Towards environmentally just supply chains: from harm reduction to transformative sustainability actions
Bibliographic record
Abstract
Purpose In response to the worsening environmental crisis, there have been multiple calls for sustainable supply chain management (SSCM) scholars and practitioners to adopt a “business-not-as-usual” approach based on justice, fairness, equity and sustainability. We add to this literature by proposing environmental justice (EJ) as a key concept for the theory and practice of SSCM. Design/methodology/approach This conceptual article builds SSCM theory on EJ and contributes to supply chain justice research and practice by introducing the concept of the “environmentally just supply chain” and presenting pathways for operationalizing it in practice. Findings Three pathways are proposed to leverage transformative SSCM to create environmentally just supply chains: human rights due diligence, resilience thinking and coproduction of environmentally just supply chains. Practical implications The three pathways can be used by actors within a supply chain to create environmentally just supply chains. Originality/value This article extends transformative, non-instrumental perspectives on environmental sustainability within SSCM scholarship to provide insights into how supply chains can be transformed through EJ. Not only does the article show the relevance of EJ for SSCM theory and practice, but it elaborates pathways for moving from harm reduction to transformative sustainability actions.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".