Navigating the energy transition: International oil company divestments and the stranded asset dilemma in Africa
Bibliographic record
Abstract
The global energy transition is accelerating the risk of stranded fossil fuel assets, posing significant economic and environmental challenges for resource-dependent countries. This paper critically examines the divestment strategies of International Oil Companies (IOCs) across five major African oil-producing countries (Nigeria, Angola, Gabon, Ghana, and Algeria) and analyzes how these transitions shape new patterns of asset stranding and governance vulnerability. The study provides an integrated evaluation of divestment impacts through a comparative analysis of regulatory frameworks and environmental governance capacities with scenario-based modelling. It finds that IOCs’ proactive divestments act as strategic risk mitigation mechanisms, enabling companies to minimize future stranded asset exposures while transferring substantial environmental, financial, and operational risks to host governments, local operators, and communities. Weak regulatory enforcement, fragmented institutional oversight, and opaque decommissioning frameworks exacerbate these risks, especially in countries with limited environmental governance capacities. Scenario modelling suggests a high probability of negative post-divestment trajectories unless urgent policy interventions are implemented. The study emphasizes the importance of enforceable decommissioning obligations, transparent divestment agreements, and enhanced environmental governance to effectively manage externalized risks. By distinguishing actor-specific challenges and offering a conceptual framework of stranded asset pathways, this paper contributes to advancing policy debates on sustainable asset transition management in the Global South.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".