Greenhouse Gas Emission Mitigation Pathways for Urban Passenger Land Transport under Ambitious Climate Targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban passenger land transport is an important source of greenhouse gas (GHG) emissions globally, but it is challenging to mitigate these emissions as this sector interacts with many other economic sectors. We develop the Climate change constrained Urban passenger Transport Integrated Life cycle assessment (CURTAIL) model to outline mitigation pathways of urban passenger land transport that are consistent with ambitious climate targets. CURTAIL uses the transport activity of exogenously defined modal shares to simulate the associated annual vehicle stocks, sales, and life cycle GHG emissions. It estimates GHG emission budgets that are consistent with global warming below 2 and 1.5 °C above preindustrial levels and seeks mitigation strategies to remain within the budgets. We apply it to a case study of Singapore, a city-state. Meeting a 1.5 °C target requires strong commitments in the transport and electricity sectors, such as reducing the motorized passenger activity, accelerating the deployment of public transit and of electrification, and decarbonizing the power generation system. Focusing on one mitigation technology or one mode of transport alone will not be sufficient to meet the target. Our novel model could be applied to any city to provide insights relevant to the design of urban climate change mitigation targets and policies.
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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 it