The Africa-Europe energy interconnection: Assessing green hydrogen suppliers for France
Bibliographic record
Abstract
Green hydrogen (GH 2 ) is a promising renewable energy vector with the potential to reduce global dependence on fossil fuels significantly. Although its production is technically feasible worldwide, the availability of natural resources and the suitability of local conditions impose substantial geographic constraints. In this context, the Africa–Europe green energy interconnection presents a strategic opportunity to facilitate cross-continental collaboration in the energy transition. By leveraging Africa's vast renewable energy potential, particularly solar and wind, this partnership can accelerate Europe's decarbonization goals while enhancing regional energy security. Beyond environmental benefits, such cooperation also stimulates economic development on both continents, offering a scalable model for global green energy alliances that integrate sustainability, resilience, and shared prosperity. This study explores the strategic role of Africa as a future green hydrogen supplier for France, addressing a critical dimension of the global energy transition. The research introduces a multi-criteria decision-making framework to evaluate nine African countries as potential green hydrogen suppliers, considering twelve multidimensional criteria across four key categories: financial viability, reliability, environmental impact, and resource availability. We employ a comparative approach using TOPSIS and VIKOR to provide a robust assessment of supplier rankings. The findings highlight Morocco as the most promising green hydrogen supplier for France, followed by Algeria, with a comprehensive sensitivity analysis revealing how decision-maker preferences influence ranking outcome. • Evaluated nine African countries as green hydrogen suppliers for France using TOPSIS and VIKOR. • Developed a MCDM framework with 12 multidimensional criteria across financial, environmental, political, and resource categories. • Identified Morocco as the most resilient and promising supplier, followed by Algeria and Namibia. • Proposed a regional GH 2 network centered on North African countries, leveraging existing gas pipeline infrastructure. • Sensitivity analysis to assess ranking robustness and highlight decision-maker influence on outcomes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".