Quantified uncertainties in comparative LCAs: what can be concluded?

Life Cycle Assessment (LCA) of products gains meaning in comparative contexts where practitioners aim to disprove comparative assertions to inform a choice. However, interpretation of comparative LCAs is challenging given the... [ view full abstract ]

Life Cycle Assessment (LCA) of products gains meaning in comparative contexts where practitioners aim to disprove comparative assertions to inform a choice. However, interpretation of comparative LCAs is challenging given the presence of conflicting trade-offs and uncertainty. Rarely there is a single alternative that performs best in every single impact. Therefore, understanding trade-offs and their robustness between alternatives and across environmental impacts, in the context of uncertainty, is a crucial step in making a specific choice. To address this challenge, several statistical methods have been proposed and applied to problems consisting of two or more alternatives and impact categories. In this study we illustrate five interpretation methods (Heijungs and Kleijn 2001; Prado-Lopez et al. 2014, 2015; Henriksson et al. 2015; Heijungs et al. 2016) and apply them to an illustrative comparative LCA of vehicles. Three alternatives are assessed: full battery electric (FBE), a hydrogen fuel cell (HFC) and an internal combustion engine (ICE) passenger car. Ten impact categories are considered. We focus on the operationalization and applicability of these methods. We find that some methods belong to a confirmatory branch of statistics focused in drawing absolute conclusions while other approaches fit best within the exploratory branch. We provide a common notation, terminology and platform for all methods as well as a guide for practitioners to help motivate the use of such approaches in comparative LCAs with uncertainty. There is an immediate necessity to interpret comparative LCA results in an uncertain context because excluding uncertainty can lead to oversimplified and non-robust support to decision makers, as our illustration case showed.

References

Heijungs, R., P. Henriksson, and J. Guinée. 2016. Measures of Difference and Significance in the Era of Computer Simulations, Meta-Analysis, and Big Data. Entropy 18(10): 361.

Heijungs, R. and R. Kleijn. 2001. Numerical approaches towards life cycle interpretation five examples. The International Journal of Life Cycle Assessment 6(3): 141–148.

Henriksson, P.J.G., R. Heijungs, H.M. Dao, L.T. Phan, G.R. de Snoo, and J.B. Guinée. 2015. Product carbon footprints and their uncertainties in comparative decision contexts. PloS One 10(3)

Prado-Lopez, V., T.P. Seager, M. Chester, L. Laurin, M. Bernardo, and S. Tylock. 2014. Stochastic multi-attribute analysis (SMAA) as an interpretation method for comparative life-cycle assessment (LCA). International Journal of Life Cycle Assessment 19(2): 405–416. 

Prado-Lopez, V., B. a Wender, T.P. Seager, L. Laurin, and M. Chester. 2015. Tradeoff Evaluation Improves A Photovoltaic Case Study. Journal of Industrial Ecology 00(0): 1–9.