IDEAS home Printed from https://ideas.repec.org/p/chf/rpseri/rp2163.html
   My bibliography  Save this paper

Climate-Related Disasters and the Death Toll

Author

Listed:
  • Valérie Chavez-Demoulin

    (University of Lausanne - School of Economics and Business Administration (HEC-Lausanne))

  • Eric Jondeau

    (University of Lausanne - Faculty of Business and Economics (HEC Lausanne); Swiss Finance Institute)

  • Linda Mhalla

    (HEC Montreal - Department of Decision Sciences; University of Geneva, Geneva School of Economics and Management, Research Center for Statistics)

Abstract

With climate change accelerating, the frequency of climate disasters is expected to increase in the decades to come. There is ongoing debate as to how different climatic regions will be affected by such an acceleration. In this paper, we describe a model for predicting the frequency of climate disasters and the severity of the resulting number of deaths. The frequency of disasters is described as a Poisson process driven by aggregate CO2 emissions. The severity of disasters is described using a generalized Pareto distribution driven by the trend in regional real gross domestic product (GDP) per capita. We predict the death toll for different types of climate disasters based on the projections made by the Intergovernmental Panel on Climate Change for the population, the regional real GDP per capita, and aggregate CO2 emissions in the "sustainable" and "business-as-usual" baseline scenarios.

Suggested Citation

  • Valérie Chavez-Demoulin & Eric Jondeau & Linda Mhalla, 2021. "Climate-Related Disasters and the Death Toll," Swiss Finance Institute Research Paper Series 21-63, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2163
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3918201
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. May Haggag & Eman Rezk & Wael El-Dakhakhni, 2023. "Machine learning prediction of climate-induced disaster injuries," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3645-3667, April.

    More about this item

    Keywords

    Climate change; Climate disasters; Death toll; Frequency and severity;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:chf:rpseri:rp2163. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ridima Mittal (email available below). General contact details of provider: https://edirc.repec.org/data/fameech.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.