Statistical based approach for uncertainty analysis in life cycle assessment: a case study in textile industry

Life cycle assessment (LCA) is widely recognised as an established tool for analysing the environmental impact of a product over the entire life cycle. As uncertainty is inevitably present along the analysis process, it is essential to quantify and treat it explicitly. In this paper, Monte Carlo (MC) simulation is implemented for the stochastic study of life cycle inventory (LCI) concerning the production process of polyester yarn (POY) based on the data from a Chinese company located in eastern China. The results of this study can be used as the initial step in performing a full LCA analysis in the textile industry. Moreover, it can be concluded that the MC approach is a powerful method for quantifying parameter uncertainty in LCI studies and can be applied for further uncertainty investigation such as uncertainty propagation, sensitivity analysis and communication.