Social and economic impact

Assessing the consequences of earthquakes on a given urban area is essential to any seismic risk reduction policy/strategy. The number of social and economic losses has risen considerably in recent decades, but losses in terms of exposure (population, GDP) are falling, interpreted as an improvement in environments thanks to international seismic risk reduction programs (e.g. Hyogo Framework and Sendai Framework) supported in particular on the annual losses probability (or occurrence) assessment.

The compilation of information in shared global international databases enables the calibration of empirical models for predicting socio-economic losses as a function of seismic (magnitude, distance) and socio-economic (population exposed and local GDP) parameters. Further validation is required, in particular to define models for predicting local, regional or global losses.

In the same vein, large-scale vulnerability assessment (e.g., city, region, country...) is also essential for spatial modeling of physical damage and associated losses. The sharing of in-situ (macroseismic) post-seismic data, the constitution of ever larger databases, and the emergence of learning and artificial intelligence methods open up fantastic prospects for dynamic exposure modelling and data-driven damage prediction.

References

• Dollet, C., & Guéguen, P. (2022). Global occurrence models for human and economic losses due to earthquakes (1967–2018) considering exposed GDP and population. Natural Hazards, 110(1), 349-372.
• Ghimire, S., Guéguen, P., Giffard-Roisin, S., & Schorlemmer, D. (2022). Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake. Earthquake Spectra, 38(4), 2970-2993.

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