Decarbonization potential of future sustainable propulsion—A review of road transportation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Modern automotive propulsion technologies must achieve the highest CO 2 reduction potential quickly to abide by the requirements of the Paris Climate Agreement. A collective utilization of renewable fuels, e‐fuels, hydrogen, and electrical energy will be able to meet different mobility and transport requirements in an optimal and CO 2 ‐neutral approach. The well‐to‐wheel greenhouse gas emissions of a propulsion system are determined by two factors, that is, the energy efficiency of the system and the carbon intensity of the energy source. Regardless of the CO 2 emission generated during the battery manufacturing and recycling process, the carbon intensity of the battery electric vehicles during operation is mainly decided by the carbon intensity of the electricity being consumed. The relatively low fleet ratios of battery electric and hydrogen‐powered vehicles and the massive remaining useful life of current internal combustion engine vehicle stock limit their impact on decarbonization in the near term. The expansion of charging infrastructure requires significant acceleration for the success of large‐scale and rapid electric vehicle adoption. For internal combustion engines, the focus is to further improve energy efficiency and the adoption of low‐to‐zero carbon renewable fuels. Hybrid and plug‐in hybrid vehicles are demonstrating the advantages of combining state‐of‐the‐art technologies to reduce both energy consumption and carbon emissions. In this review, the present status of propulsion systems is reviewed in detail, considering both the market penetration and well‐to‐wheel carbon emissions. The decarbonization potentials of various propulsion systems are then discussed.
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.
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 | 0.000 | 0.003 |
| 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 it