Reviewing initiatives to promote sustainable supply chains: The case of forest-risk commodities
Bibliographic record
Abstract
The continued growth in demand for a relatively small number of agricultural and forest commodities in global trade has placed increasing pressures on forests across landscapes in the tropics and sub-tropics. These pressures are amplified by growing domestic demand in producing countries. Such trends have led to multiple environmental challenges linked to loss of forests and biodiversity and rising carbon emissions. They also create social challenges including threats to local food security, tenure rights and the livelihoods of indigenous peoples and local communities. Expansion of trade in forest-risk commodities over the past three decades has resulted in increased pressure from civil society organizations, consumers, international banks and shareholders of consumer goods companies to develop and implement a diverse array of instruments and tools to promote sustainable or deforestation-free sourcing, and as a way to reduce exposure to reputational, financial and regulatory risks. Multi-stakeholder platforms and commodity roundtables also emerged, in response to criticisms of government failures. The multiplication of sustainability initiatives has been driven by the growing complexity and diversity of conditions under which agri-food and timber supply chains operate. Private sector actors have increasingly defined and monitored their own sustainability performance by using certification standards or by developing their own procedures and criteria. More recently, a discernible shift toward landscape or jurisdictional approaches is seen as a way to meet sustainability goals. The growing complexity of policy regimes, inevitably, results in ambiguities and can lead to tradeoffs between gains and losses. This review presents a synthesis of the multiple public, private and hybrid governance initiatives that aim to promote sustainable supplies of key forest-risk commodities. It aims to make it easier to understand a vast and rapidly expanding literature. By drawing on the published literature and scientific discussions, including the recent FTA 2020 Science Conference, the review highlights some of the outstanding challenges that urgently need to be addressed in order to achieve the targeted impacts.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".