Global and Country Inventory of Road Passenger and Freight Transportation
Bibliographic record
Abstract
This paper presents a comprehensive and validated inventory of road transport emissions worldwide. The bottom-up calculation correlates within 2% and 10% with fuel sales data in Organisation for Economic Co-operation and Development (OECD) and non-OECD regions, respectively; this adds credibility to the results. The inventory covers eight exhaust compounds emitted by five vehicle categories and five fuel types each. For many non-OECD countries, road transport exhaust emissions have been calculated for the first time at this level of detail. Furthermore, this paper provides a conservative estimate of primary particulate matter emissions from diesel and gasoline vehicles. The Group of Seven countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) together with Brazil, China, India, Mexico, and Russia account for more than three-quarters of all considered exhaust emissions, followed by major countries in the Middle East and Southeast Asia. Action in these 15 countries could reduce emissions for the whole region significantly. Exhaust control and maintenance can focus on motorized two-wheelers, buses, and heavy-duty trucks. The inventory is particularly suited for comparisons across countries and regions. Data uncertainties in transport volumes and real-world emissions, notably of hydrocarbon and particulate matter, should be reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".