Statistical analysis of the ecoinvent database to uncover relationships between life cycle impact assessment metrics

A wide range of impact assessment methodologies are available for quantifying the life cycle environmental impact of anthropogenic activities. The calculation of these metrics requires typically large amounts of data that are hard to collect in practice. To shed light on the extent to which these input data can be reduced (while yet obtaining accurate impact assessment values), this work applies a multivariate statistical analysis to the ecoinvent database. Numerical results show that many life cycle impact assessment (LCIA) metrics are highly correlated, but despite this high level of correlation no single indicator is capable of predicting the others with accuracy via univariate linear regression. Our findings open new avenues for the development of advanced streamlined LCIA methods based on multiple data regression that could exploit this high level of correlation and potentially lead to significant savings in time and resources associated with LCA studies.