Archetypes of Goal and Scope Definitions for Consistent Allocation in LCA

TLDR

The selection of an allocation procedure in LCA depends on the goal and scope of the analysis, yet it is often unclear when partitioning, system expansion, attributional, or consequential approaches should be applied. The paper clarifies how goal and scope influence the choice of modeling approaches in LCA and develops archetypes of goal and scope definitions. The authors develop archetypes that capture the minimal information needed to select an allocation procedure and discuss how these can guide the choice between attributional and consequential approaches. An illustrative example shows that a single product impact question can generate at least 15 distinct research questions and require at least five different modeling methods.

Abstract

The selection of an appropriate allocation procedure for co-production and recycling in Life Cycle Assessment (LCA) depends on the goal and scope of the analysis. However, it is not always clear when partitioning or system expansion can be applied, or when to conduct an attributional or a consequential LCA, both for LCA practitioners and users of LCA results. In this paper, the influence of the goal and scope on the selected modeling approaches is clarified. The distinction between process-oriented and product-oriented LCAs, between system expansion and substitution, and between the cut-off approach and other allocation procedures are highlighted. Archetypes of goal and scope definitions are developed. These archetypes reflect the minimum amount of information required to select an allocation procedure. It is demonstrated via an illustrative example that the question “what is the environmental impact of a product” can result in at least 15 different research questions requiring at least five different modeling methods. Finally, perspectives are provided on the use of attributional and consequential approaches to evaluate the environmental, social, and economic sustainability of products and processes.