European Energy Crises, Climate Action and Emerging Market of Carbon-Neutral LNG
Bibliographic record
Abstract
Liquified Natural Gas (LNG) has received world-wide attention due to its growing market demands as pointed out by International Energy Agency (IEA), and McKinsey. This article aims to observe contemporary European developments in accordance with the Union’s energy strategy and the Paris Agreement. With the reduction in import of Russian gas, purchasing LNG became an alternative policy option to meet overall energy needs in Europe. European governments and companies are making large investments in land-based regasification terminals and floating storage and regasification units (FSRUs). These trends have fueled hopes that Europe may be able to avoid the worst-case scenario of massive gas shortages, rationing, and industrial shutdowns in the coming months. Nonetheless, such positive short-term developments should not obscure the challenges Europe’s energy-dependent industries are facing due to high gas and electricity prices, which will likely remain elevated for some time. Industries with gas-intensive production or with high absolute demand for gas could still see disruptions during this winter. Moreover, this article also evaluates the role of carbon-neutral LNG in European energy crises and its link with eco-friendly processes as set out by the EU and its consequences for the Asian market. The major findings include that the existing carbon measurement framework does not meet the global needs of the LNG industry. Moreover, the breadth of LNG usage is linked to viable GHG emission framework availability.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".