Abstract
This study examines the impact of failing to achieve Sustainable Development Goals (SDGs) on disaster and pandemic vulnerabilities, providing a country-level perspective to inform resilience planning. The study introduces the concept of SDG-related risk, defined as the probability of not achieving the desired SDG, and classifies these risks into three categories: high, medium, and low. Using a Bayesian Belief Network (BBN) framework, two probabilistic models are developed to evaluate the influence of SDG performance on disaster risk and COVID-19 vulnerability across 165 countries. The results highlight that shortcomings in SDGs such as ‘quality education’, ‘sustainable cities and communities’, ‘no poverty’, and ‘affordable and clean energy’ significantly increase disaster and pandemic risks. Conversely, strong performance in ‘peace, justice and strong institutions’ and ‘life on land’ enhances systemic resilience. Countries with very high disaster risk are particularly exposed to deficiencies in SDGs related to ‘peace, justice and strong institutions’, ‘sustainable cities and communities’, and ‘good health and well-being’. For COVID-19 risk, ‘affordable and clean energy’ emerges as the most critical SDG influencing high-risk exposure, whereas ‘climate action’ is pivotal in predicting low-risk states. These findings demonstrate the cascading risks posed by failing to achieve critical SDGs and emphasize the need for targeted interventions to mitigate vulnerabilities to disasters and pandemics, providing actionable insights for sustainable resilience strategies.
| Original language | British English |
|---|---|
| Pages (from-to) | 159-174 |
| Number of pages | 16 |
| Journal | Global Transitions |
| Volume | 7 |
| DOIs | |
| State | Published - Jan 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 1 No Poverty
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SDG 4 Quality Education
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SDG 7 Affordable and Clean Energy
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SDG 11 Sustainable Cities and Communities
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SDG 13 Climate Action
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SDG 17 Partnerships for the Goals
Keywords
- Bayesian belief network
- COVID-19
- Disaster
- Resilience
- Risk
- Sustainability
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