STRUCTURE OF THE VEHICLE MARKET IN UKRAINE AND THE EU – SAFETY AND ENVIRONMENTAL ASPECTS
Bibliographic record
Abstract
The article presents the main indicators of the functioning of the vehicle market of Ukraine, focusing mainly on the risks related to road traffic safety and the negative impact on the environment depending on the structure of this market, in particular in such aspects as the share of new vehicles, the share of vehicles equipped with lectrified power plants, age structure. A comparative analysis of these indicators of the Ukrainian market and the EU market is also provided. In particular shown that in 2013, more than half of the market of Ukraine consisted of new vehicles. But during 2019-2021 most of the vehicles imported into Ukraine are those that have been in use for more than 10 years. Since 2014, the segment of the used vehicles market has been the largest among other segments of the Ukrainian vehicle market. As for the vehicles equipped with electrified power plants the article shows that in some EU member states, in 2021, the share of electrified cars on the market of these states exceeded half. In Ukraine, on the other hand, this indicator was about 3,5% in the 1st quarter of 2021, which indicates that Ukraine is more than 10 times behind the EU in terms of the transition to alternative power plants. This indicator is the lowest among all EU member states. The main differences between the procedures related to the placing on the market of new vehicles and vehicles that were in use were analyzed. The article also examines factors, including legislative changes, that have affected the structure of the vehicle market. The dynamics of changes in the share of national products on the Ukrainian vehicle market, as well as the structure of the Ukrainian segment of the passenger car market that were in use, were analyzed. Recommendations are provided regarding regulatory measures aimed at reducing risks in terms of road safety and environmental protection related to the structure of the Ukrainian vehicle market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".