Non-CO2 forcing changes will likely decrease the remaining carbon budget for 1.5 °C
Why this work is in the frame
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Bibliographic record
Abstract
Abstract One key contribution to the wide range of 1.5 °C carbon budgets among recent studies is the non-CO 2 climate forcing scenario uncertainty. Based on a partitioning of historical non-CO 2 forcing, we show that currently there is a net negative non-CO 2 forcing from fossil fuel combustion (FFC), and a net positive non-CO 2 climate forcing from land-use change (LUC) and agricultural activities. We perform a set of future simulations in which we prescribed a 1.5 °C temperature stabilisation trajectory, and diagnosed the resulting 1.5 °C carbon budgets. Using the historical partitioning, we then prescribed adjusted non-CO 2 forcing scenarios consistent with our model’s simulated decrease in FFC CO 2 emissions. We compared the diagnosed carbon budgets from these adjusted scenarios to those resulting from the default RCP scenario’s non-CO 2 forcing, and to a scenario in which proportionality between future CO 2 and non-CO 2 forcing is assumed. We find a wide range of carbon budget estimates across scenarios, with the largest budget emerging from the scenario with assumed proportionality of CO 2 and non-CO 2 forcing. Furthermore, our adjusted-RCP scenarios produce carbon budgets that are smaller than the corresponding default RCP scenarios. Our results suggest that ambitious mitigation scenarios will likely be characterised by an increasing contribution of non-CO 2 forcing, and that an assumption of continued proportionality between CO 2 and non-CO 2 forcing would lead to an overestimate of the remaining carbon budget. Maintaining such proportionality under ambitious fossil fuel mitigation would require mitigation of non-CO 2 emissions at a rate that is substantially faster than found in the standard RCP scenarios.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it