Expanded disaster risk assessment using agent-based modeling: a case study on floods in Sri Lanka

Brian Walsh and Stephane Hallegatte

Chapter 17 in Handbook on the Economics of Disasters, 2022, pp 355-388 from Edward Elgar Publishing

Abstract: Standard disaster risk assessments use asset losses to measure severity. This chapter adds socioeconomic resilience and welfare losses to the conventional approach. This expanded risk assessment provides new insights into differential disaster risks by considering household recovery dynamics. In application to Sri Lanka, we find that regular flooding is a major source of transient poverty. Average annual wellbeing losses due to fluvial flooding are estimated at US$119 million per year, more than double the asset losses of US$78 million. The bottom income quintile suffers just 7 percent of these asset losses, but 32 percent of wellbeing losses. Asset losses are reported to be highly concentrated in Colombo district, and wellbeing losses are more widely distributed throughout the country. In most districts, the Sinhalese majority is found to have higher socioeconomic resilience than minority households.

Keywords: Development Studies; Economics and Finance; Environment; Sociology and Social Policy (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.elgaronline.com/view/edcoll/9781839103735/9781839103735.00029.xml (application/pdf)
Our link check indicates that this URL is bad, the error code is: 503 Service Temporarily Unavailable

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:elg:eechap:19599_17

Ordering information: This item can be ordered from
http://www.e-elgar.com

Access Statistics for this chapter

More chapters in Chapters from Edward Elgar Publishing
Bibliographic data for series maintained by Darrel McCalla ().