Equity in unilateral value chain policies: A monitoring framework for the EUDR and beyond
Bibliographic record
Abstract
Unilateral value chain policies have recently emerged as a key strategy of international land use governance. They're part of a broader trend towards trade-based environmental policies, from corporate due diligence to sustainability certification and trade moratoria, that has been critiqued for reinforcing inequities in global trade. Such critique has been heightened by the current rise of unilateralism, whereby states impose environmental rules on imported commodities. Debates have ensued over the political legitimacy of unilateralism, the unequal distribution of its socio-economic impacts, and the need to safeguard local producers and communities. This paper informs these debates by developing and applying a framework for monitoring equity across scales and phases of the policy process. The framework is applied to the 2023 EU Deforestation-free Regulation (EUDR), which aims to stop EU imports of commodities linked to deforestation. We find that EUDR policy references equity as a desired outcome, but excludes affected actors from the design process. Drawing on the case of cocoa in Ghana, we identify diverse potential impacts on smallholder farmers and economies. Opportunities for the EUDR to improve equity include embedding non-EU stakeholders in international decision-making processes, enhanced and equitable partnerships with producing countries and major investments in farmer support. The paper concludes by providing an equity checklist and agenda for monitoring progress, adaptable to a wide range of unilateral and trade-based policies. • The rise of unilateral value chain policies raises new contestations over equity. • Equity risks are procedural, distributive and recognitional, and span the entire policy process. • Equity risks span global to local scales and are complex and context-specific. • Our Policy Equity Framework enables holistic and coordinated monitoring of policy impacts. • Such monitoring is critical to improve equity of both policy-making and implementation.
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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 | 0.062 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".