Contribution analysis in LCA: an overview of approaches and when to apply them
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Purpose
Contribution analysis (CA) is an essential method to understand and communicate life cycle assessment (LCA) results. Different approaches to CA have been used to answer different questions. However, it is often unclear which approach is used in LCA studies, leaving confusion as to how contributions are actually assessed. This makes a correct interpretation or replication difficult and can even lead to ill-founded conclusions. This study aims at a clear terminology for transparent CA communication.
Method
First, we introduce eight approaches to CA used in the literature. Then, we introduce an example case study to compare each CA approach against. We then discuss the eight approaches, discussing from what and to what they contribute. We also make a distinction between the direct and indirect perspectives, where direct contributions are from elementary flows (EF) of processes, while indirect contributions are from all EF of processes contributing to the intermediate flows (products).
Results and discussion
We identify and describe several approaches for direct CA: (a) Individual elementary flow CA, for specific individual EFs from single processes (individual EF CA). (b) EF CA, for one EF across all processes, e.g. all CO 2 flows. (c) Process CA, for all EFs per process, e.g. contribution to climate change impact from the process ‘electricity production from coal’. (d) Grouping can also be applied to these approaches, e.g. process group CA for all ‘electricity production’ processes. Direct and indirect contributions can also be quantified. First-tier CA, measuring the direct contribution of the functional unit (FU) process and indirect contribution of each intermediate flow of the FU. Life cycle stage CA, e.g. calculating the contributions of life cycle stages like ‘production’, ‘use’, and ‘end of life’. And finally, path CA—often visualized in Sankey diagrams—e.g. showing the ‘path’ contributions take to the FU. We compare the results, advantages, and disadvantages of each approach, discuss general limitations of CA, and give recommendations on reporting CA.
Conclusion
Our study can help guide practitioners in choosing relevant CA approaches for their studies to gain better insights and more transparently communicate and report their results. This should contribute to a higher quality and more reproducible body of LCA literature and more well-founded conclusions.
