Temporary nature-based carbon removal can lower peak warming in a well-below 2 °C scenario
Bibliographic record
Abstract
Abstract Meeting the Paris Agreement’s climate objectives will require the world to achieve net-zero CO 2 emissions around or before mid-century. Nature-based climate solutions, which aim to preserve and enhance carbon storage in terrestrial or aquatic ecosystems, could be a potential contributor to net-zero emissions targets. However, there is a risk that successfully stored land carbon could be subsequently lost back to the atmosphere as a result of disturbances such as wildfire or deforestation. Here we quantify the climate effect of nature-based climate solutions in a scenario where land-based carbon storage is enhanced over the next several decades, and then returned to the atmosphere during the second half of this century. We show that temporary carbon sequestration has the potential to decrease the peak temperature increase, but only if implemented alongside an ambitious mitigation scenario where fossil fuel CO 2 emissions were also decreased to net-zero. We also show that non-CO 2 effects such as surface albedo decreases associated with reforestation could counter almost half of the climate effect of carbon sequestration. Our results suggest that there is climate benefit associated with temporary nature-based carbon storage, but only if implemented as a complement (and not an alternative) to ambitious fossil fuel CO 2 emissions reductions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".