Bibliographic record
Abstract
The purpose of this thesis is to advance understanding of oil vulnerability1 of transport in Asia Pacific cities. This is achieved by the development of a multiscalar methodology which leads to nine published/submitted papers. This thesis is motivated by the concern of uncertain oil prices and the uneven dependence of oil in transport due to different urban designs and available modes to transport users. To achieve this purpose, a literature review (Chapter 2) is conducted, revealing prior oil vulnerability studies can be distinguished as being inter-city, intra-urban and disaggregate (household or personal) based. A conceptual framework based on prevailing vulnerability components is developed, defining oil vulnerability with the tripartite components of exposure, sensitivity and adaptive capacity. This conceptual framework helps to interpret previous and current research methodologies and approaches. The methodological approach of this thesis is based on these conceptualisations, as outlined in Chapter 3. A broad international comparison study is first presented in Chapter 4. Eleven major cities across the Asia Pacific are analysed, showing that compact cities like Hong Kong and Singapore are the least vulnerable. Australasian cities are highly vulnerable due to high car use, yet this vulnerability is offset by the relatively high income and wealth of their populations. New ways to research oil vulnerability are introduced. Data Envelopment Analysis (DEA) method is first used to benchmark the level of fuel stress, which refers to the proportion of fuel expenditure to disposable income. This is a direct measure of oil vulnerability, instead of proxy measures that were largely used in previous efforts. Oil resilience is also measured by how fuel stress could be offset by public and active transport usage. Chapter 5 used this method to examine Australian major cities, and Chapter 6 takes the DEA method to an international level, which internal divisions between Australia and Taiwan are compared. The thesis then narrows down to areas within cities (intra-urban). Presented in Chapter 7 is an improved vulnerability mapping method covering South-East Queensland (SEQ). This involves the use of previously unused adaptive capacity measures. The resultant spatial analysis provides a more nuanced socio-spatial understanding of oil vulnerability across urban spaces. Chapter 8 takes this approach and uses it in what is likely to be the first international spatial mapping comparison of intraurban oil vulnerability - Hong Kong is compared with Brisbane. The results show stark contrast of oil vulnerability of Hong Kong and Brisbane, where in the latter, the car is the dominant mode in most parts of the city. A comparative study of urban transport policy follows in Chapter 9. This involves a discursive analysis of urban transport policy of both Hong Kong and Brisbane and revealed the different levels of interest in oil vulnerability and the associated policy responses. Lastly, an analysis of Gold Coast’s recent light rail commencement is provided in Chapter 10 demonstrating the effect on vehicle travel distance on households between 2009 and 2015. The thesis contributes to transport and urban policy research by presenting likely the first international comparison of multi-level oil vulnerability, both quantitatively and qualitatively, inter-city and intra-urban. Insights drawn from the results will help to shape the debates on preparing for post-petroleum cities and to reduce automobile dependence for a more sustainable future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".