Simulation models for sustainable, resilient, and optimized global electric vehicles supply chain
Bibliographic record
Abstract
While the transition to electric vehicles (EVs) is essential for decarbonising the transportation system, the production and distribution of EVs entail substantial carbon costs. To ensure these emissions are accurately accounted for and effectively mitigated, this research introduces a probabilistic simulation model of the EV’s supply chain, addressing a critical gap in current EV life cycle analyses and providing the first comprehensive quantification of its environmental sustainability and resilience. This simulation model replicates global market dynamics and captures the complexity and uncertainty of the EV supply chain, enabling a thorough evaluation of its carbon footprint, sustainability, resilience, and what-if counterfactual scenarios for alternative market structures. The results reveal that average supply chain emissions range from 6.43 to 6.95 kg e-CO[Formula: see text] KWh[Formula: see text] across different battery technologies. Additionally, the mass flow analysis shows unbalanced dependencies at all supply phases, with one geographical region significantly dominating the supply chain structure, highlighting the current supply chain architecture’s low resilience and high vulnerability. In light of these findings, the study introduces an optimisation model for hub and resource allocation configuration, effectively reducing vulnerability levels and supply chain emissions by up to 80%.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".