Risk assessment in lithium-ion battery circular economy in sustainable supply chain in automotive industry using gray degree of possibility in game theory and MCDM
Bibliographic record
Abstract
The Circular Economy of the Automotive Industry’s Sustainable Supply Chain in the Case of Lithium-Ion Batteries is pioneering in environmental protection and ecological resource utilization. In addition to solving environmental problems, this method provides economic benefits by reducing the need for raw materials and lowering manufacturing costs. However, introducing a circular economy approach in the lithium-ion battery supply chain has numerous risks and challenges. This study addresses these challenges by crafting a framework that encapsulates the risks involved. It identifies the risks that evolving circular economy strategies might bring to the lithium-ion battery supply chain through an integrated Gray Delphi–DEMATEL–ANP method. Furthermore, this work introduces the Gray Degree of Possibility to unveil worst-case scenarios in risk analysis and extends it into zero-sum Game Theory. The study then formulates an improved zero-sum game model to determine optimal strategies for mitigating these risks. The numerical analysis reveals that, according to the proposed methodology, Environmental Pollution Risk emerges as the most critical, with a definite weight of 0.1525. This is followed by the Support Program Deficiency Risk at 0.1452 and the Improper Waste Management Risk at 0.1372.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".