State-of-the-Art Impact Indicators for Supply Chain Management: Interpretation and Best Practices

Institutions increasingly demand robust biodiversity impact metrics in order to comprehensively assess their environmental footprints. Two of the leading tools for supply chain impact analysis are Life Cycle Assessment (LCA) and multi-regional input-output analysis (MRIO), which calculate a range of environmental impact indicators at each step in a product’s supply chain. Recent impact assessment model development, applied in LCA and MRIO, has produced a raft of new metrics for biodiversity loss, including mean species abundance (de Weert et al., 2025), functional diversity (Scherer et al., 2023), and improved species richness models (Verones et al., 2020), alongside additional early-stage metrics for ecosystem service impacts (GLAM, 2024). Implementing them in a way that provides meaningful outcomes for biodiversity, however, requires a nuanced understanding in what these metrics actually measure: for example, are these metrics different ways of calculating the same impacts, or are there fundamental differences in the information they communicate? Can we directly cross-compare different biodiversity metrics, or better yet, combine them into a single metric? And how well do they communicate impacts at different geographical scales? Economic valuation methods provide an additional layer of complexity to model results: can dollar values accurately convey ecological information? If so, are these methods robust enough for practitioners yet? Some attempts have been made to reconcile this wide range of impact assessment methods (Kuipers et al., 2025; Damiani et al., 2023), but model development is fast outpacing comprehensive discussions around these metrics. Here, we aim to answer some of these questions using a case study at both local and global scales. We focus specifically on global cotton supply chains, due to their high production-level impacts and globalized manufacturing processes (Zhang et al., 2023). In doing so, we demonstrate the strengths and weaknesses of each indicator, how their results can be interpreted, and identify best practices for future practitioners.