Contribution of potential clean trucks in carbon peak pathway of road freight based on scenario analysis: A case study of China
Bibliographic record
Abstract
Reducing the carbon emissions from trucks is critical to achieving the carbon peak of road freight. Based on the prediction of truck population and well-to-wheel (WTW) emission analysis of traditional diesel trucks and potential clean trucks including natural gas, battery-electric, plug-in hybrid electric, and hydrogen fuel cell, the paper analyzed the total greenhouse gas (GHG) emissions of China's road freight under four scenarios, including baseline, policy facilitation (PF), technology breakthrough (TB), and PF-TB. The truck population from 2021 to 2035 is predicted based on regression analysis by selecting the data from 2002 to 2020 of the main variables, such as the GDP scale, road freight turnover, road freight volume, and the number of trucks. The study forecasts the truck population of different segments, such as mini-duty trucks (MiDT), light-duty trucks (LDT), medium-duty trucks (MDT), and heavy-duty trucks (HDT). Relevant WTW emissions data are collected and adopted based on the popular truck in China's market, PHEVs have better emission intensity, especially in the HDT field, which reduces by 51% compared with ICEVs. Results show that the scenario of TB and PF-TB can reach the carbon peak with 0.13% and 1.5% total GHG emissions reduction per year. In contrast, the baseline and PF scenario fail the carbon peak due to only focusing on the number of clean trucks while lacking the restrictions on the GHG emission factors of energy and ignoring the improvement of trucks' energy efficiency, and the total emissions increased by 29.76% and 16.69% respectively compared with 2020. As the insights, adopting clean trucks has an important but limited effect, which should coordinate with the transition to low carbon energy, and the melioration of clean trucks to reach the carbon peak of road freight in China.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".