Not as different as we think: Bridging accounting and environmental science for enhanced biodiversity reporting quality

As environmental risks escalate in complexity and urgency—particularly biodiversity loss and ecosystem degradation—they represent systemic threats with profound implications for financial stability, corporate resilience, and long-term economic performance. The European Central Bank underscores this concern, noting that 72% of euro area firms are highly dependent on ecosystem services and face severe disruption if these services deteriorate. These risks are amplified by the declining reliability of ecosystem functions and the growing unpredictability of ecological dynamics. Despite clear evidence of vulnerability, nature-related impacts remain poorly reflected in current corporate sustainability disclosures, constraining the ability of investors, regulators, and other stakeholders to evaluate exposure and respond effectively.

This paper addresses the urgent need for relevant, accurate, and decision-useful information by conducting a cross-disciplinary conceptual analysis that integrates insights from accounting, reporting, and biodiversity science. The central challenge lies in translating complex ecological realities into scientifically credible and practically applicable metrics within corporate reporting frameworks. We respond to this challenge by drawing an analytical parallel between fair value accounting (FVA) principles and biodiversity impact estimation, using IFRS 13’s hierarchical data levels as an entry point for comparison.

Our analysis demonstrates that fundamental trade-offs between relevance, reliability, and verifiability—well known in financial reporting—also characterize biodiversity disclosures. Similar to Level 3 FVA, biodiversity metrics that rely on generalized inputs, such as revenue-based footprinting, introduce significant uncertainty and risk of misinterpretation. These approaches compress intricate, location-specific biological realities into abstract indicators, undermining decision-making and potentially leading to ill-informed strategies.

To advance a more robust approach, we propose a conceptual framework that prioritizes Level 2 data—scientifically established environmental pressures such as land use, pollution, and resource exploitation identified by IPBES as direct drivers of biodiversity loss. Pressure-based disclosures, collected through transparent and replicable methods, offer stable, reliable inputs for science-based models. By focusing on these scientifically anchored measures, biodiversity reporting can evolve toward a practical, decision-useful model. This approach enables companies and investors to generate more precise risk assessments, strengthen the foundation for meaningful data generation, and support actionable strategies to mitigate systemic environmental risks.