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Record W4389001803 · doi:10.1038/s43247-023-01091-y

Improved estimates of carbon dioxide emissions from drained peatlands support a reduction in emission factor

2023· article· en· W4389001803 on OpenAlexafffundabout
Hongxing He, Nigel T. Roulet

Bibliographic record

VenueCommunications Earth & Environment · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeatEnvironmental scienceUnited Nations Framework Convention on Climate ChangeCarbon dioxideSeasonalityGreenhouse gasWetlandClimatologyCurrent (fluid)Climate changeAtmospheric sciencesEnvironmental protectionKyoto ProtocolEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract Under the United Nations Framework Convention on Climate Change, Annex 1 countries must report annual carbon dioxide (CO 2 ) emissions from peatlands drained for extraction. However, the Tier 1 emission factor (EF) provided in the IPCC 2014 Wetland Supplement is based mainly on warm season data from a limited number of sites. Here we evaluate the current IPCC EF and revise it with newly published data. The updated EF is 2.46 ± 0.25 t C ha −1 yr −1 , a 12% reduction and a threefold decrease in the confidence interval compared to the current IPCC (2014) EF. We generate a Tier 3 EF, 1.4 ± 0.25 t C ha −1 yr −1 for a typical extraction site in eastern Canada using numerical CoupModel that explicitly considers seasonality and interannual climatic variability, and suggest how to account for seasonality for the previously published EFs. This reduction has implications for comparing alternatives to peat-based growing substrates, the assessment of offsets, and possible punitive carbon taxes or cap-and-trade schemes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.253
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2023
Admission routes3
Has abstractyes

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