The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change
Bibliographic record
Abstract
Artificial removal of CO 2 from the atmosphere (also referred to as negative emissions) has been proposed as a means to restore the climate system to a desirable state, should the impacts of climate change become 'dangerous'. Here we explore whether negative emissions are indeed effective in reversing climate change on human timescales, given the potentially counteracting effect of natural carbon sinks and the inertia of the climate system. We designed a range of CO 2 emission scenarios, which follow a gradual transition to a zero-carbon energy system and entail implementation of various amounts of net-negative emissions at technologically plausible rates. These scenarios are used to force an Earth System Model of intermediate complexity. Results suggest that while it is possible to revert to a desired level of warming (e.g. 2 °C above pre-industrial) after different levels of overshoot, thermosteric sea level rise is not reversible for at least several centuries, even under assumption of large amounts of negative CO 2 emissions. During the net-negative emission phase, artificial CO 2 removal is opposed by CO 2 outgassing from natural carbon sinks, with the efficiency of CO 2 removal—here defined as the drop in atmospheric CO 2 per unit negative emission—decreasing with the total amount of negative emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".