Life-Cycle Analysis of GHG Emissions for CNG and Diesel Buses in Beijing
Bibliographic record
Abstract
Greenhouse gas and criteria air contaminants associated with motor vehicles are major items in the national inventories of these emissions. While exhaust after-treatment technology has achieved dramatic reductions in the emissions of criteria air contaminants (CO, HC, NOx, PM), alternative technologies for comparable GHG emission reductions are much more challenging. The use of renewable energy forms, or alternative fuels with lower carbon intensity are engineering responses that require careful assessment on a location specific basis before the emission reductions benefits can be accurately assessed. Life-cycle analysis tools such as NRCan's GHGenius model have been developed to serve as analytical frameworks and assist in such assessment. This paper starts by reviewing the issues involved in the life-cycle analysis of alternative transportation technologies, and the available life-cycle tools such as Life Cycle Emissions Model (LEM), GHGenius and Greenhouse Gases, Regulated Emissions and Energy Use in Transportation (GREET) model. The paper then examines the case of compressed natural gas (CNG) vs. diesel from the perspective of a hypothetical Clean Development mechanism (CDM) project for the CNG transit bus fleet in Beijing that was completed as part of the Canada-China Cooperation for Climate Change program.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".