Geopolitical risk index for guiding international sustainable energy trade
Bibliographic record
Abstract
Global fossil fuel markets are volatile, influenced by supply chain disruptions and geopolitical instability. As renewable energy capacities expand and emission reduction efforts intensify, more resilient and equitable trading structures are critical to avoid reproducing similar fossil fuel market vulnerabilities. This study supports informed decision-making in electricity-based fuel trade by developing a Geopolitical Risk Index tailored to the energy sector. The index was constructed through a structured selection and evaluation of existing risk indicators. Relevant indices were identified via literature review and selected based on predefined criteria. Selected indices were categorised into four dimensions: resilience, institutional quality, conflicts, and business conditions. The resulting index provides a quantitative tool for assessing geopolitical risks and evaluating energy trade partnerships. Applied to green e-fuel trade, the index assesses traded volumes and costs based on country-specific production potentials, demand, and risk scores. Results indicate that the European Nordics, Singapore, New Zealand, and Canada are the most geopolitically reliable partners, while conflict-prone nations score lowest. Excluding high-risk partners increases import costs by only 1.7% but reduces supply risks. Without considering risks, Brazil, Yemen, and several sub-Saharan countries dominate exports. Applying risk scores eliminates Yemen and increases export shares from Brazil, Namibia, Angola, and Peru. The index correlates with Moody's sovereign ratings, suggesting it captures broader factors influencing both credit worthiness and trade reliability. Incorporating the Geopolitical Risk Index into energy trade planning can help governments and investors reduce exposure to unstable regions, enhance supply security, and promote a more resilient and sustainable global energy system. • Geopolitical risk scores calculated for 190 countries across the world. • Resilience, institutional quality, conflicts, and economy are considered. • Geopolitical risk scores have an impact on potential e-fuel trading in the future. • Nordics, Singapore, and New Zealand are ranked as most reliable trade partners. • Geopolitical risk scores can be applied in energy modelling.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".