Place-based neighbourhood planning approaches to net zero transport
Bibliographic record
Abstract
Integrating place-based neighbourhood planning approaches with transport sector strategies is essential for achieving net zero transport. However, systematic research on the role of different neighbourhood types in reducing transport emissions remains limited, constraining the ability of place-based planning practices to inform the development of integrated strategies. This gap partly stems from the lack of a theoretically informed neighbourhood classification framework applicable to practice. This study addresses the gap by proposing a neighbourhood classification framework and testing its validity in evaluating the influence of different neighbourhood types on climate change costs associated with transport emissions. Using the framework, 10,068 SA1 (statistical area level 1) regions within Greater Melbourne were classified into 14 distinct neighbourhood types. Climate change costs were estimated using data from the 2012–2020 Victorian Integrated Survey of Travel and Activity (VISTA). The results show that mobility-oriented development, transit-oriented development, 20-min neighbourhood, and activity centre neighbourhoods are 6, 3, 1.6, and 1.5 times more effective, respectively, in reducing climate change costs compared to sprawl neighbourhoods. Sprawl neighbourhoods in Greater Melbourne account for an estimated $333 million annually in climate change cost linked to transport emissions. By adopting integrated transport and land use planning, these costs could potentially be reduced to $68 million annually. The findings highlight the importance of targeted, neighbourhood-specific interventions in achieving net zero transport.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".