Joint Optimization of Critical Concession Parameters for Sustainable PPP Contracts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it