Challenges and Institutional Barriers to Forest and Landscape Restoration in the Chittagong Hill Tracts of Bangladesh
Bibliographic record
Abstract
Preventing, halting, and reversing ecosystem degradation is now a global priority, partly due to the declaration of the United Nations (UN) Decade on Ecosystem Restoration by the UN General Assembly 2021–2030 on 1 March 2019. Apart from the most recent global target to protect 30% of the natural planet by 2030 as part of the Kunming-Montreal Global Biodiversity Framework agreed during COP15, there are several other global goals and targets. The Government of Bangladesh (GoB) has also pledged to restore 0.75 million hectares of forests as part of the Bonn Challenge. The Chittagong Hill Tracts (CHT) of Bangladesh contain almost one-third of the country’s state-owned forests and are home to 12 ethnic communities, whose livelihoods are dependent on forests. Although once rich in biodiversity, the majority of the forests in the region are highly degraded due to faulty management, complex institutional arrangements, and land disputes with locals. The CHT, therefore, represent the most promising region for ecosystem restoration through forest and landscape restoration (FLR). Here, using the secondary literature, we examine the current institutional arrangements and drivers of deforestation and forest degradation in the CHT region and potential benefits and modalities to make FLR successful in the region. Based on our study, we suggest that institutional reform is essential for successful FLR in the CHT. We also discuss key interventions that are necessary to halt ecosystem degradation and to secure community participation in natural resources management in the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".