The factors influencing on “made in Vietnam” electric cars demand
Bibliographic record
Abstract
This article examines the factors affecting the demand for “Made in Vietnam” electric cars. The research results 6 main factors affecting the demand for “Made in Vietnam” electric cars including (1) Prices of “Made in Vietnam” electric cars; (2) Consumer income; (3) Consumer taste; (4) Price of related goods (including substitutes and complementary goods); (5) Market expectations for “Made in Vietnam” electric cars; (6) Policy institutions and consumers’ psychology towards environmentally friendly products. On that basis, the research team used Eviews8 software to test the impact of price factors and related commodity prices on the quantity and demand of electric cars. The results also indicate that when the price of “Made in Vietnam” electric cars increases by 1%, the quantity demanded for “Made in Vietnam” electric cars decreases by 1.39%; When income increases to 1%, consumers will be willing to save 0.26% to buy an electric car. If the price of substitute goods increases by 1%, the demand for “Made in Vietnam” electric cars will increase by 1.91%, and if the price of complementary goods increases by 1%, the demand for “Made in Vietnam” electric cars will decrease by 3.12%. From the results obtained, the research team has some recommendations to stimulate demand for “Made in Vietnam” electric cars, a product with many advantages in the green fuel era.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".