Highway Project Value of Money Assessment under PPP Mode and Its Application
Bibliographic record
Abstract
The application of Public-Private Partnership (PPP) mode in transportation infrastructure construction has achieved more progress worldwide; now this mode has been adopted in highway projects of China from 2015. In the application of PPP mode, there are three main facts in China, which include whether the government is responsible for land acquisition and resettlement (LAR), the discount rate changes, and the replacement of business tax by value-added tax (VAT) in 2016. So this paper discusses Value for Money (VFM) quantitative assessment of highway projects under PPP mode in China, which considers currently three actual issues in China. A case study of Heda freeway in China has shown that (1) the government’s responsibility for LAR compensation may attract social capital investor and reduce the risk of social instability, (2) a reasonable range of a low discount rate can greatly reduce government expenditure, and (3) the replacement of business tax by VAT will increase the highway project company’s burden. The research results will be helpful for value of money assessment of highway projects under PPP mode in China and may offer the reference for other countries’ highway projects under PPP mode.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".