Assessment of energy poverty in EU countries in 2010-2022
Bibliographic record
Abstract
The main goal of the paper was to analyze the level of energy poverty in EU countries, with particular emphasis on three years: 2010, 2015 and 2022. The basic definition of energy poverty assumes a situation in which a household is unable to provide for an adequate level of energy services at home. Choice of the time period for the analysis was dictated by the availability of statistical data and, on the other hand, by the desire to analyze the impact of the time factor on the phenomenon under study. The application of the modified TOPSIS method for the construction of synthetic measures, in which common coordinates of the Positive Ideal Solution and Negative Ideal Solution were calculated for all analyzed periods, made it possible to assess the dynamics of the analyzed phenomenon between these periods. The carried out analyses show that EU countries remain differentiated in terms of energy poverty levels, but that this variation has been decreasing over time. This clearly indicates that the level of the examined phenomenon is equalizing in the analyzed group of countries. Particularly important was the improvement in the positions of the member states, whose accession took place after 2004. An in-depth comparative analysis of changes in energy poverty levels between the “new” and “old” member states is the essential added value of this work. Due to the changing geopolitical conditions in Europe and around the world, it should be borne in mind that not only developing countries will face energy shortages. Therefore, the authors believe that it is crucial to commit to political actions and to conduct scientific research on the widest possible use of various types of energy in order to reduce energy poverty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".