New Energy Vehicles under the Volatility of International Energy Prices: Evidence from Regional Conflict
Bibliographic record
Abstract
Russia and Ukraine engaged in a military battle on February 24, 2022. Russia's military actions are viewed as an act of aggression toward Ukraine by the United States, the United Kingdom, the European Union, and other nations. The global energy crisis brought on by the onset of conflict and international sanctions is unavoidably having an influence on the new energy vehicle industry because Russia is the most significant producer and exporter of oil in the world. This article will use the counterfactual framework built by ARIMA model to study and analyze the impact of the Russian-Ukrainian conflict on the new energy vehicle industry. In conclusion, in short term, the cost effect dominates, and in long term, substitution effect dominates. This paper innovatively uses an index to examine the relationship between the Russian-Ukrainian conflict and the new energy vehicle industry. And it provides reference advice for government policy-making and investor portfolio selection. Government should vigorously develop the new energy industry, while investors should consider increasing the proportion of investment in new energy vehicles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".