The influence of non-CO <sub>2</sub> forcings on cumulative carbon emissions budgets
Bibliographic record
Abstract
Carbon budgets provide a useful tool for policymakers to help meet the global climate targets, as they specify total allowable carbon emissions consistent with limiting warming to a given temperature threshold. Non-CO 2 forcings have a net warming effect in the Representative Concentration Pathways (RCP) scenarios, leading to reductions in remaining carbon budgets based on CO 2 forcing alone. Carbon budgets consistent with limiting warming to below 2.0 °C, with and without accounting for the effects of non-CO 2 forcings, were assessed in inconsistent ways by the Intergovernmental Panel on Climate Change (IPCC), making the effects of non-CO 2 forcings hard to identify. Here we use a consistent approach to compare 1.5 °C and 2.0 °C carbon budgets with and without accounting for the effects of non-CO 2 forcings, using CO 2 -only and RCP8.5 simulations. The median allowable carbon budgets for 1.5 °C and 2.0 °C warming are reduced by 257 PgC and 418 PgC, respectively, and the uncertainty ranges on the budgets are reduced by more than a factor of two when accounting for the net warming effects of non-CO 2 forcings. While our overall results are consistent with IPCC, we use a more robust methodology, and explain the narrower uncertainty ranges of carbon budgets when non-CO 2 forcings are included. We demonstrate that most of the reduction in carbon budgets is a result of the direct warming effect of the non-CO 2 forcings, with a secondary contribution from the influence of the non-CO 2 forcings on the carbon cycle. Such carbon budgets are expected to play an increasingly important role in climate change mitigation, thus understanding the influence of non-CO 2 forcings on these budgets and their uncertainties is critical.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".