Zero-emission medium- and heavy-duty fleet operation: The perspectives of stakeholders
Bibliographic record
Abstract
The transition to zero-emission medium- and heavy-duty vehicles (ZEMHDVs) offers a valuable opportunity to reduce reliance on fossil fuels and combat climate change. Various countries are implementing policies, incentives, and targets to accelerate the adoption of ZEMHDVs in fleet operations. However, the penetration rate of these vehicles remains low. This study utilizes data from semi-structured interviews with 24 organizations and investigates stakeholders’ perspectives, experiences, and behaviors with respect to ZEMHDV adoption. The Latent Dirichlet Allocation (LDA) model is employed to extract topics that reflect stakeholders’ perspectives. Following that, a qualitative content analysis is conducted to identify barriers and enablers to ZEMHDV adoption. The findings revealed 12 main topics with 68 sub-themes (35 barriers and 33 enablers). Furthermore, five categories of proposed interventions are synthesized, including financial resources and incentives, technological advancement, tailored policies and regulations, planning and operational tools, and data sharing, education, and awareness.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".