Uncertainty management in life cycle assessment using the Beta-DQR method: e-mobility case study on bicycles production

Abstract

LCA is challenged by uncertainties in both input data and LCA methodology, which must be addressed for reliable comparisons. This study introduces a novel Beta-DQR method, designed to robustly manage LCA uncertainties. The Beta-DQR method integrates the strenghts of the beta distribution and Data Quality Rating. The method starts with a contribution analysis to identify the most influential background data. It then calculates the quality of background data using a DQR rating system. The DQR values are transformed into beta distribution parameters, which are then used in Monte Carlo simulations. By conducting simulation runs, the method generates probabilistic results that account for data quality and variability using discrenibility analysis. An example of a comparative case study of bicycle and electric bicycle production was chosen to validate the Beta-DQR method. The case study topic was justified by the results of conducted social survey. The deterministic analysis indicated that the electric bicycle had a 13.51% higher carbon footprint than the standard bicycle. The Beta-DQR method revealed that the difference was not statistically significant when uncertainties were considered. This method also reduced the number of datasets requiring quality assessment from 22 to 5 for the standard bicycle and from 24 to 7 for the electric bicycle, thereby saving time while maintaining high reliability. The Beta-DQR method provides a better understanding of the LCA results, supporting environmental decision-making. It enhances LCA results reliability, offers a practical, time-efficient approach, and advances uncertainty management, particularly in comparative LCA of any complex systems.