Evidence map›Paper›PMID 38092814›Full record

ArticleNature communications2023

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 De Alban, Hao Tang and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. On the potential for strategic behavior in jurisdictional REDD.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Charting the future of high forest low deforestation jurisdictions.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Hoong Chen TeoDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore. hcteo@u.nus.edu.ORCID http://orcid.org/0000-0003-0127-978X
Nicole Hui Li TanDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.
Qiming ZhengDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0002-7393-6585
Annabel Jia Yi LimDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.
Rachakonda SreekarDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.
Xiao ChenCentre for Nature-based Climate Solutions, National University of Singapore, Singapore, Singapore.
Yuchuan ZhouDepartment of Geography, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0009-0001-1687-9342
Tasya Vadya SariraDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.
Jose Don T De AlbanDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore.ORCID http://orcid.org/0000-0002-1671-5786
Hao TangCentre for Nature-based Climate Solutions, National University of Singapore, Singapore, Singapore.
Daniel A FriessCentre for Nature-based Climate Solutions, National University of Singapore, Singapore, Singapore.
Lian Pin KohDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore. lianpinkoh@nus.edu.sg.ORCID http://orcid.org/0000-0001-8152-3871

Funding

Agency for Science, Technology and Research (A*STAR) LCER Phase 2 funding programme (U2303D4101)Hong Kong Polytechnic University (Hong Kong PolyU) P0044791Ministry of Education - Singapore (MOE) MOE-T2EP50122-0006National Research Foundation Singapore (National Research Foundation-Prime Minister's office, Republic of Singapore) NRF-RSS2019-007
6 · The paper itself

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.

Identifiers

PMID38092814
PMCPMC10719246

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.