Life Cycle Inventory of cutting technologies in the ornamental stone supply chain

The main goal of the PhD research project is to contribute to the development of methodologies and Life Cycle Inventory data of the most representative techniques and technologies in the ornamental stone supply chain. The realisation of Life Cycle datasets, currently scarcely available in Life Cycle databases, aims to provide a practical tool to enterprises and researchers dealing with sustainability issues in the stone sector. The interest in enhancing the stone supply chain sustainability has been boosted by the recent European policies on Circular Economy and Raw materials, which are encouraging the passage from a linear economy (made of the phases of extraction-production-use-disposal) to a circular economy, where the value of products, materials and resources is maintained in the economy for as long as possible (European Commission, 2015). Moreover, sustainable supply chain improvements are urged by the market competition, represented by stone materials from developing countries and by other Italian construction materials, whose sectors have started thinking in terms of sustainability from quite a long time, gaining a priority with, for examples, Green Public Procurements. In this context, the Life Cycle Assessment (LCA) has been identified as the best framework for assessing the potential environmental impacts of products by the European Commission’s Integrated Product Policy Communication (COM (2003) 302). LCA is indeed a scientific and standardized tool which considers the entire life cycle of a product/process in order to quantify materials, energy and emissions and to evaluate the environmental consequences. Nevertheless, in the stone sector, LCA is hindered by the current scarce availability of Life Cycle Inventory datasets on the specific stone supply chain techniques and technologies. In this context, the PhD project here presented gives a contribute to fill the gap in LCI datasets availability and quality. To this aim primary data were collected in Italian quarries, transformation plants and cutting tool enterprises (in particular, 4 marble quarries, 10 gneiss quarries, 7 transformation plants and 3 tool producers). When necessary, secondary data (from papers, patents and technical sheets) were also collected to complete the inventory or to cross-check the measured data. On the basis of these data, the average datasets of the stone supply chain techniques were modelled using Gabi software. Finally, primary data uncertainty on the collected data was handed through the calculation of the standard deviation, to assess the value ranges around the mean values and to evaluate the consequent precision of the LCI datasets. The modelled LCI datasets have been also submitted to an internal quality control based on impact assessment results. Uncertainty analyses have been developed through the calculation of standard deviation on some impact results and through Monte Carlo stochastic simulations (run with 1000 iterations), which evaluate the stability of the results toward random parameters constellations. In addition, the developed LCI datasets on stone technologies have been organised in a cradle-to-gate LCA model which, through editable parameters, can be easily adapted to perform LCA of specific stone supply chains. It has been created a unique model comprehending technologies for both soft and hard stones, in order to allow the model to be employed also by enterprises working with both the materials in the same plant. Finally, a collaboration with the Brazilian CETEM research centre, led to the development of a preliminary study on Social Life Cycle Assessment (SLCA). Following the UNEP/SETAC guidelines on SLCA (2009), secondary data have been collected for both the Italian and the Brazilian ornamental stone sectors. Questionnaires to collect primary data are proposed with the aim of supporting future works on stone social sustainability. This PhD study is therefore expected to boost to use of the LCA tools among stone enterprises and to provide data able to support researchers and decision makers.