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Record W4407858744 · doi:10.1080/17583004.2025.2468476

CO <sub>2</sub> emitted from peat use in horticulture supports a lower emission factor

2025· article· en· W4407858744 on OpenAlexafffundabout
Bidhya Sharma, Hongxing He, Nigel T. Roulet

Bibliographic record

VenueCarbon Management · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Sphagnum Peat Moss AssociationSchlumberger Foundation
KeywordsPeatEnvironmental scienceHorticultureGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Peat extracted for horticulture is used for growing food, ornamental plants and for soil augmentation. Peatlands are large carbon (C) stores, and the use of extracted peat in aerobic, off-site conditions have implications on the accounting of CO2 emissions. The IPCC (2006, 2013) emission factor for peat use assumes instant oxidation i.e., all extracted peat is mineralized to CO2 in the same year. This is reasonable for peat used for fuel, but horticultural peat takes several decades to decompose. Using historical and present peat extraction data in Canada we calculate a time-integrated emission based on a first-order decomposition model of peat since 1940; when horticultural peat extraction approximately started. Our data compilation shows 36 Mt of peat C has been removed from peatlands (1994–2022) for horticultural use with extraction increasing at the rate of 10.93Kt/year. We calculate approximately 11.9 Mt CO2-C (95% CI= 10.7-12.7) has been released into the atmosphere from the decomposition of the extracted peat between 1940–2022, an estimate that is 2.8 to 3.4 times lower than what the IPCC default would suggest. Our findings have implications for comparing the impacts of peat-based growing media to other alternatives and to C taxes that could apply to horticultural peat users.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.585

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2025
Admission routes3
Has abstractyes

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