Speaker
Description
Data Quality Rating (DQR) allows to assess the level of credibility of the Life Cycle Assessment (LCA) performed. Furthermore, it allows to identify gaps, drive subsequent data collection and enables fair comparison of systems. This makes DQR calculation one of the most important parameters to be assessed when analyzing the environmental performance of systems and of the underlying data. This presentation introduces an updated DQR methodology tailored specifically for LCAs in the space sector. The enhanced methodology incorporates sector-specific criteria and advanced evaluation metrics, addressing the unique challenges posed by the diverse and complex nature of space missions and technologies. Key updates include refined assessment parameters for data completeness, precision, and technological, temporal and geographical representativeness, as well as criteria taking in consideration both the collection of data and its modelling. The proposed DQR framework aims to standardize data quality evaluations, thus ensuring more robust and credible LCAs. Case studies demonstrating the application of the updated methodology in recent space missions will be discussed, highlighting its effectiveness in identifying data quality gaps and improving overall assessment accuracy.
