The water footprint of peat from tropical and boreal locations
Bibliographic record
Abstract
Peat, when used for energy or for horticulture, is a form of biomass that develops in mires or peatlands over a period of hundreds of years. Recent studies have shown the large water footprints of bioenergy and hydropower. The concept of the water footprint (WF) uses a life cycle approach by taking water use in production chains into account and in this way distinguishes between direct and indirect water use. This study adopts the WF life cycle approach for the assessment of indirect water use for peat from tropical and boreal climates. In this way, it includes the WF related to the formation of peat hundreds of years ago. The blue WFs are determined by evaporation on the one hand and peat growth rates on the other. The study shows that WFs of peat are comparable to WFs (m3 per GJ) of presently available bioenergy. Counterintuitively, the indirect blue WFs are smallest for peat from tropical locations in Indonesia, where evaporation rates are high, and largest for locations in the boreal areas with relatively small evaporation rates. For Indonesia, the blue WF was 8 m3 per kg dry mass; for boreal areas, blue WFs ranged between 11 (western Canada) and 15 m3 per kg dry mass. In the boreal areas, both evaporation and growth rates are smaller than in tropical areas, but peat growth is relatively smaller than evaporation rates resulting in relatively large blue WFs. The WF of total annual peat production lies between 122 and 231 Gm3 which has been established over hundreds of years. When compared to the annual blue WF of humanity of 1025 Gm3 per year, the contribution of the WF of peat is small.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".