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A Cost Effective Solution to Reduce Disaster Losses in Developing Countries: Hydro-Meteorological Services, Early Warning, and Evacuation
Open accessAbstract
In Europe, it can be estimated that hydro-meteorological information and early warning systems save several hundreds of lives per year, avoid between 460 million and 2.7 billion Euros of disaster asset losses per year, and produce between 3.4 and 34 billion of additional benefits per year through the optimization of economic production in weather-sensitive sectors (agriculture, energy, etc.). The potential for similar benefits in the developing world is not only proportional to population, but also to increased hazard risk due to climate and geography, as well as increased exposure to weather due to the state of infrastructure. This analysis estimates that the potential benefits from upgrading to developed-country standards the hydro-meteorological information production and early warning capacity in all developing countries include: (i) between 300 million and 2 billion USD per year of avoided asset losses due to natural disasters; (ii) an average of 23,000 saved lives per year, which is valued between 700 million and 3.5 billion USD per year using the Copenhagen Consensus guidelines; and (iii) between 3 and 30 billion USD per year of additional economic benefits. The total benefits would reach between 4 and 36 billion USD per year. Because some of the most expensive components of early warning systems have already been built (e.g., earth observation satellites, global weather forecasts), these investments are relatively modest, estimated here around 1 billion US per year, reaching benefit-cost ratios between 4 and 36.
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Cites
The impact of climate change on global tropical cyclone damage
Mendelsohn, R., Emanuel, K., Chonabayashi, S., & Bakkensen, L. (2012) · Nature Climate Change · doi:10.1038/nclimate1357 · Cited by 805
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Self-citationFlood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review
Cea, L., & Costabile, P. (2022) · Hydrology · doi:10.3390/hydrology9030050 · Cited by 317
Flood forecasting with machine learning models in an operational framework
Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., Voloshin, D., Kratzert, F., Elidan, G., Dror, G., et al. (2022) · Hydrology and Earth System Sciences · doi:10.5194/hess-26-4013-2022 · Cited by 267
Metadata
- Authors
- Hallegatte, S.
- Venue
- World Bank, Washington, DC eBooks
- Publisher
- World Bank, Washington, DC
- Published
- 2012-05
- OpenAlex
- W1553239328
- Cited by
- 181
- References
- 1
- Type
- report
- Language
- en
- Open access
- green
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