Critical Risk Factors in PPP Waste-to-Energy Incineration Projects
Bibliographic record
Abstract
Municipal solid waste (MSW) is increasing rapidly due to the global economic growth and worldwide mass urbanization, creating serious environmental, economic and social problems. In China, public-private partnership (PPP) is regarded as an effective mechanism to attract private capital to provide MSW treatment works and services, and hence a number of waste-to-energy (WTE) incineration projects have been developed. Various risks could occur in different stages of the PPP project delivery process, causing problems or even leading to failure of a project. This paper first identified 21 risk factors in PPP WTE incineration projects through literature review and case studies. Then, through a questionnaire survey, the top five most critical risk factors were found by statistically analyzing the significance of each factor. Next, factor analysis was conducted to determine the major common dimensions of the failure reasons in PPP WTE incineration projects. After that, agreement analysis was performed to explore the perspectives of academic researchers and industry experts in terms of the similarity and difference in the ranking of the risk factors. Finally, the causal relationships of the risk factors were discussed. Outputs of this research would facilitate both public and private sectors to design effective preventive measures to successfully address the risks in PPP WTE incineration projects, and they could also be used as a reference for risk management in PPP projects of other sectors as well.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".