An Economics-Based Methodology for Analysis of the Inequitable Distribution of Flooding Risk
dc.contributor.author | El-Rafey, Jad | en |
dc.contributor.committeechair | Irish, Jennifer L. | en |
dc.contributor.committeemember | Shao, Yang | en |
dc.contributor.committeemember | Munoz Pauta, David Fernando | en |
dc.contributor.department | Civil and Environmental Engineering | en |
dc.date.accessioned | 2025-06-11T08:00:48Z | en |
dc.date.available | 2025-06-11T08:00:48Z | en |
dc.date.issued | 2025-06-10 | en |
dc.description.abstract | Flooding is a significant hazard that threatens both human life and property. There are a number of flooding mechanisms: fluvial, pluvial, and coastal, which are all being exacerbated by the effects of climate change. Coastal flooding hazards are especially dangerous given population dynamics leading to increased populations that are subject to greater flooding hazard due to sea level rise (SLR). Flooding hazards are taken on by people, but they are not distributed equally throughout populations. This thesis proposes an analysis methodology to analyze this inequity using the First Street Foundation (FSF) Flood Factor® (FF). This methodology produces qualitative and quantitative results based on the economics analysis concepts of the Lorenz curve (LC) and the Gini index (G). These results facilitate comparison of demographics to determine inequitable flooding distributions. This thesis also examines an application of the proposed methodology specifically examining coastal counties in the contiguous U.S. Results indicate that Nonwhite demographics typically experience greater inequity within their demographic in coastal regions while the distribution of flooding exposure is more equal among the White demographic. This new methodology enables determination of inequitable flooding distributions between demographics on a national scale. | en |
dc.description.abstractgeneral | Flooding is a dangerous hazard which has caused loss of life and property and will only become a more prevalent threat. There are a number of types of flooding, these being flooding from rivers, flooding due to excess rainfall, and flooding from coastal water. Coastal flooding is especially of interest due to sea level rise (SLR) and climate change, which increases the ocean water level and intensity of flooding events. These flooding events, no matter the type, impact people, but the distribution of that impact is not equal. In order to analyze this distribution, this thesis proposes an analysis framework to determine inequities of flooding hazard between demographics. This is performed by utilizing the First Street Foundation (FSF) Flood Factor® (FF) dataset. This dataset provides a simple measure of flooding hazard for areas throughout the U.S. that ranges from 1 to 10. The greater an area's FF, the greater the flooding hazard. The proposed methodology incorporates these FF values alongside U.S. Census data to examine flooding exposure across demographics. This is done using typical economic analysis tools called the Lorenz curve (LC) and the Gini index (G). An example application of this methodology is performed for the coastal states in the contiguous U.S. This new methodology assists in analyzing the inequities associated with flooding for different demographics, contributing to governance decisions. | en |
dc.description.degree | Master of Science | en |
dc.format.medium | ETD | en |
dc.identifier.other | vt_gsexam:44023 | en |
dc.identifier.uri | https://hdl.handle.net/10919/135458 | en |
dc.language.iso | en | en |
dc.publisher | Virginia Tech | en |
dc.rights | In Copyright | en |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
dc.subject | Inequity | en |
dc.subject | Flooding Risk | en |
dc.subject | Lorenz Curve | en |
dc.subject | Gini Index | en |
dc.title | An Economics-Based Methodology for Analysis of the Inequitable Distribution of Flooding Risk | en |
dc.type | Thesis | en |
thesis.degree.discipline | Civil Engineering | en |
thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
thesis.degree.level | masters | en |
thesis.degree.name | Master of Science | en |