Reasons Why Only Kuwaiti Citizens Drive Electric Vehicles despite Being Only a Quarter of the Population
Bibliographic record
Abstract
The adoption of fully battery-powered electric vehicles (EVs) in Kuwait is currently at less than one percent of the population, and hence Kuwait has the lowest index rank among countries around the globe. Effectively, only Kuwaiti nationals have the opportunity to own EVs, as there are no fast direct-current charging stations in Kuwait, and Kuwaiti landlords (expats cannot own real estate) do not allow the installation of EV-charging home wall boxes at their rented properties. Given these facts, this paper only focuses on the preferences of Kuwaiti nationals, even though they only constitute 25 percent of the population of Kuwait. To accomplish the present objectives, a quantitative descriptive method (closed-ended questions) was used to collect data from a sample of 227 Kuwaiti nationals who were representative of the owners of half a million internal combustion engine (ICE) cars and that were categorized as early majority consumers. The findings of the present study indicate that over 50 percent of the respondents would prefer to buy an EV in the following three years when certain criteria were satisfied, including government-controlled pricing policies and recharging point availability, high-speed roads, and free EV-dedicated parking spaces. Furthermore, over 40 percent of respondents stated that they would contemplate purchasing an electric vehicle if the price of gasoline or diesel increased by 19 to 50 percent. The findings also indicated that more than 40 percent of respondents believed that EVs are fire- and crash-safe, and roughly 50 percent of the respondents would be willing to pay between 6 and 20% more for an EV because they believe that EVs are ecofriendlier vehicles and are significantly faster than conventional petrol vehicles. Additionally, respondents rewarded those vehicles with an excellent mark because of their ecological, economical, and technological attributes and benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".