Fuelwood demand and supply in Rwanda and the role of agroforestry
Bibliographic record
Abstract
Fuelwood in Rwanda is assumed to come from forests and woodlands, thus contributing to large-scale deforestation. Available studies on fuelwood demand and supply support this assumption and indicate a continuously rising demand of fuelwood, notably from forest plantations. These assertions are insufficiently substantiated as existing forest stock may not be depleted by rapid increase in demand for food and energy resources resulting from population growth, but rather from the need for agricultural land. Evidence suggests that the demands for fuelwood, in addition to other sources of energy, is supplied from agroforestry systems which has not been quantified so far. This review analyses sources and use of fuelwood in Rwanda, indicating the importance of on-farms trees and woodlots in fuelwood supply. It is concluded that the effect of fuelwood consumption on land use is difficult to disentangle as many other factors including land clearing for agriculture, livestock farming, human settlements, illegal cutting of valuable timber species, the demand for charcoal in towns and past conflicts, contributed significantly to the high rate of deforestation in the country. If fuelwood demand is to be met on a sustainable basis, more fuelwood has to be produced on agricultural lands and in forest plantations through species site matching and proper management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".