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Learning from the past: a machine-learning approach for predicting the resilience of locked-in regions after a natural shock

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  • Federico Fantechi
  • Marco Modica

Abstract

Italy has been affected by many different shocks in recent years, from the Great Recession to many natural hazards. While many studies have analysed the effects of natural and socio-economic shocks on urbanized and developed areas, very few have focused on locked-in and less developed regions. In this study we focus on the pernicious effects of three earthquakes that have affected the labour markets of rural and inner municipalities of Central Italy during the last 20 years. We adopt a machine-learning technique that allows us to provide a scenario five to seven years after the earthquake for 133 municipalities affected by the Central Italy earthquake in 2016.

Suggested Citation

  • Federico Fantechi & Marco Modica, 2023. "Learning from the past: a machine-learning approach for predicting the resilience of locked-in regions after a natural shock," Regional Studies, Taylor & Francis Journals, vol. 57(12), pages 2537-2550, December.
    • Handle: RePEc:taf:regstd:v:57:y:2023:i:12:p:2537-2550
    DOI: 10.1080/00343404.2022.2089644
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