Green Commute: Reducing Carbon Footprint in Commuting Scenarios in Karaj
Bibliographic record
Abstract
This study addresses the substantial contribution of mobile sources in commuting to greenhouse gas (GHG) emissions in megacities and underscores strategies for carbon footprint emission reduction. Field interviews and questionnaires facilitate data collection, enabling the classification of the studied vehicle fleet based on various parameters. The scenarios aim to minimize GHGs from the commute, utilizing the International Vehicle Emission (IVE) model to establish a primary carbon footprint emission inventory for commuting in Karaj. The base scenario reveals that commuting in Karaj produces 1579423 grams of CO2e, with CO2, N2O, and CH4 emissions at 1389039 grams, 43.74 grams, and 6385.38 grams, respectively. Three carbon footprint reduction scenarios, involving removing diesel vehicles, adopting natural gas-fueled vehicles, and replacing Euro 4 and 5 with older vehicles, demonstrate that the removal of diesel vehicles (S1) and adopting natural gas-fueled vehicles, and replacing Euro 4 and 5 with older vehicles (S3) are the most effective strategies, achieving a 99% efficiency rate in reducing CO2e. This study highlights the substantial impact of curbing carbon emissions from GHGs. S1 and S3 show significant reductions in carbon footprint emissions, emphasizing the crucial role of strategic planning and greenhouse gas minimization in controlling emissions from commuting. These findings underscore the critical importance of reducing carbon footprints and commuting to effectively mitigate GHGs in congested urban areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".