Market diffusion of alternative fuels and powertrains in heavy-duty vehicles: A literature review
Bibliographic record
Abstract
With about 22%, the transport sector is one of the largest global emitters of the greenhouse gas CO2. Long-distance road freight transport accounts for a large and rising share within this sector. For this reason, in February 2019, the European Union agreed to introduce CO2 emission standards following Canada, China, Japan and the United States. One way to reduce CO2 emissions from long-distance road freight transport is to use alternative powertrains in trucks — especially heavy-duty vehicles (HDV) because of their high mileage, weight and fuel consumption. Multiple alternative fuels and powertrains (AFPs) have been proposed as potential options to lower CO2 emissions. However, the current research does not paint a clear picture of the path towards decarbonizing transport that uses AFPs in HDVs. The aim of this literature review is to understand the current state of research on the market diffusion of HDVs with alternative powertrains. We present a summary of market diffusion studies of AFPs in HDVs, including their methods, main findings and policy recommendations. We compare and synthesize the results of these studies to identify strengths and weaknesses in the field, and to propose further options to improve AFP HDV market diffusion modelling. All the studies expect AFPs on a small scale in their reference scenarios under current regulations. In climate protection scenarios, however, AFPs dominate the market, indicating their positive effect on CO2 reduction. There is a high degree of uncertainty regarding the emergence of a superior AFP technology for HDVs. The authors of this review recommend more research into policy measures, and that infrastructure development and energy supply should be included in order to obtain a holistic understanding of modelling AFP market diffusion for HDVs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".