The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A global multi-hazard risk analysis of road and railway infrastructure assets
Open accessAbstract
Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that ~27% of all global road and railway assets are exposed to at least one hazard and ~7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which ~73% is caused by surface and river flooding. Global EAD are small relative to global GDP (~0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on ~60% of roads exposed to a 1/100 year flood event.
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Citations
Cites
The rise and fall of the Environmental Kuznets Curve
Stern, D. I. (2004) · World Development · doi:10.1016/j.worlddev.2004.03.004 · Cited by 4,013
Ensemble flood risk assessment in Europe under high end climate scenarios
Alfieri, L., Feyen, L., Dottori, F., & Bianchi, A. (2015) · Global Environmental Change · doi:10.1016/j.gloenvcha.2015.09.004 · Cited by 346
Metadata
- Authors
- Koks, E. E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S. A., Hall, J. W., & Hallegatte, S.
- Venue
- Nature Communications
- Published
- 2019-06-25
- Volume
- 10
- Issue
- 1
- Pages
- 2677-2677
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2955829472
- Cited by
- 603
- References
- 2
- FWCI
- 44.48
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
