Carbon emissions from South‐East Asian peatlands will increase despite emission‐reduction schemes
Bibliographic record
Abstract
Abstract Carbon emissions from drained peatlands converted to agriculture in South‐East Asia (i.e., Peninsular Malaysia, Sumatra and Borneo) are globally significant and increasing. Here, we map the growth of South‐East Asian peatland agriculture and estimate CO 2 emissions due to peat drainage in relation to official land‐use plans with a focus on the reducing emissions from deforestation and degradation ( REDD +)‐related Indonesian moratorium on granting new concession licences for industrial agriculture and logging. We find that, prior to 2010, 35% of South‐East Asian peatlands had been converted to agriculture, principally by smallholder farmers (15% of original peat extent) and industrial oil palm plantations (14%). These conversions resulted in 1.46–6.43 Gt CO 2 of emissions between 1990 and 2010. This legacy of historical clearances on deep‐peat areas will contribute 51% (4.43–11.45 Gt CO 2 ) of projected future peatland CO 2 emissions over the period 2010–2130. In Indonesia, which hosts most of the region's peatland and where concession maps are publicly available, 70% of peatland conversion to agriculture occurred outside of known concessions for industrial plantation development, with smallholders accounting for 60% and industrial oil palm accounting for 34%. Of the remaining Indonesian peat swamp forest ( PSF ), 45% is not protected, and its conversion would amount to CO 2 emissions equivalent to 0.7%–2.3% (5.14–14.93 Gt) of global fossil fuel and cement emissions released between 1990 and 2010. Of the peatland extent included in the moratorium, 48% was no longer forested, and of the PSF included, 40%–48% is likely to be affected by drainage impacts from agricultural areas and will emit CO 2 over time. We suggest that recent legislation and policy in Indonesia could provide a means of meaningful emission reductions if focused on revised land‐use planning, PSF conservation both inside and outside agricultural concessions, and the development of agricultural practices based on rehabilitating peatland hydrological function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".