Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology
Bibliographic record
Abstract
Abstract It is widely accepted that firms play an important stewardship role in addressing sustainable development concerns. A key challenge in this role is to balance the often conflicting pressures created by sustainable development—firm‐level economic performance versus environmental degradation and social disruption. Drawing on complexity theory, risk management, stakeholder theory and the innovation dynamics literature, we discuss the problems of integrating sustainable development concerns in the supply chain, specifically the applicability of life cycle assessment (LCA). Many authors have emphasized the importance of the “cradle to grave” approach of LCA in optimizing closed‐loop supply chains, improving product design and stewardship. Based on two case studies (an agricultural biotechnology and an oil and gas company) with supporting data collected from key stakeholders, we argue that sustainable development pressures have increased complexities and presented ambiguous challenges that many current environmental management techniques cannot adequately address. We provide a framework that addresses these deficiencies and discuss implications for practitioners and management theory.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".