Transport energy use and greenhouse gases in urban passenger \ntransport systems: A study of 84 global cities
Bibliographic record
Abstract
The transport sector will be very hard hit by the “big rollover” in world oil production due to occur within the next 10 years. Urban transport in particular is almost entirely dependent upon oil, and will take many years to shift to other energy sources. Most cities will be particularly vulnerable during the transition to a post-petroleum world. Likewise, the growing focus on global warming and greenhouse issues places additional pressure on urban transport to reduce its CO2 output. This paper provides a review of transport, urban form, energy use and CO2 emissions patterns in an international sample of 84 cities in the USA, Australia, Canada, Western Europe, high income Asia, Eastern Europe, the Middle East, Africa, low income Asia, Latin America and China. This overview concentrates on factors such as urban density, transport infrastructure and car, public transport and non-motorised mode use, which help us to better understand the different levels of per capita passenger transport energy use and CO2 emissions in different cities. Patterns of energy consumption, modal energy efficiency and CO2 emissions in private and public transport in the different groups of cities are examined. Automobile cities such as those in the USA use extraordinary quantities of energy in urban transport. An average US urban dweller uses about 24 times more energy annually in private transport as a Chinese urban resident. Public transport energy use per capita represents a fraction of that used in private transport in all cities, with rail being the most energy-efficient mode. CO2 emissions from passenger transport follow a similar pattern. For example, Atlanta produces105 times more CO2 per capita than Ho Chi Minh City. Some policy recommendations are outlined to reduce urban passenger transport energy use and greenhouse gases and provide other positive outcomes in terms of sustainability and livability in cities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".