Renaissance of villages for the revitalization of marginal areas

Abstract

There is now a clear disparity between major cities and villages located in marginal areas of Italy. Progressive depopulation of inland areas and urban polarization such as consolidated territorial dynamics are difficult to dampen and adapt to the new paradigms of sustainable territorial development, although they have been abruptly redirected by the Covid 19 pandemic. The instability created by this pandemic offers the opportunity to redefine new parameters of intervention and new scenarios for the development of territories in relation to the new needs of decentralization and physical distancing. The project “Renaissance of villages for the revitalization of marginal areas” aims to create the conditions to repopulate and rebalance shrinking territories by establishing new centers of attractiveness. This project envisages the active involvement of municipalities and local authorities with the aims at implementing the multi-sectoral analysis of the tangible and intangible values of territories. It intends to develop an interactive web dashboard to be provided to municipalities in order to create both a learning environment and a spatial decision support system for future local policy actions towards a sustainable participatory local development. In this way, it is proposed a functional method with a place-based approach to managing the existing territorial complexity through innovative models of territorial governance and policymaking, among them the effective implementation of participatory and multi-actor visions of territorial development. Specifically, this paper provides the Italian villages’ archetypes through the quantitative spatial cluster multivariate analysis, which is the basis for the construction of the dashboard. To cluster the villages, the main variables have been identified, assesses, and mapped. The results are fundamental in order to define the future scenarios for each archetype assessing the Key Performance indicators (KPIs).