Accounting for the climate benefit of temporary carbon storage in nature
Bibliographic record
Abstract
Abstract Nature-based climate solutions can contribute to climate mitigation, but the vulnerability of land carbon to disturbances means that efforts to slow or reverse land carbon loss could result in only temporary storage. The challenge of accounting for temporary storage is a key barrier to the implementation of nature-based climate mitigation strategies. Here we offer a solution to this challenge using tonne-year accounting, which integrates the amount of carbon over the time that it remains in storage. We show that tonne-years of carbon storage are proportional to degree-years of avoided warming, and that a physically based tonne-year accounting metric could effectively quantify and track the climate benefit of temporary carbon storage. If the world can sustain an increasing number of tonne-years alongside rapid fossil fuel CO 2 emissions reductions, then the resulting carbon storage (even if only temporary) would have considerable and lasting climate value by lowering the global temperature peak.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".