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Comparing the Economic Impact of Natural Disasters Generated by Different Input-Output Models: An Application to the 2007 Chehalis River Flood (WA).

Andre F T AvelinoSandy Dall'erba
Published in: Risk analysis : an official publication of the Society for Risk Analysis (2018)
Due to the concentration of assets in disaster-prone zones, changes in risk landscape and in the intensity of natural events, property losses have increased considerably in recent decades. While measuring these stock damages is common practice in the literature, the assessment of economic ripple effects due to business interruption is still limited and available estimates tend to vary significantly across models. This article focuses on the most popular single-region input-output models for disaster impact evaluation. It starts with the traditional Leontief model and then compares its assumptions and results with more complex methodologies (rebalancing algorithms, the sequential interindustry model, the dynamic inoperability input-output model, and its inventory counterpart). While the estimated losses vary across models, all the figures are based on the same event, the 2007 Chehalis River flood that impacted three rural counties in Washington State. Given that the large majority of floods take place in rural areas, this article gives the practitioner a thorough review of how future events can be assessed and guidance on model selection.
Keyphrases
  • machine learning
  • systematic review
  • south africa
  • high intensity
  • deep learning
  • single cell
  • quality improvement