Selection of PPP Projects in China Based on Government Guarantees and Fiscal Risk Control
Bibliographic record
Abstract
Public-Private Partnership (PPP) is an effective investment channel for government to provide public services. PPPs have the advantage of transferring some project risk to the private sector. They also imply that the public sector should establish appropriate laws and regulations to enable government departments to effectively avoid the emergence of new fiscal risks, which may affect the sustainability of fiscal budgets. This paper expounds the fiscal risks implied by PPP projects in China and the status of government guarantees in various forms of PPP projects; chance-constrained goal-programming (CCGP) is used to simulate government project selection under budget and risk control constraints. The analysis takes fiscal space, the expected costs and benefits of government guarantees, and the possibility of excess government subsidies into consideration. Constrained by fiscal risk minimization and budget limitations, PPP projects with government guarantees can maximize social-economic net present value and simultaneously optimize welfare. The paper also puts forward corresponding policy recommendations based on the research findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".