Potential reductions of CO2 emissions from the transition to electric vehicles: Thailand’s scenarios towards 2030
Bibliographic record
Abstract
The climate crisis has become a significant concern for many worldwide sectors. One of the primary causes of global warming comes from carbon dioxide (CO2) emissions, in which the transport sector is the major emitter, especially from road vehicles. Electric vehicles (EVs) have been seen as the best decarbonization choice to respond to the global warming issue by replacing fossil-fueled engines with electrically driven engines, especially electricity from renewable energy sources. Last year, Thailand’s government unveiled a roadmap with a view to achieving EV production of at least 30% of all auto production in the country by 2030 through the 30@30 policy plan. This study describes the potential reductions of CO2 emissions and their cost resulting from the transition to EVs in Thailand. The CO2 emissions of EVs caused by Thailand’s road vehicle fleet and battery production have been considered, and the forecasting scenarios of the proportion of EVs in 2030 have been studied. In the scenarios, the number of road cars in 2030 was estimated based on the registered vehicle statistics in Thailand. The data on seven-passenger cars powered by gasoline and electricity are used. A potential incentive from the government to encourage the use of EVs is also discussed based on analyzing the cost of carbon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".