Life Cycle Assessment (LCA) is the primary method for evaluating environmental performance across a product’s life cycle, with the interpretation phase being critical for identifying significant issues and guiding improvements. Contribution Analysis (CA) is the main tool for understanding how processes and flows contribute to overall impacts, yet despite its importance, CA is not well standardized.
CA is widely used in LCA interpretation to identify key contributors to impacts, detect data quality issues, and select parameters for sensitivity analysis. However, CA lacks an overarching definition and structured methodology, leading to inconsistent and sometimes irreproducible results and a lower credibility of LCA studies. Various approaches to CA exist and can assess two different sides: Direct contributions (e.g. CO2 emitted from electricity production) and Indirect contributions (e.g. CH4 emitted during coal mining for the production of electricity...
Show moreLife Cycle Assessment (LCA) is the primary method for evaluating environmental performance across a product’s life cycle, with the interpretation phase being critical for identifying significant issues and guiding improvements. Contribution Analysis (CA) is the main tool for understanding how processes and flows contribute to overall impacts, yet despite its importance, CA is not well standardized.
CA is widely used in LCA interpretation to identify key contributors to impacts, detect data quality issues, and select parameters for sensitivity analysis. However, CA lacks an overarching definition and structured methodology, leading to inconsistent and sometimes irreproducible results and a lower credibility of LCA studies. Various approaches to CA exist and can assess two different sides: Direct contributions (e.g. CO2 emitted from electricity production) and Indirect contributions (e.g. CH4 emitted during coal mining for the production of electricity from coal). Each approach can assess one or both of these sides from a different perspective.
For large groups of processes however, combined Direct and Indirect CA cannot be easily applied. However, the Hypothetical Extraction Method (HEM), used in the field of Input Output Analysis, in principle is capable of providing such a perspective on CA. It was however unclear if that approach could be adapted to LCA and what insights it could provide.
CA is especially important in the context of prospective LCA (pLCA), where scenario-driven change in the data makes it harder to interpret results. In pLCA, the most used tool currently is Premise to generate future versions of background databases based on scenario storylines. To learn about the specific challenges that different approaches to CA pose and learn how they could be overcome, this thesis applied CA in three pLCA cases. These cases were chosen for the following reasons: 1) cobalt production given its low production scale but high expected change in demand; 2) sand and gravel production, given concrete demand and the vastly larger production scale relative to cobalt; and 3) evaluation of the pLCA tool Premise via analysis of nine commonly used industrial products to clarify drivers of future impacts.
In this way, this thesis aimed to realize the following main objective: to structure and -where necessary- formalize CA and to learn from practical cases so that this toolbox of approaches becomes more useful to LCA practitioners.
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