Energy Security and Sustainability for the European Union after/during the Ukraine Crisis: A Perspective
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The special military operation initiated by the Russian Federation (RF) against Ukraine has focused on the European Union (EU) and, to a lesser degree, U.S. fossil fuel resource dependency. The Russian Federation’s economy is heavily geared toward exports of carbon-based fuels. As a result of the proximity of the EU and Ukraine, these two entities are the largest importers of RF fossil fuels. Ukraine’s and EU’s large population and heavy industries utilize energy in large quantities. As a result of the overreliance on Russian carbon energy imports, the overall energy security index of the EU dropped by approximately 1–1.5% over the last 20 years. The energy security index can positively correlate with greenhouse emissions or a composite unit considering gas reserves and carbon dioxide emissions. To improve the EU energy security index, the EU imposed several phase-out energy bans in coordination with the U.K., U.S., Canada, Japan, and Australia in response to the ongoing crisis. An energy balance analysis demonstrates that an attractive option, namely, a hydrogen (H 2 ) infrastructure upgrade at the EU regional level, is feasible. The infrastructure upgrade at the regional level could generate an energy equivalent substitution of 20 exajoules (1 × 10 18 J) for heating and power to enable the EU to be free of energy imports from the RF for all carbon resources except oil. Further policy changes to facilitate a transition to sustainable resources, along with corresponding improvements in the efficiency of businesses, housing, and transport sector, could make the EU carbon neutral by 2050 and free from RF carbon imports before 2060.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".