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Uncertainties in deforestation emission baseline methodologies and implications for carbon markets

Author

Listed:
  • Hoong Chen Teo

    (National University of Singapore
    National University of Singapore)

  • Nicole Hui Li Tan

    (National University of Singapore
    National University of Singapore)

  • Qiming Zheng

    (National University of Singapore
    National University of Singapore
    Hong Kong Polytechnic University, Hung Hom)

  • Annabel Jia Yi Lim

    (National University of Singapore
    National University of Singapore)

  • Rachakonda Sreekar

    (National University of Singapore
    National University of Singapore
    University of Queensland)

  • Xiao Chen

    (National University of Singapore
    National University of Singapore)

  • Yuchuan Zhou

    (National University of Singapore)

  • Tasya Vadya Sarira

    (National University of Singapore
    National University of Singapore
    Duke University)

  • Jose Don T. Alban

    (National University of Singapore
    National University of Singapore)

  • Hao Tang

    (National University of Singapore
    National University of Singapore)

  • Daniel A. Friess

    (National University of Singapore
    National University of Singapore
    Tulane University)

  • Lian Pin Koh

    (National University of Singapore
    National University of Singapore
    National University of Singapore
    National University of Singapore)

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.

Suggested Citation

  • 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 & Lian Pin, 2023. "Uncertainties in deforestation emission baseline methodologies and implications for carbon markets," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-44127-9
    DOI: 10.1038/s41467-023-44127-9
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    References listed on IDEAS

    as
    1. Irawan, Silvia & Widiastomo, Triyoga & Tacconi, Luca & Watts, John D. & Steni, Bernadinus, 2019. "Exploring the design of jurisdictional REDD+: The case of Central Kalimantan, Indonesia," Forest Policy and Economics, Elsevier, vol. 108(C), pages 1-1.
    2. Tim Laing & Luca Taschini & Charles Palmer & Johanna Wehkamp & Sabine Fuss & Wolf Heinrich Reuter, 2015. "Understanding the demand for REDD+ credits," GRI Working Papers 193, Grantham Research Institute on Climate Change and the Environment.
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