“Carbon Cowboys” could inflate REDD+ payments through positive measurement bias

The United Nations Framework Convention on Climate Change (UNFCCC) guidelines aim for a maximum 10% uncertainty in forest biomass inventories, after which penalties accrue. Identification of this magnitude of error requires recognition of discrepancies in carbon stock estimates between project proponents and by Measurement, Reporting, and Verification (MRV) auditors for REDD+ (Reduced Emissions from Deforestation and Forest Degradation, plus the role of conservation, forest management and enhancement of carbon stocks). Given that carbon stocks might be intentionally overestimated by profiteers who would thereby benefit financially, it is important to know how those estimates might most expeditiously be inflated by systematic or random positive biases in measurements of tree diameter, height and wood density. We explore the differences in magnitudes of forest biomass estimate inflation that result from a scenario in which positive bias is added to a random selection of 1–20% of all trees, and a systematic “Carbon Cowboy” scenario in which 1–20% is added to the measurements of the largest trees. As expected, biases under the random scenario must be both highly frequent (>20% of trees) and large (>10%) to breach the UNFCCC 10% uncertainty threshold. In contrast, for the Carbon Cowboy scenario, a measurement bias in tree diameter as small as 10% reaches the same limit if added to the largest 5% of trees. A 10% upward bias achieves the same result if applied to the diameter, height and wood density of the largest 1% of trees. These findings suggest that MRV auditors of REDD+ projects should be especially vigilant about systematic measurement biases that involve large trees.