Selected Problems of the Automotive Industry—Material and Economic Risk
Bibliographic record
Abstract
This article is a synthetic, brief review of the literature, reports and references on the transformation of the automotive industry into zero-emission cars, in particular electric cars. It analyzes the technological and economic aspects of changes in the automotive industry regarding the transformation to zero-emission cars. Despite great de-emission parameters, the production of electric cars does not have a zero carbon footprint. The acquisition of critical elements, their production and the production of other components and materials needed for their construction have an environmental impact. The supply chains of materials for the construction of batteries for electric cars are characterized by significant risks related to, among others, a lack of diversification and limited flexibility. The dominant supplier of rare elements for batteries is China. The article analyzes the impact of prices on the demand for electric cars and compares them to internal combustion cars. Research shows that most electric cars are sold in China, the USA and Europe (about 95% of the supply). The costs of cars are of great importance, which, given the current reduction in the purchasing power of consumers, make the forecasts of the dynamic growth of electromobility very cautious, and even stagnation in the purchase of electric cars is expected in the second half of 2023.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".