Rewetting of Disused Drained Peatlands and Reduction of Greenhouse Gas Emissions
Bibliographic record
Abstract
Drained peatlands are a significant source of greenhouse gas emissions to the atmosphere. When abandoned, they become the most likely sites of peat fires. An effective way to reduce greenhouse gas emissions and prevent peatland fires in disused drained peatlands is through rewetting and wetland restoration. These can make significant contributions to the implementation of the Paris Climate Agreement within the Land Use, Land-Use Change and Forestry sector and, ultimately, to climate change mitigation. An approach for estimating greenhouse gas emission reductions following rewetting, applicable to national and regional accounting, as well as to specific rewetting projects, is presented. It includes a methodology for determining effectively rewetted areas that can be considered wetlands, the application of IPCC greenhouse gas emission factors to said sites, and an uncertainty assessment. Starting from 2020 the Russian Federation National Report of anthropogenic emissions by sources and removals by sinks of greenhouse gasses not controlled by the Montreal Protocol utilised this approach in its inclusion of rewetted peatlands. An assessment of greenhouse gas emission reductions is presented using the example of a 1500 ha section of a peatland within the Fire Hazardous Peatland Rewetting Programme in Moscow Oblast (2010–2013). CO2 emission reductions were cumulatively 33.4 thous. t by 2022 (taking into account nitrous oxide fluxes, dissolved organic carbon removal and increased CH4 emissions—20 thous. t CO2-eq.) and are projected to reach almost 113 (68) thous. t by 2050. Greenhouse gas emission reductions not yet included as well as possible ways of accounting for them in the future are also noted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".