Abstract:
Location-decision in investment processes requires widespread analysis of a target place's social, environmental, and economic characteristics. This dissertation aims to assess a region's competitiveness by creating a composite indicator that groups a wide range of information. More in detail, the proposed indicator permits the ranking of Italy's regions according to fourteen variables split into four subcategories, namely Quality of life (Physical Well-being, Pollution, Education, Crime), Governance (Bureaucracy, Public Debt), Economics and Finance (Market Size, Interest Rate, Employment, Export) and System of Innovation (Transport Network Efficiency, Research and Development, Digitalization, Energy). The region-related data are extracted from verified and reliable sources and are combined as a weighted average. Given the total lack of weighting information, a multicriteria decision-aiding method called Stochastic Multicriteria Acceptability Analysis (SMAA) was utilized to generate unbiased sets of weights by randomly extracting the values from a uniform distribution. After applying the latest normalization method to the rigorously collected data, the platform RStudio is used to build the final ranking illustrated as each region's probability of occupying a rank position. Lombardia is in the lead, followed by other northern regions, while the southern ones occupy the last positions, with Calabria being the worst. Such ranking is then employed as a starting point for the location of some recycling facilities to promote strategic decisions within circular economy processes for the fashion industry.
