of Climate and Severe Weather and Natural Hazard Preparedness Across Countries to Inform Disaster Risk Communicatio
Invited abstract in session TC-39: Resilient Infrastructure System, stream Sustainable & Resilient Systems and Infrastructures.
Tuesday, 12:30-14:00Room: Newlyn LG.01
Authors (first author is the speaker)
| 1. | Jack Thompson
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| School of Business, University of Leeds | |
| 2. | Suraje Dessai
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| University of Leeds | |
| 3. | Sarah Jenkins
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| University of Leeds | |
| 4. | Yim Ling Siu
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| School of Earth & Environment, University of Leeds | |
| 5. | Barbara Summers
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| Leeds University Business School, University of Leeds | |
| 6. | Andrea Taylor
|
| Leeds University Business School, University of Leeds |
Abstract
Understanding factors that shape public risk perceptions of climate and severe weather events, and their levels of preparedness, is essential for crafting effective disaster risk communication and reduction strategies. Prior research examines individual-level factors, such as personal experience, in isolation, focusing on their influence on risk perceptions and preparedness at a moment in time. This approach is limited, as it neglects the potential role of country-level factors, like governance, and the way the dynamics vary over time. Here we address this gap, exploring the influence of individual and country-level influences using data from the Lloyd’s Register Foundation World Risk Poll, gathered every 2 years since 2019 from 143 countries, and integrating country-level indicators on natural hazards, climate trends, governance quality, and economic status. Our multilevel models indicate that individual experiences with severe weather and projected increases in flood risks are among the strongest predictors of heightened risk perceptions. We also find that effects of factors like age and urbanicity vary across countries. To make our findings actionable, we group countries using cluster analysis into six distinct clusters based on preparedness, affluence, and severe weather concern. The results offer valuable insights for tailoring risk communication, enabling agencies to effectively design interventions, and ultimately enhancing preparedness and resource allocation.
Keywords
- Disaster and Crisis Management
- Risk Analysis and Management
- Analytics and Data Science
Status: accepted
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