Addressing Deforestation in Global Supply Chains: The Industry Approach
Notice bibliographique
Résumé
The winner of the International Statistic of the Decade is 8.4 million – the number of football pitches deforested from 2000 to 2019 in the Amazon rainforest. The Royal Statistical Society selected this statistic to give a powerful visual to one of the decade’s worst examples of environmental degradation. Global food supply chains are the major driver behind this deforestation. As globalization has dispersed the production of goods around the world, global supply chains increasingly displace the environmental and social impacts of consumption in rich and emerging economies to distant locations. Grown predominantly in (sub)tropical ecosystems and consumed in industrialized economies, cocoa/chocolate represents the inherent transnational challenges of many of today’s highly prized foods. Chocolate’s distinct geographies of production and consumption result in forest loss and persistent poverty in places far from the immediate purview of consumers. Despite growing public awareness and media attention, most consumers of conventional cocoa/chocolate products are unable to know the precise origins of their chocolate due to its complex supply chain involving multiple intermediaries. Outside of niche chocolate products that carry significantly higher price tags, the average chocolate consumer buying a Mars bar or Reeses peanut butter cup remains in the dark about the social and environmental impacts of their purchases. In 2017, the global cocoa/chocolate industry responded by committing themselves to “zero deforestation cocoa,” whereby they aim for full supply chain traceability to ultimately end deforestation and restore forest areas in cocoa origins. The problem that this research aims to address is that despite their continued proliferation, corporate zero deforestation supply chain initiatives have thus far had only modest success in reaching their stated aims (Lambin et al. 2018). As company pledges grow in number and magnitude, deforestation continues in many commodity production areas, especially in tropical forest areas (Curtis et al. 2018). Through a systematic review of company pledges. this research brings more understanding to what precisely the global cocoa industry is committing to, and how these pledged changes are meant to be rolled out in practice. This knowledge will improve accountability by bringing clarity to questions surrounding who is meant to do what and how along the bumpy road to zero deforestation cocoa. Further, this research will shed light on the lesser known actors in the cocoa supply chain: the intermediary cocoa traders often operating informally in cocoa origins though a case study in Côte d’Ivoire- the world’s number one cocoa exporter. As technological advancements in commodity traceability and forest monitoring reduce the perceived distance between cocoa producers and their downstream buyers, supply chain actors are forging new partnerships to reduce the climate footprint of chocolate. This research accompanies one of these innovative partnerships between cocoa farming and chocolate eating communities. References Curtis et al. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108-1111. Lambin, et al. (2018). The role of supply‐chain initiatives in reducing deforestation. Nature Climate Change, 1. https://doi.org/10.1038/s41558‐017‐0061‐1, 109–116.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,010 | 0,015 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».