Life Cycle Greenhouse Gas Emissions of Conventional and Alternative Heavy-duty Trucks: Literature Review and Harmonization
Bibliographic record
Abstract
Globally, heavy-duty trucks (HDTs) contribute an increasing share of greenhouse gas (GHG) emissions. Switching to alternative HDTs has the potential to mitigate HDT GHG emissions. Studies have evaluated the decarbonization potential of alternative HDTs through life cycle assessments (LCAs). However, these studies used varying study design parameters and assumptions and obtained inconsistent results, making it challenging to generalize findings. In this thesis, I conducted a literature review to examine the results and assumptions in 28 HDT LCAs and identify inconsistencies and best practices for future studies; moreover, I conducted HDT LCA harmonization to evaluate GHG impacts for alternative HDTs and identify sources of variations in HDT GHG emissions reported in the LCAs. An HDT LCA data inventory and a harmonization framework were developed, further, recommendations for future HDT LCAs and policies for HDT decarbonization were provided. The results are expected to facilitate the evaluation and reduction of HDT GHG emissions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".