Reference states for assessing biodiversity impacts in life cycle assessment: a decision framework

Life cycle assessment (LCA) is a well-established tool for assessing the environmental performance of products or services throughout their life cycle. However, when evaluating biodiversity impacts, holistic evaluations are still challenging. Shortcomings are e.g. the incomplete coverage of potential impact pathways as well as the predominant focus on the species level of biodiversity. Additionally, the reliability of LCA-results for biodiversity impact assessment is undermined by the inconsistency in modelling choices, particularly when selecting a reference state against which all potentially induced changes are compared to. Such reference states necessarily underly the majority of impact models in LCA and options range from historical baselines, natural counterfactuals or re-naturalization scenarios to limit and target references. The choice of a reference state can have a significant impact on LCA-results and, if not used consistently, compromise their significance and comparability as well as the compatibility of models. Proposals for suitable reference states and how to construct them have been given by several authors, mainly focusing on the modelling of land use related impacts. Nevertheless, there is no harmonised manner in applying them yet. Furthermore, LCA-developers often do not mention their modelling-choice explicitly, making it hard for practitioners to judge the applicability of a model and interpret its results.

The aim of this study is to unfold inconsistencies due to a heterogenous use of reference situations when assessing biodiversity impacts and thereby contribute to a higher reliability and comparability of LCA-results. Therefore, reference states that are applied in existent impact assessment models will be identified and categorized within a harmonized nomenclature. The relationship between the reference situations and the statements that can be derived from corresponding LCA-results will be assessed and the compatibility of models will be evaluated. The expected outcome of this research is a comprehensive overview of modelling options that can serve as a decision framework to support both LCA-developers and -practitioners in selecting the best suitable assessment model for their purpose.