Comparative life cycle assessment of prospective battery-grade material production in Norway
Bibliographic record
Abstract
The manufacturing of battery grade materials is electricity intensive and currently\ndominated by China. Several European countries are exploring options of\nventuring into locally produced raw materials for battery manufacturing. In\nNorway, four battery manufacturing facilities are planned or under construction\nwhich increases the need for locally manufactured battery grade materials. This\nstudy investigates the potential impacts of producing nickel, cobalt and manganese\nsulfate in Norway, a country known for its renewable-sourced electricity. A high\nresolution model is developed for each of the three battery grade materials which\nconsiders individual steps of the value chain from mining to the final product.\nThis is vital in the modelling as some processes, especially those pertaining to\nmining and ore processing occur outside of Norway. Environmental impacts of\nthese battery grade materials are performed with Arda, an in house LCA calculator\nusing ReCiPe2016 as midpoint characterization method.\n\nThe results show that, producing nickel and cobalt sulfate in Norway yields 3.3kg\nCO2eq. and 7.7kg CO2eq., respectively, with the highest contribution from ore\nprocessing which occurs in Canada. Manganese sulfate produced in Norway\nwith ores mined in Gabon causes a GWP of 1.3 kgCO2eq., mainly due to metal\nrefining impacts. The results are benchmarked with other studies performed\nacross different geographical system boundaries to depict the emission reduction\nopportunities in producing these battery grade materials in Norway. What is\nobserved is that, production of these sulfates in Norway has significant emission\nreduction benefits as compared to other studies reported in the scientific literature.\nTo increase the robustness of the analysis, the thesis further develops scenarios\nto investigate the effect of changes in the electricity mix intensity of different\nmining and production countries on the overall GWP. Within these scenarios, the\nNorwegian case still emerges with the lowest GWP. Results of this study indicate\nthat producing battery grade materials in Norway has prospects of reducing the\nemissions associated with cell materials in lithium-ion batteries. Furthermore,\nfrom the scenarios developed, the GWP of cathode precursors can be significantly\nreduced by using low carbon electricity in both mining and producing countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".