Differences in land-based mitigation estimates reconciled by separating natural and land-use CO2 fluxes at the country level
Bibliographic record
Abstract
Anthropogenic and natural CO 2 fluxes on land constitute substantial CO 2 emissions and removals but are usually not well distinguished in national greenhouse gas reporting. Instead, countries frequently combine natural and indirect human-induced CO 2 fluxes on managed land in their reports, which diminishes their usefulness for designing policies consistent with climate mitigation targets. Here, we separate natural and land-use-related CO 2 fluxes from national reports in eight countries using global models to improve the assessment of attribution of terrestrial CO 2 fluxes to direct anthropogenic activities. In most investigated countries, the gap between model-based and report-based CO 2 flux estimates is reduced if natural and indirect human-induced CO 2 fluxes on managed land are considered. Further examinations show that remaining differences are linked to country-specific discrepancies between model-based and report-based estimates. Separating natural and land-use-related CO 2 fluxes at national scales supports a fair burden sharing of climate mitigation across countries and facilitates the assessment of land-based mitigation ambitions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".