Uncertainties in deforestation emission baseline methodologies and implications for carbon markets

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 sensitiv...

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Bibliographic Details
Published in:Nature communications 2023-12, Vol.14 (1), p.8277-8277, Article 8277
Main Authors: Teo, Hoong Chen, Tan, Nicole Hui Li, Zheng, Qiming, Lim, Annabel Jia Yi, Sreekar, Rachakonda, Chen, Xiao, Zhou, Yuchuan, Sarira, Tasya Vadya, De Alban, Jose Don T., Tang, Hao, Friess, Daniel A., Koh, Lian Pin
Format: Article
Language:English
Subjects:
704/844/682
706/1145
Accuracy
Carbon
Carbon offsets
Deforestation
Emission analysis
Emissions trading
Humanities and Social Sciences
Jurisdiction
multidisciplinary
Science
Science (multidisciplinary)
Sensitivity analysis
Uncertainty
Variability
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Summary: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. This study reveals high variability in deforestation emission baselines typically used to derive carbon credits, with median error at 0.778 times the actual rate. It underscores the need for enhanced methods to improve carbon market accuracy and reliability.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-44127-9