Bridging Systemic Gaps in Waste-to-Energy Infrastructure: A Comparative Study of PPP Structuring, Sustainability Metrics, and Participatory Governance in Sub-Saharan Africa and the EU
Bibliographic record
Abstract
In light of increasing urbanisation and the dual challenges of waste accumulation and energy insecurity, Waste-to-Energy (WtE) infrastructure presents a promising intersection of environmental stewardship and electricity generation.The effective deployment of WtE technologies in developing regions, particularly Sub-Saharan Africa, remains hampered by fragmented governance, limited financing, and insufficient public engagement.Public-Private Partnerships (PPPs) offer a strategic funding model, yet their efficacy is unevenly realised across geographies.This study adopts a Systematic Literature Review (SLR) methodology within an interpretivist research paradigm, employing a qualitative research approach to analyse 58 peer-reviewed articles, government reports, and institutional case studies published between 2010 and 2025.Data were analysed thematically using NVivo software.The findings reveal that successful WtE PPPs in the UK, Germany, the USA, and Canada are underpinned by robust regulatory frameworks, sustainability metrics, and participatory mechanisms.In contrast, WtE initiatives in Sub-Saharan Africa, including South Africa and Zimbabwe, are undermined by weak policy coherence, poor stakeholder coordination, and limited institutional capacity.The paper concludes that bridging these systemic gaps requires integrative governance, contextualised sustainability assessment, and inclusive stakeholder engagement.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".