Joint Optimization of Critical Concession Parameters for Sustainable PPP Contracts
Bibliographic record
Abstract
To fully leverage the advantages of the public–private partnership (PPP) model in delivering sustainable infrastructures, the values of critical concession parameters need to be determined. Traditional determination methods overlook the sustainability benefits of the projects, which hinders their application on sustainable infrastructures financed as PPPs. This research enriches the risk allocation scenarios by quantifying the sustainability benefits and develops a multiparameter joint determination method according to fair-risk allocation and game equilibrium principles. This research presents an innovative concept of value-for-money risk and highlights the fact that for sustainable PPP contracts, the concession parameters should be determined for reasonably shared value-for-money risks instead of revenue risks. Project JZ is created as a numerical example to verify the applicability of the proposed method. The result shows that the proposed method can determine the optimal values of concession period, concession price, and minimum revenue guarantee (MRG), which contribute to a win–win outcome in achieving the goals of financial and sustainability benefits for both public and private parties. The data analysis reveals that achieving the equilibrium risk-sharing ratio for value-for-money risks requires a shorter concession period or a decreased MRG level than revenue risks. Also, higher concession prices correlate with shorter concession periods and increased MRG levels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".