1 Urban Planning and Transport Paradigm Shifts for Surviving the Post-Petroleum Age in Cities
Bibliographic record
Abstract
Cities vary enormously in the amount of energy they use in passenger transport, especially private passenger transport. In a study of 100 cities worldwide, Atlanta, Georgia residents each consume annually an average of almost 103,000 MJ in private passenger transport energy (about 2,970 litres of gasoline equivalent), while at the other end of the spectrum in Ho Chi Minh City, the figure is a mere 922 MJ or 26 litres. In the developed world, where fairer comparisons can be made, US cities consume on average 60,000 MJ per capita per annum for private passenger transport (1,730 litres) while Australian and Canadian cities average about 31,000 MJ (895 litres). High income Asian cities such as Tokyo, as well as Western European cities, which are wealthier on average than their North American and Australian counterparts, consume only between 9,500 MJ (274 litres) and 15,700 MJ (452 litres) per capita respectively. The large sample of developing cities in the study average only about 6,500 MJ (187 litres). Urban development in the auto-dependent cities of North America and Australia clearly requires abundant and secure quantities of relatively cheap oil, without which these cities would begin to unravel, whereas other high income cities are not nearly so dependent on this non-renewable resource. At the same time that the world approaches, or perhaps has already reached peak oil production (the “big rollover”) and begins to decline in its output of this resource, newly industrialising nations are dramatically
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".