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Record W4408431027 · doi:10.5194/egusphere-egu25-13389

Effect of land carbon accounting methods on the climate response to cumulative CO2 emissions

2025· preprint· en· W4408431027 on OpenAlexaff
H. Damon Matthews, Kirsten Zickfeld, Alexander MacIsaac, Mitchell Dickau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate Change and Environmental Impact
Canadian institutionsSimon Fraser UniversityConcordia University
Fundersnot available
KeywordsCumulative effectsGreenhouse gasCarbon accountingEnvironmental scienceClimate changeNatural resource economicsAccountingBusinessEconomicsGeologyEcology

Abstract

fetched live from OpenAlex

The proportionality between global temperature change and cumulative CO2 emissions underpins our understanding of how climate will respond to future emissions, and what level of emissions reductions will be needed to stabilize global temperatures. Typically, fossil fuel and land-use CO2 emissions are treated as equivalent drivers of this global temperature response, and emissions reductions from both sources are assumed to contribute similarly to mitigation targets. However, measuring land-use CO2 emissions in the real world is complicated by the difficulty in separating direct emissions (those caused by deforestation and other human land-use activities) from indirect carbon fluxes caused by CO2 fertilization and other land carbon responses to changing climate conditions. Consequently, an emission (or removal) of CO2 from land use activities as measured and reported in national emissions inventories is not equivalent to a land-use emission as defined in modelling studies that have been used to quantify the climate response to cumulative fossil fuel and land-use CO2 emissions. Here we assess the impact of these different land carbon accounting conventions on two key metrics of the climate response to cumulative CO2 emissions: the Transient Climate Response to cumulative CO2 Emissions (TCRE) and the Zero Emissions Commitment (ZEC). Using a spatially-explicit intermediate complexity Earth system model, we quantify these two metrics as a function of (1) fossil fuel CO2 emissions only; (2) fossil fuel + direct land-use CO2 emissions; and (3) fossil fuel + net land-use CO2 fluxes including indirect land carbon sinks. We show that both the magnitude and time-dependence of the TCRE and ZEC metrics is sensitive to the inclusion and definition of land carbon emissions. This finding underscores the need for improved clarity and care in the application of scientific findings to real-world mitigation efforts related to land carbon emissions and removals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.389
Teacher spread0.346 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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