Addressing Deforestation in Global Supply Chains: The Industry Approach
Bibliographic record
Abstract
<p>The winner of the International Statistic of the Decade is <strong>8.4 million </strong>– 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.</p><p>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.</p><p>References</p><p>Curtis et al. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108-1111.</p><p>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.</p><p> </p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".