Sustainable life cycle management of batteries in a closed-loop supply chain under hierarchical cost-sharing contracts and carbon policies
Bibliographic record
Abstract
Concerns about the environment have led to an increase in the number of electric vehicles, which has heightened the dependency on batteries. However, this dependence has also brought about issues such as environmental pollution and resource depletion. Therefore, addressing the battery life cycle and recycling is crucial. As electric vehicle battery capabilities gradually decrease over time, they can be repurposed for second-life usage in applications, such as energy-sharing systems, or recycled when their capacity is low. In line with sustainability goals, carbon emissions from battery production, remanufacturing, and recycling must also be considered. This research examines strategic decisions related to pricing, battery quality, and carbon emissions in a closed-loop supply chain (CLSC) for batteries. Notably, a holistic supply chain perspective is adopted to optimize both sustainability and economic performance across manufacturers, remanufacturers, and retailers within the CLSC. The study compares different scenarios, including carbon tax policies and carbon trading markets, while focusing on optimizing three dimensions of sustainability, namely economic, social, and environmental aspects. Since battery manufacturers are responsible for the life cycle of their products, this study introduces a hierarchical cost-sharing contract to establish a holding company for life cycle management and to improve coordination among supply chain entities. Thus, the models are analyzed in centralized, Stackelberg game decision-making structures, and a newly introduced contract is developed to improve coordination among supply chain members. Moreover, this study introduces a novel integration of a game theory model with a data-driven framework to address uncertainties in input parameters. The results indicate that the carbon trading market can be more profitable for supply chain members than the carbon tax policy, with the new contract further enhancing profitability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".