Comparison of Passenger Vehicle Fuel Economy and Greenhouse Gas Emission Standards Around the World
Bibliographic record
Abstract
Nine major regions around the world have implemented or proposed various fuel economy and greenhouse gas (GHG) emission standards. Yet these standards are not easily comparable, due to differences in policy approaches, test drive cycles, and units of measurement. This paper develops a methodology to compare these programs to better understand their relative stringency. Key findings from the report include: (1) The European Union (EU) and Japan have the most stringent standards in the world. (2) The fuel economy and greenhouse gas emission performance of the U.S. cars and light trucks—both historically and projected based on current policies—lags behind most other nations. The United States and Canada have the lowest standards in terms of fleet-average fuel economy rating, and they have the highest greenhouse gas emission rates based on the EU testing procedure. (3) The new Chinese standards are more stringent than those in Australia, Canada, California, and the United States, but they are less stringent than those in the European Union and Japan. (4) If the California GHG standards go into effect, they would narrow the gap between U.S. and EU standards, but the California standards would still be less stringent than the EU standards.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".