Bibliographic record
Abstract
Data for 1995 and 2005 on forty-two cities in the USA, Canada, Australia, Europe and Asia suggest that car use as well as total motorised mobility have decoupled from real growth in metropolitan GDPs. The car vehicle kilometres travelled per unit of GDP in thirty-nine out of the forty-two cities studied has reduced by an average of 24%. In thirty-five or 83% of the cities, total motorised passenger kilometres travelled per unit of GDP was lower in 2005 than it was in 1995, by an average of 26%. Decoupling of urban mobility from GDP can occur in the context of still rising car use or total mobility. However, in twelve out of the forty-two cities the actual car use per capita also declined by an average of over 6%. Overall, it is found that the average increase in car use in these forty-two cities from 1995 to 2005 was 7% or less than one-third of the level in the 1980s. This decoupling of car use from GDP growth is thus part of the ‘peak car use’ phenomenon. New data showing an improvement in the relative speed of public transport systems compared to general road traffic over many decades, which is being led by a strong global trend towards urban rail, may help to explain these results. Further research is needed to see if Chinese and Indian cities, with their heavy investments in rail, can also start to show a decoupling of passenger transport from GDP. Overall, the results suggest a possible future where wealth can continue to be created globally whilst reducing the use of cars, oil and their damaging global impacts.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".