The 30 by 30 biodiversity commitment and financial disclosure: Metrics matter

Abstract

The recent adoption of the Kunming-Montreal Global Biodiversity Framework commits nearly 200 nations to protect 30% of their land by 2030 – a substantial increase from the current global average of c. 17%. Given current and increasing constraints in the economy, the easiest approach to reach compliance would be to protect the cheapest areas. But what would this mean for the protection of biodiversity, and how could financial disclosure support – or undermine – success? Here we explore biological and financial consequences of area protection selected under various metrics and methodologies. We find substantial differences in performance, with the cheapest solution always being the worst for biodiversity. Corporate disclosure provides a powerful mechanism for supporting conservation but is often dependent on simplistic and underperforming metrics. We show that conservation solutions optimized through artificial intelligence are likely to outperform commonly used biodiversity metrics. We call for increased collaboration between economists, biologists, and data scientists to support the urgent implementation of the Global Biodiversity Framework.

Keywords: Area-based conservation, Biodiversity metrics, Kunming-Montreal Global Biodiversity Framework, Nature-related Financial Disclosure, Mean Species Abundance, Reinforcement learning