Meta-comparisons: how to compare methods for LCA?

Abstract

Introduction: Many methodological papers report a comparison of methods for LCA, for instance comparing different impact assessment systems, or developing streamlined methods. A popular way to do so is by studying the differences of results for a number of products. We refer to such studies as quasi-empirical meta-comparisons. Review of existing approaches: A scan of the literature reveals that many different methods and indicators are employed: contribution analyses, Pearson correlations, Spearman correlations, regression, significance tests, neural networks, etc. Critical discussion: We critically examine the current practice and conclude that some of the widely used methods are associated with important deficits. A new approach: Inspired by the critical analysis, we develop a new approach for meta-comparative LCA, based on directional statistics. We apply it to several real-world test cases, and analyze its performance vis-à-vis traditional regression-based approaches. Conclusion: The method on the basis of directional statistics withstands the tests of changing the scale and unit of the training data. As such, it holds a promise for improved method comparisons.