EV Energy Convergence Plan for Reshaping the European Automobile Industry According to the Green Deal Policy
Bibliographic record
Abstract
The paper dealt with the fact that the green deal took place when the demand for electrical energy surged. However, the procurement of electric vehicles and much of the electric energy of the future still depends on fossil fuels. Accordingly, the importance of the IT industry is highlighted, and the demand for hydrogen-electric vehicles and related industries increases. The method of this study investigated the relevance of EV charging as a future next-generation power source rather than the electric energy demand of the IT industry. This study derives the correlation between industrial electricity and household energy PPP according to economic growth through empirical regression analysis. As the result, it was found that the amount of change, including electric and next-generation electric vehicles, was significant for on thirds of the countries in the change in purchasing power compared to GDP. This affects overall purchasing power as twelve out of thirty two countries with EV demand (Italy, Canada, Switzerland, Poland, Slovenia, Germany, Slovakia, Finland, Sweden, Czech Republic, Estonia, Denmark) are more sensitive to electric energy. This is related to the charging of EVs or hydrogen as the next-generation power of the future rather than the electric energy demand of the IT industry. By preventing waste of unused electricity of IT-electric energy sources and charging-preserving hydrogen electricity, it seems indispensable to prepare for the national IT power conservation buffer facility for supply and demand in future growth.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".