Meta-comparisons: how to compare methods for LCA

Heijungs, R.; Dekker, E.

doi:doi:10.1007/s11367-022-02075-4

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-a-vis traditional regression-based approaches. Conclusion...Show moreIntroduction 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-a-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.Show less

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Meta-comparisons: how to compare methods for LCA