Identification of the Barriers and Key Success Factors for Renewable Energy Public-Private Partnership Projects: A Continental Analysis
Bibliographic record
Abstract
The global energy demand has been increasing and posing multiple challenges across the globe, including global warming, environmental pollution, and energy-sustainability issues. Thus, multiple countries have been adopting renewable-energy (RE) sources to provide clean, reliable, affordable, and sustainable energy. Previously, a number of renewable energy projects has been delivered in the form of a public–private partnership (PPP) to take advantage of the private sector’s investment, technology advancements, and expertise. In general, renewable-energy projects are considered large-scale universal projects that involve expertise from different countries and require a clear understanding of the barriers and key success factors (KSFs) across the globe. Thus, this paper focuses on providing a comprehensive understanding of the main barriers and success factors of renewable-energy projects across the globe. For that aim, a comprehensive literature review was first carried out to identify and report on the barriers and KSFs of renewable-energy projects. This was followed by a questionnaire survey wherein the opinions of 60 experts with wide experience in RE PPPs in multiple countries were collected and analyzed. The analysis shows that political and regulatory barriers are the main risks globally. Additionally, well-prepared contract documentations and skilled and efficient parties are the KSFs. However, these factors change from one continent to another. Additionally, this paper sheds light on the difference between the public and private sectors’ perceptions on the severity of the risks and the importance of the KSFs to each sector.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".