Toward Sustainable Business Models for Shared Reverse Logistics of Electric Vehicle Battery
Bibliographic record
Abstract
The rapid growth of the electric vehicle (EV) industry is driving an unprecedented demand for batteries, underscoring the importance of implementing reverse logistics practices. A shared reverse logistics (SRL), in which multiple entities collaborate to tackle the complexities of managing returned products and end-of-life processes, can play a vital role in ensuring the sustainability of EV reverse logistics. To ensure revenue generation, sustainability, and effective collaboration among stakeholders of an EV shared reverse logistics network, a well-defined business model is essential. However, reviewing litrature reveals that a sustainable business model for EV shared reverse logistics have not been adequately developed. This paper aims to develope a novel business model by emphasizing on the importance of the Triple Bottom Line approach for a SRL within the EV battery sector. The proposed business model offers several key benefits, including addressing the complex challenges of managing product returns in the EV industry, maximizing recaptured value, promoting sustainability, achieving cost-effectiveness, reducing waste, and increasing profitability through efficient management of returned goods and materials. By integrating collaborative efforts across the supply chain components, the model optimizes resources and reduces environmental footprint, addressing the growing concern for sustainable handling and recycling of spent EV batteries. To perform this study, firstly we conducted a literature review on existing closed-loop supply chain EV battery, shared reverse logistics, and business models. Next, we propose a business model for EV shared reverse logistics focusing on sustainability KPIs. The study delves into potential sustainability indicators relevant to the EV battery's reverse supply chain while also highlighting components that can be shared among stakeholders. Our findings emphasize the potential of SRL as an enabler for sustainable growth in the EV battery industry. This work not only emphasizes the significance of innovative sustainable practices but also charts a viable pathway for stakeholders to collectively address the challenges of the end-of-lifeEV battery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".