Bibliographic record
Abstract
Abstract. Afforestation and reduction of fossil fuel emissions are two major components of climate mitigation policies. However, their effects on the earth’s climate are different because reduction of fossil fuel emissions directly alters the biogeochemical cycle of the climate system, while afforestation causes biophysical changes in addition to changes in the biogeochemical cycle. In this paper, we compare the climate and carbon cycle consequences of carbon removal by afforestation and an equivalent fossil fuel emission reduction using simulations from an intermediate complexity Earth system model. Our simulations show that the climate is cooler by 0.36 °C, 0.47 °C, and 0.42 °C in the long term (2471–2500) in the case of reduced fossil fuel emissions compared to the case with afforestation when the emissions follow the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. Though afforestation results in a lower atmospheric CO2, the cooling from the reduced atmospheric CO2 is partly offset by the warming from surface albedo decrease associated with the regrowth of forests. Since this warming effect from surface albedo decrease is nearly absent in the reduced fossil fuel emission case, the climate is relatively cooler, even though the atmospheric CO2 levels are similar to the afforestation case. Thus, in terms of climate benefits, reducing fossil fuel emissions is relatively more beneficial than afforestation for the same amount of carbon removed from the atmosphere. Nevertheless, fossil fuel emission reduction and afforestation efforts should be pursued simultaneously as both lead to a decrease in global mean warming and reduced ocean acidification.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.177 | 0.145 |
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".