Correlations and complementarities in data and methods through Principal Components Analysis (PCA) applied to the results of the SPIn-Eco Project.

Abstract

This paper demonstrates how the results from different methods can be interpreted on the basis of a statistical approach that can help find new hints in the evaluation of sustainability at the territorial level. The SPIn-Eco Project for the Province of Siena (Italy) is an example of an environmental sustainability assessment of an area using methods that are suitable for a large system: Ecological Footprint, Greenhouse Gas Inventory, Extended Exergy Analysis, Emergy Evaluation, and Remote Sensing. The calculation of many indicators, derived from these methods, has prompted us to use a statistical method (Principal Components Analysis, PCA) to understand the degree of similarity/congruence of the indicators (here we have examined 26 of them) and the possibility of recognizing patterns or clusters in the description of the 36 municipalities that compose the Province of Siena. Among the results, unexpectedly, emergy flow and the Ecological Footprint resulted as being completely uncorrelated, apparently due to the importance that the non-renewable part of the emergy holds in the evaluation. The municipalities of the province are considerably spread out over the graphs, even though that of Siena is quite far from the rest along the first dimension. In addition, we were able to distinguish between more homogeneous districts (sets of municipalities), such as Val di Merse and Val d'Orcia, and very diverse ones, such as Val d'Elsa and Val di Chiana.