A Typology of Strategic Behaviour in PPPs for Expressways: Lessons from China and Implications for Europe
Bibliographic record
Abstract
In line with governance trends around the world, a growing number of expressways in the People’s Republic of China are managed as Public-Private Partnerships (PPPs). The tremendous growth in demand for mobility in newly emerging economies has led to a gap between investment needs and available public funding. Using private funds is potentially helpful in closing this gap and accommodating the social and economic needs of motorization. By some, it is also hoped that contracting-out and involvement of the private sector will lead to higher transparency and accountability. However, in line with what has been found in various transport infrastructure modes, during uncertain and hazy transition periods that arise after infrastructure reforms, many forms of potentially pernicious strategic behaviour can pop up. Strategic behaviour emerges from information a-symmetry between private and public players, where the former act as agents and the latter as principals. In this article, China’s evidence on various types of strategic behaviour in the management of expressways is found. Several PPP projects for expressways in China are investigated empirically. And conclusions are drawn as to what possible cures are effective countermeasures of strategic behaviour, and what are the implications for Europe.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".