A quantitative analysis of Rural Sustainability using a decision support system: the case of Belluno province, Italy

Abstract

The recent accelerated urbanization has led to an increase of people living in cities, nevertheless looking at the statistics, a significant proportion of population keeps occupying rural areas. Predominantly rural and intermediate regions represent 90% of the territory and 57,3% of the population in the EU-27. Therefore, rural development plays a key role in policy making, particularly in the European Union where it is supported by specific, financial programs. Beyond rurality, another crucial principle in long-term EU policies and strategies is represented by sustainability. Despite their well-known and increased importance, measuring sustainability and rurality of a specific area remains a difficult task due to its complexity and multidimensional nature. It is extremely difficult to define rurality and translate it into empirical assessment. The objective of this study is to develop a multi-criteria decision support system through an innovative approach to assess the level of rural sustainability in the entire Province of Belluno (North-eastern Italy). In this sense, we have attempted to translate selected qualities, describing rural sustainability in the 67 municipalities of the Province, into quantitative benchmarks and to create an information system considering a causal-effect relationship. Through the Rough Set Theory (RST) method, a reliable mathematical tool for classification, analysis and inference related to uncertain and incomplete information set (Pawlak, 1982), we were able to analyze which are the factors that led to different levels of rural sustainability, in order to obtain the so called decision rules. Given these decision rules we will be able to give a consistent conclusion and an explanation for the collected information system. After all, it can be also stated that RST is an effective and high-potential tool for managing, planning and support decision making at a strategic scale.