From Scalable Biodiversity Measurement to Credible Biodiversity Metrics

Résumé fourni par la source

Governments struggle to develop effective policies to counter the decline of species and ecosystems. An obstacle to command-and-control and incentive-based mechanisms is that biodiversity is costly to measure, creating an information asymmetry in which firms and governments are incentivised to withhold information on adverse impacts. Using a principal-agent model, we show that credible reporting requires biodiversity measurement to satisfy four criteria: low marginal cost, low dispersion, sufficient information content, and parsimony. Though not yet deployable, a route towards meeting these criteria is emerging: the integration of deep-learning species distribution models (DL-SDMs) with remote sensing, proximal sensing, novel community data, and citizen science, which we term Scalable Biodiversity Measurement. We assess DL-SDM readiness against the Mitigation Hierarchy (Avoidance, Minimisation, Remediation, Offsetting), which spans the full range of actions disclosed in sustainability reporting. The components for auditing avoidance, spatial minimisation, and conservation offsets now exist, pending investment in infrastructure and training data. Operational minimisation and remediation remain immature, requiring advances in causal attribution. We propose a roadmap to scale up credible biodiversity metrics: (1) investment in large, standardised training datasets; (2) a transparent political process to compress high-dimensional outputs into parsimonious metrics; and (3) deeper integration of biodiversity science with mechanism-design economics.

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