Contrasting climate and carbon-cycle consequences of fossil-fuel use versus deforestation disturbance
Bibliographic record
Abstract
Abstract Carbon dioxide emissions from deforestation disturbance (e.g. clear-cutting, forest fires) are in the same units as carbon dioxide emissions from fossil fuels. However, if the forest is allowed to regrow, there is a large difference between climate effects of that forest disturbance and climate effects of fossil CO2. In this study, using a set of idealized global climate-carbon model simulations with equal amounts of CO2 emissions, we show that on century to millennial timescales the response of the climate system to fossil-fuel burning versus deforestation disturbance are vastly different. We performed two 1000 year simulations where we add abrupt emissions of about 600 PgC to the preindustrial state as a consequence of either fossil fuel use or deforestation disturbance with vegetation regrowth. In the fossil fuel simulations, after 1000 years, about 20% of the initial atmospheric CO2 concentration perturbation remains in the atmosphere and the climate is about 1 °C warmer compared to preindustrial state. In contrast, in the case of deforestation with regrowth, after 1000 years, atmospheric CO2 concentration returns close to preindustrial values, because deforested land will typically recover its carbon over the decades and centuries in the absence of further human intervention. These results highlight the differences in the degree of long-term commitment associated with fossil-fuel versus deforestation 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.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".