Regulating PPP Projects in the Energy Sector: An Exploratory Survey of Skills Required
Bibliographic record
Abstract
The involvement of the private sector in the provision of public services through concessions has been growing over the years and the energy sector has seen its own fair share of such concessions. Because these services have monopoly characteristics, regulatory institutions were set up to protect society from monopoly exploitation, inefficiencies and market failures. However, there has been a growing disenchantment with the state of service provision around the globe and consumers are blaming regulatory institutions' inability to protect them. The effectiveness of any regulatory institution is dependent on the expertise and competence of its staff. Therefore, this study seeks to determine the capacity requirement for effective regulatory governance and how best the present capacity gaps can be filled. This study reports the results of a survey of 101 energy industry stakeholders in public and private sectors across 35 countries. It was found that expertise in management, contract design, business analysis, project management, facilities management, risk management, ex-post negotiations, and sector-specific knowledge were crucial to the effective performance of regulatory institutions. Recruiting experts from the private sector was considered the most effective method of filling the capacity gaps in regulatory institutions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".