Reducing Carbon Emissions Through EVs Preference: Evidence from Suburban East Coast Malaysia
Bibliographic record
Abstract
Electric vehicles (EVs) signify a shift from traditional fossil fuel-burning vehicles and were introduced to address consumers' dependence on fossil fuels, rising emission levels, and other environmental issues.This study empirically analyses the key determinants of consumer behavior regarding environmental air quality issues and vehicle characteristics, which significantly influence the preference for EVs.The current work was conducted in the suburbs of the East Coast of Malaysia, specifically in the Kuala Nerus district of Terengganu.Notably, 220 respondents selected their preferred vehicle type in February 2021.The survey data were elicited using an experiment card.Prices, charging time, and the level of CO2 emission reduction were found to be the primary catalysts for consumers' preference for EVs over conventional vehicles (petrol combustion) in Kuala Nerus, Terengganu, based on the multinomial logit model.Furthermore, gender status and increased level of income and education were identified as demographic factors with a high probability (positive sign and significant at 5%) for the respondents' EV adoption and choice in Kuala Nerus, Terengganu.Relevant authorities should implement appropriate measures to increase EV consumption and energy efficiency and reduce carbon emissions and fossil fuel energy dependence in the Malaysian automotive industry.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".