Supply risk evolution of raw materials for batteries and fossil fuels for selected OECD countries (2000–2018)
Bibliographic record
Abstract
Fossil fuels are the dominant form of storable energy, but their share in the global energy supply is slowly diminishing due to climate mitigation policies. Alternative energy production from variable renewable energy sources for both stationary and mobile use requires some form of energy storage. Batteries are the current frontrunner for this application, particularly with Li-ion batteries that are reliable and highly efficient. However, batteries themselves have evolved to meet current requirements and expectations. These changes in battery chemistry have shifted the dependency on raw materials used to produce them. Raw materials critical for battery production are subject to supply risk due to their availability or trade policies prompting a need for supply risk assessment. Such resource supply risks depend on the perspective of the importing country or region. By analysing the supply risk of raw materials used in the production of batteries in comparison to fossil fuels, it is possible to understand the shift in risk to storable energy that is underway. In this study, we analyse the supply risk of selected raw materials used in batteries and compare it with the supply risk of fossil fuels for the period 2000 to 2018 from the perspective of the European Union, USA, South Korea, Japan, Canada and Australia using the GeoPolRisk method. Our analysis demonstrates a higher risk of supply for raw materials compared to that of fossil fuels for all the selected territories. Rare earth elements, graphite and magnesium, are amongst the raw materials with the highest supply risk due to their concentrated production in one or only a few countries. Countries have recognised the need for raw material security and made specific policies to ensure secure supply. Raw material security is an emerging concern for all the countries, especially in the case of batteries for major manufacturing nations that are heavily import-dependent. Raw materials producing countries like Canada and Australia focused on stockpiling minerals and minerals exploration while importing countries such as Japan and South Korea are looking for alternate sources for their supply. The results from our analysis suggest that the necessary policy reforms taken for energy security have benefited all the countries with a reduced risk of fossil fuel supply, while similar policies to secure raw materials are discussed but not yet fully implemented.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".