Ricerc@Sapienza

This study introduces a bio-economic approach to evaluate the influence of local socioeconomic contexts on complex processes of landscape transformation (urbanization, withdrawal of farming with woodland creation and loss in crop mosaics) in a sustainable development perspective.

We exploit the provincial variability of COVID-19 cases registered in Italy to select the territorial predictors of the pandemic. Absent an established theoretical diffusion model, we apply machine learning to isolate, among 77 potential predictors, those that minimize the out-of-sample prediction error. We first estimate the model considering cumulative cases registered before the containment measures displayed their effects (i.e. at the peak of the epidemic in March 2020), then cases registered between the peak date and when containment measures were relaxed in early June.