A Well-to-Wheel Analysis of Greenhouse Gas Emissions from Passenger Vehicles in Thailand: Strategies for Enhancing Sustainable Transportation
Bibliographic record
Abstract
This paper aims to analyze greenhouse gas (GHG) emissions from passenger vehicles with a particular focus on comparing EVs and internal combustion engine vehicles (ICEVs) using various ratios of ethanol and gasoline blends in Thailand.For this purpose, four passenger vehicle models of both EVs and ICEVs are selected based on their power output and popularity in Thailand, and their GHG emissions are analyzed by employing a well-to-wheel (WTW) analysis.The results indicate that EVs achieve over a 60% reduction in GHG emissions compared to ICEVs running on gasoline.Interestingly, blending ethanol with gasoline has resulted in more than a 50% decrease in GHG emissions compared to using pure gasoline.Despite the considerable benefits of EVs, the transition from ICEVs to EVs could potentially impact the biofuel and agricultural sectors, directly influencing the Thai economy and society due to its agricultural-based economy.To achieve a balanced transitional pathway from ICEVs to EVs, this paper suggests adopting several strategies, for example, continuing to blend biofuels with petroleum products until the full adoption of EVs, shifting biofuels from transportation to electricity generation, and advancing technology to develop new biofuel products.These strategies could provide valuable insights for future research on GHG emissions mitigation and offer a basis for evaluating the effectiveness of decarbonization policies in Thailand.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".