Global “Climate Opportunity Benefit” of Forest Regeneration: Meta‐Analysis Shows Warming From Soil CH <sub>4</sub> and N <sub>2</sub> O Is Small Relative to Agriculture
Bibliographic record
Abstract
Abstract Global assessments of ecosystem regeneration as a climate mitigation strategy have traditionally focused on CO 2 , despite the acknowledgment that methane (CH 4 ) and nitrous oxide (N 2 O) are also important greenhouse gases (GHGs). We conducted a meta‐analysis of studies that measured soil CH 4 and N 2 O fluxes in unmanaged, regenerating forested and savanna ecosystems, with a focus on understanding biome‐specific differences in these GHG fluxes compared to a counterfactual of agricultural land use. We expected most regenerating ecosystems to act as small CH 4 sinks and relatively larger N 2 O sources, with a net warming combined CH 4 ‐N 2 O effect. Three of the five forested biomes we studied followed this pattern: subtropical/tropical forest, subtropical/tropical savanna and temperate conifer forest (0.60 ± 0.30, 0.15 ± 0.06, and 0.83 ± 0.24 Mg CO 2 e ha −1 yr −1 , respectively). Results suggest that even after 100 years of regeneration, the radiative cooling of the climate from CO 2 sequestration in aboveground biomass exceeds the radiative warming driven by the net CH 4 ‐N 2 O effect among all ecosystems on average globally. We also found that the “climate opportunity benefit” of ecosystem regeneration—the difference in the net CH 4 ‐N 2 O effects of agriculture versus regeneration—yields a net cooling effect for all biomes. However, because the CH 4 ‐N 2 O effect diminishes the cooling effect of ecosystem regeneration, our results underscore that it is unsound to use ecosystem regeneration as a justification for continuing fossil fuel emissions.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.032 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".