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The use of life cycle assessment (LCA) methods is rapidly expanding as a means of estimating the biodiversity impacts of organisations across complex value chains. However, these methods have limitations and substantial uncertainties, which are rarely communicated in the results of LCAs. Drawing upon the ecological and LCA literature on uncertainty and two worked examples of biodiversity footprinting, we outline where different types of uncertainty occur across multiple stages in LCA, from input data to the choice of biodiversity metric. Some uncertainties are epistemic, incorporating structural (e.g. the types of pressures included in models), parametric (e.g. uncertainty around characterisation factors and information gaps) and measurement uncertainty, as well as natural variability, and stochasticity. Other uncertainties are linguistic (e.g. ambiguity around definitions of biodiversity) and decision-based (e.g. choices made when matching company data to inventory categories). Based on this review, we provide suggestions for (i) understanding, reducing and navigating uncertainties in biodiversity footprinting and (ii) ensuring the robust and appropriate use of LCA techniques as part of broader organisational biodiversity strategies. Understanding the risks posed by these uncertainties, weighing them against the costs of inappropriate action or inaction and ensuring decisions are robust to these uncertainties is vital for designing effective biodiversity strategies. By appreciating and navigating uncertainties, opportunities exist to utilise LCAs for high-level risk screening to prioritise action and highlight areas where focused effort and more granular data are needed to track progress towards abating impacts year-on-year and identify low-risk actions. However, we recommend biodiversity strategies should not be based solely on absolute LCA impact results. Instead, LCAs should be used alongside other approaches to guide location-specific and robust action to deliver a nature-positive future.

Original publication

DOI

10.1111/2041-210X.70001

Type

Journal article

Journal

Methods in Ecology and Evolution

Publication Date

01/01/2025