Enhancement of Small Towns in Inland Areas. A Novel Indicators Dataset to Evaluate Sustainable Plans

• Main Authors: Antonio Nesticò, Pierfrancesco Fiore, Emanuela D’Andria
• Format: Article
• Language: English
• Published: MDPI AG 2020-08-01
• Series: Sustainability
• Subjects: sustainable development; small towns valorization; architectural recovery; analytic hierarchy process (AHP); multi-criteria indicators; economic evaluation
• Online Access: https://www.mdpi.com/2071-1050/12/16/6359

In response to the abandonment and depopulation of small towns in inland areas, it is necessary to provide analysis and technical-economic evaluation tools with the aim of selecting effective recovery and valorization strategies. In the light of what criteria and indicators should this selection be carried out? The principles of sustainability guide us to a new definition of social, economic, environmental, and historical-architectural criteria. The intention is to outline a new way of classifying the judgment criteria, exclusively referring to the peculiarities of small towns. In turn, the criteria are specifically defined in sixteen sub-criteria, again able to represent the salient features of small municipalities: Local traditions, <i>genius loci</i>, urbanization levels, but also prevailing economy, environmental (flora and fauna, water, soil, air, etc.), and historical-architectural components (relations between the small town and the immediate context, formal relationship between building and urban core, etc.). This is followed by the drafting of a novel dataset of evaluation indicators, capable of expressing the project actions’ capacity to pursue the objectives expressed by the criteria. These are datasets that give back 24 indicators for the social sub-criteria, 42 for the economic sub-criteria, 34 for the environmental ones, and 38 for the historical-architectural ones. The goal-criteria-subcriteria-indicators structure outlined in this paper opens up research perspectives on the characterization of a hierarchical model of multi-criteria analysis.