Uncertainties in deforestation emission baseline methodologies and implications for carbon markets

Abstract

Carbon credits generated through jurisdictional-scale avoided deforestation projects require accurate estimates of deforestation emission baselines, but there are serious challenges to their robustness. We assessed the variability, accuracy, and uncertainty of baselining methods by applying sensitivity and variable importance analysis on a range of typically-used methods and parameters for 2,794 jurisdictions worldwide. The median jurisdiction’s deforestation emission baseline varied by 171% (90% range: 87%-440%) of its mean, with a median forecast error of 0.778 times (90% range: 0.548-3.56) the actual deforestation rate. Moreover, variable importance analysis emphasised the strong influence of the deforestation projection approach. For the median jurisdiction, 68.0% of possible methods (90% range: 61.1%-85.6%) exceeded 15% uncertainty. Tropical and polar biomes exhibited larger uncertainties in carbon estimations. The use of sensitivity analyses, multi-model, and multi-source ensemble approaches could reduce variabilities and biases. These findings provide a roadmap for improving baseline estimations to enhance carbon market integrity and trust.

An open-access database of nature-based carbon offset project boundaries

“…These credits have emerged as a key mechanism for directing public and private financial resources towards climate change mitigation 1 . The market for traded carbon credits is estimated to have grown from $295.7 million in 2018 to over $2 billion in 2021 9 .…”

“…NBS-derived carbon credits are most commonly issued based on a baseline emissions scenario representing what could have occurred in the absence of the projects proposed activities (e.g. avoiding planned/unplanned deforestation, afforestation) 9 . This system incentivizes project developers to maximize the number of credits issued by constructing pessimistic baselines.…”

“…This system incentivizes project developers to maximize the number of credits issued by constructing pessimistic baselines. Problematically, project developers are given considerable latitude in the choice of forecasting algorithms, which has substantial impact in the final number of credits issued 9,15,16 . Recently, NBS projects developed under programs and frameworks such as the Reducing Emissions from Deforestation and forest Degradation (REDD+), have been criticized and accused of issuing millions of credits with no genuine emission reductions 1,12,13 .…”