Greenhouse Gas Emission Mitigation Pathways for Light-duty Vehicle Fleets under Ambitious Climate Targets
Bibliographic record
Abstract
Mitigating greenhouse gas (GHG) emissions from light-duty passenger vehicles (LDVs) will be necessary to maintain global warming below 2 °C, and ideally below 1.5 °C. In this dissertation, methods to estimate the life cycle GHG emission implications of LDV-focused mitigation strategies and to outline mitigation pathways under ambitious climate targets are developed at national and urban scales. First, a fleet-based life cycle model, the FLAME (Fleet Life cycle Assessment and Material-flow Estimation) is developed to examine the life cycle GHG emission implications of mitigation strategies, such as lightweighting the U.S. LDV fleet or deploying mid-level ethanol blends (15-30% ethanol by volume) in Canada's LDV fleet. The model combines the high technological resolution of life cycle assessment (LCA) with the temporal and dynamic perspectives of LDV fleet models. Recommendations are provided on the most effective timing of the mitigation strategies, and on their contributions to national GHG emission reduction pledges. Then, the FLAME model is augmented with a backcasting procedure to outline GHG emission mitigation pathways for LDV fleets to maintain global warming below 2 °C, and the electrification of the U.S. LDV fleet is used as a case study. The backcasting procedure relies on an innovative approach based on Integrated Assessment Models (IAMs) to quantify national and sectoral GHG emission budgets. Finally, the CURTAIL model (Climate change constrained URban passenger TrAnsport Integrated Life cycle assessment) is developed and applied in Singapore to integrate all passenger land transport modes at an urban level and to seek associated combinations of mitigation strategies that are consistent with maintaining global warming below 2 °C or 1.5 °C. The methods developed in this dissertation bridge gaps between the refined perspectives of LCA and the global perspectives of IAMs to support the development of national and urban policies for LDVs to respect ambitious climate targets. The findings suggest that there is no technological silver-bullet, there is an urgency to act, and all mitigation efforts should be pursued.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".