Choosing the right approach to green your supply chains
Bibliographic record
Abstract
Purpose Due to the different institutional pressure such as those from market, regulations and competitors, companies have implemented green supply chain management (GSCM). Unfortunately, tens of GSCM practices exist. Whether all companies should implement GSCM and how to achieve both environmental and economic performance are still not clear for many companies. The purpose of this paper is to develop models that can be helpful for companies to identify right GSCM practices and implement GSCM effectively and efficiently. Design/methodology/approach Based on about 18 years of study on GSCM with four surveys in China in 2001, 2005, 2012 and 2016, as well as numerous site visits and interviews mainly in China but also in Japan, Germany and Canada, this paper explores institutional drivers as well as opportunities and challenges using theoretical analysis and case studies. GSCM is defined considering a product life cycle. A key three-step GSCM approach is theoretically developed considering opportunities and challenges through life cycle analysis (LCA) of a product and position of a company. Findings All companies should implement GSCM practices to avoid risks. To effectively implement GSCM practices, a company should understand the life cycle of its product and its position in the supply chain. A key three-step LCA-based approach can help companies to identify the critical GSCM practices. Originality/value A key three-step LCA-based approach for GSCM implementation is originally developed based on theoretical analysis and eight years of study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".