Fire vs. fossil fuel: all CO <sub>2</sub> emissions are not created equal
Bibliographic record
Abstract
Abstract. Fire is arguably the most influential natural disturbance in terrestrial ecosystems, thereby playing a major role in carbon exchanges and affecting many climatic processes. Nevertheless, fire has not been the subject of dedicated studies in coupled climate–carbon models with interactive vegetation until very recently. Hence, previous studies resorted to results from simulations of fossil fuel emissions to estimate the effects of fire-induced CO2 emissions. While atmospheric CO2 molecules are all alike, fundamental differences in their origin suggest that the effects from fire emissions on the global carbon cycle and temperature are irreconcilable with the effects from fossil fuel emissions. The main purpose of this study is to illustrate the consequences from these fundamental differences between CO2 emissions from fossil fuels and non-deforestation fires (i.e., following which the natural vegetation can recover) using 1000-year simulations of a coupled climate–carbon model with interactive vegetation. We assessed emissions from both pulse and stable fire regime changes, considering both the gross (carbon released from combustion) and net (fire-caused change in land carbon, also accounting for vegetation decomposition and regrowth, as well as climate–carbon feedbacks) fire CO2 emissions. In all cases, we found substantial differences from equivalent amounts of emissions produced by fossil fuel combustion. These findings suggest that side-by-side comparisons of non-deforestation fire and fossil fuel CO2 emissions – implicitly implying that they have similar effects – should therefore be avoided, particularly when these comparisons involve gross fire emissions. Our results also support the notion that most net emissions occur relatively soon after fire regime shifts and then progressively approach zero, whereas gross emissions stabilize around a new value that is a poor indicator of the cumulative net emissions caused by the fire regime shift. Overall, our study calls for the explicit representation of fire in climate models, rather than resorting to ersatz results coming from fossil fuel simulations, as a valuable step to foster a more accurate understanding of its impacts in the Earth system.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".