Natural disasters, particularly floods, pose a significant threat to cities and communities, and their fre- quency and severity are increasing due to climate change. Data from the EM-DAT database reveals that floods have been the most frequent natural disasters since 2002. Projections from the Intergovern- mental Panel on Climate Change suggest that heavy rainfall events will continue to increase, making floods more likely in the 21st century.
There is an urgent need to address the socio-economic impact of these disasters, particularly on the most vulnerable populations. Economic losses not only disrupt efforts to mitigate climate change, but also worsen inequalities, especially between high and low-income groups. Traditional risk assessments are based solely on property losses. Previous research highlighted that this is not sufficient to measure the socio-economic impact of floods. An interdisciplinary approach is required to integrate methodolo- gies, improve representation of vulnerable populations and fully understand the complex challenges posed by poverty and climate change.
This study uses a modelling framework applied to a case study of Charleston, South Carolina. For the case study, an extreme hurricane was chosen to test the model. The framework aims to measure well-being losses due to flooding and to evaluate the impact of insurance policies on vulnerable groups. The well-being losses are derived from household consumption levels.
Results from the case study show that total well-being losses exceeded total asset losses. Absolute well-being losses were similar across income groups. The relative well-being losses, represented per dollar of damaged assets, were higher for the low-income groups. The implementation of insurance policies reduced overall well-being losses. When the scenario with full insurance coverage is in force, there are still well-being losses. In order to create complete resilience, it is necessary to implement multiple measures.
Further research is recommended to improve the modelling framework. Many assumptions increased the uncertainty of the model. Detailed and additional data would improve the certainty of the results. Ultimately, the research aims to provide useful evidence for policy makers and stakeholders to make more informed decisions to build resilient communities and reduce inequalities in the face of climate change.