Extent and causes of forest cover changes in Vietnam’s provinces 1993–2013: a review and analysis of official data
Bibliographic record
Abstract
Within a region plagued by deforestation, Vietnam has experienced an exceptional turnaround from net forest loss to forest regrowth. This so-called forest transition, starting in the 1990s, resulted from major changes to environmental and economic policy. Investments in agricultural intensification, reforestation programs, and forestland privatization directly or indirectly promoted natural forest regeneration and the setting-up of plantation forests mainly stocked with exotic species. Forest cover changes, however, varied widely among regions due to specific socio-economic and environmental factors. We studied forest cover changes (including natural and planted forests) and associated drivers in Vietnam’s provinces from 1993–2013. An exhaustive literature review was combined with multivariate statistical analyses of official provincial data. Natural forest regrowth was highest in northern mountain provinces, especially in the period 1993–2003, whereas deforestation continued in the Central Highlands and Southeast Region. Forest plantations increased most in mid-elevation provinces. Statistical results largely confirmed case study-based literature, highlighting the importance of forestland allocation policies and agroforestry extension for promoting small-scale tree plantations and allowing natural forest regeneration in previously degraded areas. Results provide evidence for the abandonment of upland swidden agriculture during 1993–2003, and reveal that spatial competition between expanding natural forests, fixed crop fields, and tree plantations increased during 2003–2013. While we identified a literature gap regarding effects of forest management by para-statal forestry organizations, statistical results showed that natural forests increased in areas managed for protection/regeneration. Cover of other natural forests under the organizations’ management, however, tended to decrease or stagnate, especially more recently when the organizations increasingly turned to multi-purpose plantation forestry. Deforestation processes in the Central Highlands and Southeast Region were mainly driven by cash crop expansion (coffee, rubber) and associated immigration and population growth. Recent data trends indicated limits to further forest expansion, and logging within high-quality natural forests reportedly remained a widespread problem. New schemes for payments for forest environmental services should be strengthened to consolidate the gains from the forest transition, whilst improving forest quality (in terms of biodiversity and environmental services) and allowing local people to actively participate in forest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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 teacher head, 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".