Evaluating the Impact of Macroeconomic Policy Interventions and Recession on a Sewage Treatment PPP Project Using a System Dynamic Model
Bibliographic record
Abstract
The revenue of a public-private partnership (PPP) project is influenced by macroeconomic scenarios such as economic recession and policy adjustments. But these macrofactors and their dynamic relations with microfactors in PPP projects have not been thoroughly understood. In this article, system dynamics (SD) and real option (RO) are integrated to develop a novel model to investigate the impacts of the macro-risk factors on the revenue of PPP projects. Five scenarios were studied through simulation. The results indicate that the loan interest rate and tax rate are negatively correlated to the revenue, while the GDP growth rate and self-owned capital rate are positively correlated. This indicates that the government can stimulate the private sector to invest in PPP projects by providing lower loan interest and increasing the self-owned capital rate. This integrated approach has been proposed for use by decision-makers to evaluate the impact of economics and policies in the future. This study provides a comprehensive review and reliable theoretical analysis regarding the adoption of PPP by China’s local governments, yielding to main policy implications for further promoting the efficiency of PPP development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 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".