Making concepts matter: sustainable mobility and indicator systems in transport policy<sup>*</sup>
Bibliographic record
Abstract
Strategies for sustainable mobility and transport have, in recent years, been launched in many countries and also on the international level: so far, limited success has been recorded. However, the questions arise how the sustainability of transport systems and policies can in fact be measured, and how these measurements can be used in transport planning. This article focuses on indicators and monitoring frameworks applied in the transport sector. It explores a limited number of indicator systems presently in use, discussing if and how they contribute to making the concepts of sustainability operational for the governance of mobility. The six systems discussed include one general environmental indicator system, one transport policy performance measurement framework, and four indicator systems, which in particular focus on the interaction between transport and the environment. Four of the systems are national (Denmark, Canada and two from the US), and two are international (the European Environment Agency (EEA) and the Organisation for Economic Co‐operation and Development (OECD)). The paper concludes that these and other indicator systems appear to provide relatively limited guidance towards achieving sustainable mobility. There are four major issues which need considering if the aims of sustainability are to be incorporated more adequately: how to manage environmental comprehensiveness; how to bring causal factors into the systems; how to incorporate sustainability and policy targets, and how to link indicator systems and policy making.
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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 | 0.061 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.024 | 0.039 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".