Sustainable Transportation for the Climate: How Do Transportation Firms Engage in Cooperative Public-Private Partnerships?
Bibliographic record
Abstract
This research examines the effectiveness of transportation-sector public-private partnerships (PPPs). Coordination across sectors is needed to reduce transportation-related greenhouse gas emissions. PPPs are of interest to transportation firms, but they may prefer private-sector opportunities given that working with the public sector can present challenges. However, the challenges are not clear and, therefore, this needs research investigation to develop understandings for policy to make PPPs work better for firms. Moreover, this research informs firms so that they may better comprehend and manage the risks of PPPs or choose other opportunities. This empirical research uses a sample of 300 transportation firms across 28 countries. The findings suggest that, although government contracts may be lucrative, the institutional environment of the PPP context is not preferable to other business-oriented private-sector opportunities. If more sustainable transportation is to be constructed to address climate change and other public interests, policymakers may need to rethink PPPs to adapt to the needs of transportation firms.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".