Towards an Evidence-Based Value-for-Money Assessment Methodology: Consideration of Innovation, Flexibility, and Risks in Public-Private Partnerships
Bibliographic record
Abstract
Public-private partnerships (PPPs) have been advocated as an effective delivery model that promotes innovation, flexibility and efficient risk allocations. Value for Money (VfM) assessment is a crucial decision-supporting tool in the developing process. However, the current assessment methods suffer from subjectivity and ambiguity. The thesis documents the doctoral research in developing an integrated probabilistic VfM assessment framework that characterizes the interaction among innovations, flexibility, and risks in PPPs based on multiple research methods including personal interview, empirical data analysis, analytical model and framework development. The study consists of four major pieces, each representing a published or to-be- published journal article. The first paper deals with empirical evidences of the use of engineering innovations in Canada’s PPP market. Personal interview was employed in the study. The empirical evidence proves that PPPs does provide unique innovation opportunities and scope. The second paper presents a direct comparative analysis between traditional and PPP projects by tracking the cost and schedule performance data in Canadian market. The research demonstrates that PPPs outperform traditional models on cost and schedule performance during the construction stage. The third paper presents a stochastic modelling methodology to evaluate the lifecycle cost risk considering maintenance flexibility, characterizing the complex interaction among the lifecycle cost, performance deterioration, and maintenance strategies. Simulation-based optimization and dynamic programming analysis are used to determine the probability distributions of the lifecycle cost and the VfM. The study shows that the lifecycle cost risk is not fully transferred to the private party. Finally, the fourth and last paper integrates the results from the previous three papers and develops a probabilistic VfM assessment framework to incorporate interaction between value drivers and uncertainty sources based on empirical evidences. A new VfM assessment paradigm using the probability of negative VfM as the decision criterion is recommended. The proposed VfM framework makes use of the best empirical evidences available, and when empirical data are not available, some credible analytical models to quantify the value and risks associated with the value drivers. The proposed framework has painted a clear road map to enhance the objectivity and validity of the VfM assessment.
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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 | 0.185 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.032 | 0.020 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| 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".