The effectiveness of net negative carbon dioxide emissions in reversing anthropogenic climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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