Governance of zero deforestation cocoa in West Africa: New forms of public–private interaction
Bibliographic record
Abstract
Abstract In March 2017, twelve of the world's leading cocoa and chocolate companies made a collective commitment to end the deforestation associated with the global cocoa supply chain. This marks one of the latest forms of transnational business governance, whereby state actors share the regulation of environmental and social externalities with private authority. This paper responds to the call for more contextual research into the complex policy ecosystems in which zero deforestation commitments are implemented and how transnational private authority is interacting with, and possibly being reconfigured by, domestic governance and territory. Combining policy analysis, field work on cocoa farms, focus groups, and over 45 interviews, this paper provides empirical evidence to explain how business commitments to zero deforestation cocoa interact with domestic political processes to reduce deforestation in key cocoa‐producing countries. The focus is on three top cocoa producer countries in West Africa, where smallholder cocoa farming causes environmental degradation due to deforestation, and socioeconomic progress for smallholders is difficult to achieve. In these countries, the private sector's commitment to reduce deforestation is situated in government‐led programs to reduce emissions from deforestation and forest degradation. The findings show that a codependent relationship is evolving between corporate and state‐led efforts to reduce deforestation, where the success of zero deforestation cocoa relies on synergistic public–private interaction. Cocoa intensification without expansion, supply chain traceability, and jurisdictional commodity sourcing are the three main areas of future collaboration identified.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".