The adequate integration of sustainability into transport policy
Bibliographic record
Abstract
In this paper we discuss to what extent transport policy fails to integrate five types of external effects, and what kind of research needs follow from the objective to make transport sustainable. The discussion is a synthesis of the findings collected and synthesized in the framework of Focus Group 4 of the STELLA project. The assignment of Focus Group 4 was to draw up a set of recommendations for future transport policy-oriented research dealing with external effects, on the basis of a series of specialist workshops. Five different kinds of so-called external effects of transport were identified beforehand, being environment, safety and security, public health, land use and congestion. Safety and security as well as congestion are external effects in the sense that they are not ‘internalised’ in the price of the transport service, but they do affect predominantly others within the transport system. This means that with some delay the transport market still reacts to changes in the intensity of these effects, albeit biased or insufficient. The public goods character of both externalities however implies that public intervention is needed to attain better performance of these external effects, partly via internalisation of the external effects and partly via planning (i.e. by evaluating the trade-offs ex ante). The other external effects, however, are not only insufficiently internalised in the transport price, but they are also predominantly affecting parties outside the transport system. Consequently, changes in the intensity of these effects do not feed back directly into the transport market. In that case public intervention has even a more complicated task, since it takes more time and is more complicated to learn what are actually the right balances for the trade-offs between adequate access and, in turn, sustainability, spatial quality, and public health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".