Critical Risk Factors in PPP Waste-to-Energy Incineration Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it