Tree plantations and forest regrowth are linked to poverty reduction in Africa
Bibliographic record
Abstract
Numerous countries have adopted large-scale tree planting programs as a climate mitigation strategy and to improve local livelihoods. However, it remains poorly documented how the surge in tree plantations has altered local livelihoods. Here, we assess whether tropical tree plantation expansion and forest regrowth across 18 African countries are associated with local people’s living standards. By combining a recent map that distinguishes tree plantations from regrowth from 2000 to 2012 with multidimensional poverty measures from more than 200,000 households, we find a positive association between people's living standards and areas where tree plantations have expanded or, to a lesser extent, forest regrowth has occurred. Because tree plantations make up a large proportion of recent increases in tropical tree cover – and controversy remains about their potential impacts on both biodiversity and local people – our study provides broad empirical support for the idea that tree plantations and forest regrowth can be linked with reduced poverty in the short term. The expansion of tree plantations and forest regrowth areas is associated with improvements in people’s living standards across 18 African countries, according to an analysis combining the map of tree plantations and regrowth with multidimensional poverty indicators from 200,000 households.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".