Under the supervision of Professor Valerie Chavez-Demoulin, Professor Eric Jondeau, and Professor Linda Mhalla
Grade 6.0 (Highest grade)
This thesis focuses on the intersection of climate change and wealth inequality, particularly in the context of floods. It draws inspiration from Chavez-Demoulin, Jondeau, and Mhalla’s work in 2021, highlighting how carbon emissions intensify climate change, resulting in more extreme flood events. Additionally, it explores how GDP per capita shapes flood severity, showing that regions with higher GDP tend to encounter less severe flood events. Utilizing robust quantitative methodologies, particularly the application of Extreme Value Theory encompassing Poisson and Generalized Pareto Distribution (GPD) models, this research draws upon data derived from reputable sources such as EM-DAT, World Bank, and IPCC databases. Based on IPCC scenarios, the projections yield these key observations.
(1) Flood Frequency: A positive correlation is evident between flood frequency and CO2 emissions, with decreasing trends observed across most scenarios, except for SSP5-8.5, characterized by high emissions and pronounced climate system impacts.
(2) Average Death per Event: GDP inversely affects flood severity, with higher GDP regions experiencing milder events. Projections highlight severity reduction across scenarios. (3) Annual Deaths: Projections suggest decreased deaths across scenarios except for SSP5-8.5.
Implications span effective risk reduction, early warning systems, and vulnerability research in developing nations. This study enriches climate disaster literature by providing insights into flood management challenges, mitigation, and research paths. Acknowledged limitations include regional classifications, data scarcity, and modeling non-linear vulnerability dynamics. Further research could refine classifications, analyze temporal data variations, and model non-linear income-vulnerability relationships.