Public Private Partnership to Brownfield Remediation Projects in China: A Combined Risk Evaluation Approach
Bibliographic record
Abstract
Brownfields have been receiving significant attention all over the world because of their potential threats to the environment and public health. However, a funding shortage constitutes the main obstacle to the brownfield remediation (BR). In China, to ease financial dilemmas, the governments seek collaborations with private-sector companies, i.e., the Public Private Partnership (PPP) mode. Despite all the benefits, BR and PPP contain high risks, making stakeholders extremely cautious about investing in such projects. To support the decision-making process of the public and private parties, this paper designs a comprehensive approach to evaluate the risks of BR PPP projects in China. In more detail, several commonly used risk methods, such as TOPSIS, GRE, and FSE, are employed to construct a combined risk evaluation process, which applies multiple combined evaluation techniques to iteratively integrate individual results from those methods until a valid common result is achieved. To show the practical implementation procedure of the proposed combined approach, a hypothetical case study is performed to assess the risks of seven BR PPP projects. The analytical process also verifies that the consistency and reliability of the risk evaluation result can be achieved effectively and efficiently by jointly deploying multiple risk methods through combined techniques. The proposed decision framework facilitates a novel research idea in evaluating complicated risk situations, and can be applied to other similar scenarios where uncertainties and inconsistencies are inevitable.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".