Uncertainties in deforestation emission baseline methodologies and implications for carbon markets

Hoong Chen Teo (), Nicole Hui Li Tan, Qiming Zheng, Annabel Jia Yi Lim, Rachakonda Sreekar, Xiao Chen, Yuchuan Zhou, Tasya Vadya Sarira, Jose Don T. Alban, Hao Tang, Daniel A. Friess and Lian Pin Koh ()
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Hoong Chen Teo: National University of Singapore
Nicole Hui Li Tan: National University of Singapore
Qiming Zheng: National University of Singapore
Annabel Jia Yi Lim: National University of Singapore
Rachakonda Sreekar: National University of Singapore
Xiao Chen: National University of Singapore
Yuchuan Zhou: National University of Singapore
Tasya Vadya Sarira: National University of Singapore
Jose Don T. Alban: National University of Singapore
Hao Tang: National University of Singapore
Daniel A. Friess: National University of Singapore
Lian Pin Koh: National University of Singapore

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-44127-9

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