Is the Implementation of Cocoa Companies' Forest Policies on Track to Effectively and Equitably Address Deforestation in West Africa?
Bibliographic record
Abstract
ABSTRACT Tropical forests play a crucial role in achieving the sustainable development goals by contributing to climate stability, conserving biodiversity and sustaining livelihoods. However, forests are disappearing due to agricultural expansion. In West Africa, cocoa production is a major driver of deforestation. This study examines the design and implementation of forest‐focused supply chain policies (FSPs) in cocoa supply chains in Côte d'Ivoire and Ghana, the world's two leading cocoa producers. FSPs are voluntary policies of companies to combat deforestation, restore forests, and improve farmers' livelihoods. Drawing on 91 stakeholder interviews, we developed a conceptual framework to examine FSPs' theory of change, implementation and potential effectiveness and equity. Our findings reveal shortcomings in FSPs' design and implementation. FSPs are mostly narrowly focused on preventing illegal deforestation and only target farmers in companies' ‘direct’ supply chains, neglecting important landscape‐scale approaches and processes. Companies also fail to include smallholder farmers sufficiently in policy design and implementation. Lastly, FSPs prioritise productivity enhancement but overlook the importance of addressing farmers' social norms and values. We provide recommendations on how to address the shortcomings to achieve sustainable cocoa production.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".