IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v162y2020i2d10.1007_s10584-020-02825-z.html
   My bibliography  Save this article

Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability

Author

Listed:
  • Christian L. E. Franzke

    (University of Hamburg
    University of Hamburg)

  • Herminia Torelló i Sentelles

    (University of Hamburg
    University of Hamburg)

Abstract

Weather and climate hazards cause too many fatalities each year. These weather and climate hazards are projected to increase in frequency and intensity due to global warming. Here, we use a disaster database to investigate continentally aggregated fatality data for trends. We also examine whether modes of climate variability affect the propensity of fatalities. Furthermore, we quantify fatality risk by computing effective return periods which depend on modes of climate variability. We find statistically significant increasing trends for heat waves and floods for worldwide aggregated data. Significant trends occur in the number of fatalities in Asia where fatalities due to heat waves and floods are increasing, while storm-related fatalities are decreasing. However, when normalized by population size, the trends are no longer significant. Furthermore, the number of fatalities can be well described probabilistically by an extreme value distribution, a generalized Pareto distribution (GPD). Based on the GPD, we evaluate covariates which affect the number of fatalities aggregated over all hazard types. For this purpose, we evaluate combinations of modes of climate variability and socio-economic indicators as covariates. We find no evidence for a significant direct impact from socio-economic indicators; however, we find significant evidence for the impact from modes of climate variability on the number of fatalities. The important modes of climate variability affecting the number of fatalities are tropical cyclone activity, modes of sea surface temperature and atmospheric teleconnection patterns. This offers the potential of predictability of the number of fatalities given that most of these climate modes are predictable on seasonal to inter-annual time scales.

Suggested Citation

  • Christian L. E. Franzke & Herminia Torelló i Sentelles, 2020. "Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability," Climatic Change, Springer, vol. 162(2), pages 507-525, September.
  • Handle: RePEc:spr:climat:v:162:y:2020:i:2:d:10.1007_s10584-020-02825-z
    DOI: 10.1007/s10584-020-02825-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10584-020-02825-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10584-020-02825-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Olivier Deschênes & Enrico Moretti, 2009. "Extreme Weather Events, Mortality, and Migration," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 659-681, November.
    3. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    4. Bingshun He & Xianlong Huang & Meihong Ma & Qingrui Chang & Yong Tu & Qing Li & Ke Zhang & Yang Hong, 2018. "Analysis of flash flood disaster characteristics in China from 2011 to 2015," 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. 90(1), pages 407-420, January.
    5. Roger Koenker, 2017. "Quantile Regression: 40 Years On," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 155-176, September.
    6. Christian L. E. Franzke, 2017. "Impacts of a Changing Climate on Economic Damages and Insurance," Economics of Disasters and Climate Change, Springer, vol. 1(1), pages 95-110, June.
    7. Marian Leimbach, Nico Bauer, Lavinia Baumstark, Michael Luken and Ottmar Edenhofer, 2010. "Technological Change and International Trade - Insights from REMIND-R," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    8. M. Carmen Alvarez-Castro & Davide Faranda & Pascal Yiou, 2018. "Atmospheric Dynamics Leading to West European Summer Hot Temperatures Since 1851," Complexity, Hindawi, vol. 2018, pages 1-10, January.
    9. Laura A. Bakkensen & Robert O. Mendelsohn, 2016. "Risk and Adaptation: Evidence from Global Hurricane Damages and Fatalities," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 3(3), pages 555-587.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Franzke, Christian L.E., 2021. "Towards the development of economic damage functions for weather and climate extremes," Ecological Economics, Elsevier, vol. 189(C).
    2. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    3. Damian Clarke & Manuel Llorca Jaña & Daniel Pailañir, 2023. "The use of quantile methods in economic history," Historical Methods: A Journal of Quantitative and Interdisciplinary History, Taylor & Francis Journals, vol. 56(2), pages 115-132, April.
    4. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
    5. Fabio Zambuto, 2021. "Quality checks on granular banking data: an experimental approach based on machine learning," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Micro data for the macro world, volume 53, Bank for International Settlements.
    6. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2019. "Quantile Diffusions for Risk Analysis," Papers 1912.10866, arXiv.org, revised Sep 2021.
    7. Francisco J. Delgado, 2021. "On the Determinants of Fiscal Decentralization: Evidence From the EU," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(56), pages 206-206, February.
    8. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.
    9. Ahmed, Walid M.A., 2021. "Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    10. Anne M. Lausier & Shaleen Jain, 2018. "Diversity in global patterns of observed precipitation variability and change on river basin scales," Climatic Change, Springer, vol. 149(2), pages 261-275, July.
    11. Guillen, Montserrat & Bermúdez, Lluís & Pitarque, Albert, 2021. "Joint generalized quantile and conditional tail expectation regression for insurance risk analysis," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 1-8.
    12. Aicha Kharazi & Francesco Ravazzolo, 2023. "Regulatory Collateral Requirements and Delinquency Rate in a Two-Agent New Keynesian Model," Working Paper series 23-03, Rimini Centre for Economic Analysis.
    13. Xolani Sibande & Rangan Gupta & Riza Demirer & Elie Bouri, 2023. "Investor Sentiment and (Anti) Herding in the Currency Market: Evidence from Twitter Feed Data," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(1), pages 56-72, January.
    14. Fabio Bellini & Ilaria Peri, 2021. "An axiomatization of $\Lambda$-quantiles," Papers 2109.02360, arXiv.org, revised Jan 2022.
    15. Chavleishvili, Sulkhan & Manganelli, Simone, 2019. "Forecasting and stress testing with quantile vector autoregression," Working Paper Series 2330, European Central Bank.
    16. Rui Evangelista & João Andrade E Silva & Esmeralda A. Ramalho, 2021. "How heterogeneous is the impact of energy efficiency on dwelling prices? Evidence from the application of the unconditional quantile hedonic model to the Portuguese residential market," Working Papers REM 2021/0186, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    17. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    18. Francesca Caselli & Mr. Philippe Wingender, 2018. "Bunching at 3 Percent: The Maastricht Fiscal Criterion and Government Deficits," IMF Working Papers 2018/182, International Monetary Fund.
    19. Xu Chen & Surya T. Tokdar, 2021. "Joint quantile regression for spatial data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 826-852, September.
    20. D Barrera & S Crépey & E Gobet & Hoang-Dung Nguyen & B Saadeddine, 2022. "Learning Value-at-Risk and Expected Shortfall," Working Papers hal-03775901, HAL.

    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:spr:climat:v:162:y:2020:i:2:d:10.1007_s10584-020-02825-z. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.