Rethinking sustainability in cocoa supply chain in light of SDG disclosure
Bibliographic record
Abstract
Purpose This paper aims to assess how cocoa supply chain companies disclose sustainable development goals (SDGs) information in their sustainability reports. This assessment highlights strategic aspects of sustainable supply chain management and reveals leveraging sustainability points in the cocoa industry. Design/methodology/approach The two-step qualitative approach relies on text-mining company reports and subsequent content analysis that identifies the topics disclosed and relates them to SDG targets. Findings This study distinguishes 18 SDG targets connected to cocoa traders and 30 SDG targets to chocolate manufacturers. The following topics represent the main nexuses of connections: decent labour promotion and gender equity (social), empowering local communities and supply chain monitoring (economic) and agroforestry and climate action (environmental). Practical implications By highlighting the interconnections between the SDGs targeted by companies in the cocoa supply chain, this paper sheds light on the strategic SDGs for this industry and their relationships, which can help to improve sustainability disclosure and transparency. One interesting input for companies is the improvement of climate crisis prevention, focusing on non-renewable sources minimisation, carbon footprint and clear indicators of ecologic materiality. Social implications This study contributes to policymakers to enhance governance and accountability of global supply chains that are submitted to different regulation regimes. Originality/value To the best of the authors’ knowledge, no previous study has framed the cocoa industry from a broader SDG perspective. The interconnections identified reveal the key goals of the cocoa supply chain and point to strategic sustainability choices for companies in an important global industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".