Global and Country Inventory of Road Passenger and Freight Transportation
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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