Environmental Risk Trade-off for New Generation Vehicle Production: Malaysia Case
Bibliographic record
Abstract
New Generation Vehicle such as Hybrid Electric (HEV) and Battery Electric Vehicles (EV) have higher efficiency compared to conventional vehicles, and therefore releasing less carbon emissions. However, arguments arise whether this kind of New Generation Vehicle is truly clean compared to the existing system, especially in developing country such as Malaysia since current knowledge only focus on Greenhouse Gas (GHG) generation. This study aims on provide better understanding of the environmental consequences of the compact vehicle production activities based on 5 impact classifications which is GHG generation, Acidification, Eutrophication, Carcinogenic Effect, and Human Health measured in “Disability Adjusted Life-Year” (DALY) using Life Cycle Inventory (LCI) Analysis under local electricity mix in 2017 and 2030. A trade-off comparison then can be made to assess the current vehicle technologies with high potential of mass usage in Malaysia– Conventional Internal Combustion Engine Vehicle (CV), EV, and HEV vehicles with two types of batteries; Nickel Magnesium Hydride (HEV-NiMH), and Lithium Nickel-Magnesium-Cobalt (HEV-NMC). This study found that EV have slightly higher potential to cause a global warming (5,791kg of CO2 equivalent emission), follow by HEV-NiMH (4,814kg), HEV-NMC (4,596kg) and CV (4,166kg) embodied per vehicle. Cradle-to-gate of CV is better in term of GHG emission and Carcinogenic impact compared to all the studied subjects but in overall measurement, it is not the best solution for human health, measured in DALY. Conversely, HEV have high environmental impact on the same categories. DALY for 2017 EV production is at 0.0014, CV at 0.0019, HEV-NiMH at 0.0036 and HEV-NMC at 0.0022. The situation created a trade-off between having higher Acidification and Eutrophication from CV production against having higher GHG emission of its replacement EV production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".