Making money talk nicely: Biodiversity impact assessment for investors

Under the Global Biodiversity Framework’s Target 15, large and transnational companies are required to report nature-related financial risks—driving investors and firms to adopt new biodiversity impact assessment tools. Similar pressures stem from Article 29 of France’s Energy and Climate Law, the European Sustainability Reporting Standard ESRS E4, the Taskforce on Nature-related Financial Disclosures (TNFD), and the Science Based Targets Network (SBTN). Yet despite this regulatory momentum, no standardised method exists for calculating corporate biodiversity footprints. Current tools differ substantially in how they characterise company activities, model environmental pressures, and translate these pressures into ecological impacts. As a result, businesses and financial institutions face significant uncertainty when trying to obtain reliable, decision-ready biodiversity metrics.

This paper offers the most extensive quantitative comparison to date of major investor-focused biodiversity impact assessment tools, supported by a systematic review of their methodological underpinnings. Drawing on a comparative analysis of eight tools applied to 500 large publicly listed U.S. companies, we show that company-level biodiversity impact rankings exhibit only low to moderate correlation. Consequently, investors relying on any single tool could reach materially different conclusions about corporate biodiversity impacts, potentially resulting in markedly different portfolio allocations when biodiversity is considered in investment decisions. We suggest that these divergences stem from differences in value-chain boundaries, environmental pressure modelling (e.g., land use, water use, greenhouse gas emissions), and the impact characterisation methods employed to translate these pressures into ecological outcomes such as LC-Impact, ReCiPe, GLOBIO, or bespoke alternatives. However, we also highlight significant uncertainty regarding the specific drivers of these divergent assessments, owing to opaque, inconsistently documented, or—in some cases—entirely absent methodological detail. Notably, even tools built on similar datasets or conceptual frameworks frequently produce divergent biodiversity footprint estimates, underscoring both the epistemic fragility and the limited decision-readiness of current approaches.

We discuss key challenges and opportunities for improving corporate biodiversity footprinting, including the need for greater transparency in modelling assumptions, integration of spatially explicit operational and ecological data, and clearer communication of uncertainty.