Drivers and impact of deforestation in the natural tropical rainforests of Uganda
Bibliographic record
Abstract
Uganda is endowed with numerous tropical hardwoods, diverse animal species, abundant aquatic life, and a rich variety of bird species. Uganda experienced a significant decline in its virgin forests. Given the worrying level of deforestation, it is timely to assess some of its driving factors and impacts. As the world strives to achieve zero deforestation by 2030, it is crucial to understand the factors driving deforestation and forest degradation in Uganda. The primary objective of this study was to investigate the drivers and impacts of deforestation, including energy emissions, agriculture, and roundwood production. We utilised data from the Food and Agriculture Organisation of the United Nations covering the years 2004–2016. The study employed both multiple linear regression (MLR) and dynamic linear model (DLR) to study the variables influencing deforestation and forest degradation. The results indicated that both agriculture and energy emissions had a positive and highly significant effect on forest conversion. Forest production (roundwood) had a very highly significant negative impact on forest conversion. The study recommends policies that should help Uganda improve its agriculture to be efficient and optimal, and mitigate the large-scale destruction of virgin forests for cultivation and livestock. Furthermore, the government of Uganda should implement strict legislation on the use of roundwood, wood fuels, charcoal, and firewood to significantly reduce its heavy reliance on forests. Ceteris paribus, financial and fiscal policies could bridge the energy gap and improve agriculture, thereby achieving Sustainable Development Goals related to deforestation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".