Analysis of factors influencing the electricity (capacity) price growth in the energy market of the Siberian Federal District
Bibliographic record
Abstract
One of the main competitive advantages of the Russian Federation in the industrial products world markets is the relatively low prices for electricity sold on the domestic market for the industry sectors. Low electricity prices for energy-intensive industrial sectors, such as ferrous and non-ferrous metallurgy, mining, oil extraction and refining, etc., allow Russian economy to occupy a dominant position in various areas of world markets, and industries are able to maintain financial stability indicators and finance modernization and technical re-equipment programs. In the fourth quarter of 2021 and the first quarter of 2022, on the territory of the Siberian United Energy System, which includes consumers of the Siberian Federal District, an increase in prices for electricity supplied to end consumers, primarily industry, was revealed. In March 2022, the increase in electricity prices compared to the same period of the previous year, in the Krasnoyarsk Krai was 18.2 %, in the Republic of Khakassia – 13.1 %. In other regions of Russia over the specified period, the increase in final electricity prices for industry averaged 2.6 %. Thus, the empirical analysis carried out in the article revealed that electricity prices in the regions of the Siberian Federal District actually began to approach the average electricity prices in other federal districts of Russia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".