The effectiveness of agricultural carbon dioxide removal using the University of Victoria Earth System Climate Model
Bibliographic record
Abstract
A growing body of evidence suggests that to achieve the temperature goals of the Paris Agreement, carbon dioxide removal (CDR) will likely be required in addition to massive carbon dioxide (CO 2 ) emissions reductions. Nature-based CDR, which includes a range of strategies to enhance carbon storage in natural and managed land reservoirs, such as agricultural lands, could play an important role in efforts to limit climate warming to well below 2 °C above preindustrial levels. However, there remains a substantial knowledge gap on how the climate will respond to CDR when the removed carbon remains in the active carbon cycle. This study uses an intermediate-complexity climate model to perform simulations of agricultural CDR via soil carbon sequestration at rates reflecting realistic costs under three future emissions scenarios. We found that plausible levels of agricultural CDR reduced CO 2 concentration by 5–19 ppm and global surface air temperature by 0.02–0.10 °C by the end of the century. This temperature decrease was non-linear with respect to cumulative removals, as the removed carbon remained part of the active carbon cycle, lessening the climate benefit than if it was removed permanently. In low-emissions scenarios, a given amount of CDR was found to be more effective at reducing surface air temperature and less effective at reducing atmospheric CO 2 , compared to high-emissions scenarios. This was due to a proportionally larger impact of CDR on radiative balance at lower atmospheric CO 2 and reduced weakening of the carbon sinks at lower atmospheric CO 2 . CDR was substantially more effective when implemented at a higher rate, as CDR results in a proportionally larger difference in a climate with lower cumulative air fraction of CO 2 . Land and soil carbon responses were driven by the scenario-dependent balances between the impacts of CDR on primary productivity from CO 2 fertilization and the impacts on soil respiration from increased soil carbon availability and global temperatures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".