Public transport investments as generators of economic and social activity
Bibliographic record
Abstract
High-quality public transport systems increase accessibility, which is linked to wider economic and social benefits that improve the health of the populations served. This paper reviews evidence on the existence and magnitude of these wider benefits. We searched for academic studies that evaluated the effects of specific public transport investments or disinvestments on levels of economic and social activity. Public transport improvements increase economic activity, both at an aggregate level (higher gross domestic product) and household level (higher income), although the effect can be geographically imbalanced. Better public transport boosts employment but tends to increase house prices, leading to gentrification, although suitable policies can prevent this effect. Public transport improves social connections, especially for older people in isolated rural areas. In urban areas, it can reduce connections due to barriers to pedestrians. Disinvestment in public transport, such as closure of bus services, has multiple economic and social costs, although the evidence is still scarce. Public transport has potentially wide but possibly unequal economic and social benefits. • Public transport investments boost GDP and employment • Economic effects are usually geographically imbalanced • Better public transport increase house prices, possibly leading to gentrification • Public transport improves social connections, especially for older people • Disinvestment has economic and social costs but evidence is still scarce
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".