Systematic Risks\nof the Global Lithium Supply Chain\nNetwork: From Static Topological Structures to Cascading Failure Dynamics
Bibliographic record
Abstract
Recent years have seen increasing concerns on supply chain risks of lithium, a critical material for achieving e-mobility transition and climate ambitions. These risks propagate both along the life cycle and across national boundaries in a multilayer network. However, most previous studies are either only based on static network measures or focused on individual layers, ignoring dynamic cascading risks and interconnected and interdependent relationships along life cycle stages and across economies. Here, we integrated trade-linked material flow and complex network analyses to investigate intricate interconnections, interdependencies, and systematic risks of the global lithium supply chain. Both static and dynamic measures of the global lithium supply chain network exhibit a “robust-yet-fragile” property: robust for random shocks yet fragile for targeted shocks and robust for small or local disruptions yet fragile for large or cascading failures. Portugal, Brazil, Singapore, Canada, Finland, Norway, South Africa, Israel, Hungary, and the United Arab Emirates are most likely to be affected by supply disruptions. A hypothetical USA-China trade decoupling will increase the severity and susceptibility of network-wide failures by around 5%. Our results call for global collaborations and collective efforts to balance efficiency and security and avoid a “zero-sum game” in securing the lithium supply chain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.024 | 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; both teacher heads agree on what is shown here.
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".