Financing public transport: case studies of international and Australian cities
Bibliographic record
Abstract
While the Gold Coast has been successful in negotiating $1 billion in joint funds to build its light rail system and to improve travel between its urban activity centres, the community of the Newcastle region is currently trying to prevent the closure of the existing rail service. The urban futures of these two major Australian cities could head in different directions because of their future transport framework in the city centres. One key explanation lies in their ability to source funding for improving the public transport systems. This study examines options used for funding the public transport sector in medium-size cities in Canada, Germany and France, where governance and the level of economic development have some similarities to Australia. Comparisons are then made with the situation here – Newcastle (NSW) and the Gold Coast (QLD). By exploring the differences in funding sources and their intergovernmental funding arrangements, this paper concludes with options for enhancing the funding capability of the public transport sector in regional Australian cities which can be further explored or developed. When considering upgrading public transport services to enhance accessibility, energy efficiency, and social equality – as well as impacts on land value and urban structure – it is essential to understand cities’ funding capability in the public transport sector. In fact, funding arrangements reflect the government’s approach to planning and policy-making, and influence the quality of services provided. Hence, comparative case studies on funding the public transport sectors in different cities contribute to further identifying institutional solutions to advanced public transport outcomes. This paper examines funding arrangements of the public transport sector in medium-size cities with populations between 300 thousand and 3 million in Canada, Germany and France, to compare with Australian cities such as Newcastle and the Gold Coast. By exploring the similarities and differences in funding mechanisms adopted within their public transport governance structure, this paper identifies possibilities for enhancing funding capability of the public transport sector in regional Australia.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".