Implementing wastewater treatment projects through build–operate–transfer contracts
Bibliographic record
Abstract
This paper focuses on the initial assessment of wastewater treatment (WT) projects through Public Private Partnerships (PPPs).Due to the fact that the initial investment of a PPP-type WT project is too high and can be fi nanced by both the public and private sectors, crucial during the project's feasibility stage is to estimate the partners' funding rates.Herein, the fi nancial analysis that is included in the cost benefi t analysis methodology as well as the quantitative value for money assessment method are used, in order to introduce a new process that estimates the funding ratios of the partners.Specifi cally, the process calculates the upper and lower boundaries of the public and private sectors' funding ratios in the initial investment, which include all the funding scenarios that are profi table for both partners.It applies mostly in the WT projects that are considered to be implemented through the build-operate-transfer contract type, which is probably the most commonly used type in PPPs.The new process is used in a WT project case study, in which alternative funding scenarios of the initial investment are examined and two specifi c funding scenarios are distinguished, which include all the possible funding ratio values by the public and the private parts.The process that is presented here can be a useful tool to decision makers, because it helps them to evaluate different funding scenarios of the initial investment and to select the most suitable in each case option, that will be profi table for both partners.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".