Trends and prospects for the development of renewable energy sources in the european union
Bibliographic record
Abstract
The largest net exporter of traditional energy resources to the EU countries, Russia should take into account the prospects for the development of renewable energy sources in European countries, since inter-fuel competition can lead to a decrease in demand for hydrocarbons from Russia in the energy markets of the European Union. Fuel granules (pellets) are gradually becoming one of the traditional types of fuel for generating heat and electricity, as well as industrial steam in various industries… All plant biomass in this scenario accounts for only 2.8 GW. The highest cost of electricity generation in the EU determines its dependence on subsidies, as opposed to heat production. The examples of the Netherlands and Great Britain are very indicative. In 2010–2012, the Netherlands was one of the first places in the EU for the import of pellets, because at that time there was a subsidy program for the generation of electricity by co-firing biofuel (pellets) with coal – about 5–6 eurocents per 1 kWh. Since 2013, after the closure of the program, the import of pellets has decreased by more than three times. In the UK, on the contrary: in 2010–2012, the annual import of pellets was about 1 million tons, and after the adoption of the subsidy program by 2020, it approached 9 million tons per year. Sales of renewable energy products to small-scale energy enterprises, primarily for generating thermal energy and industrial steam, are stable throughout the year and predictable, in contrast to supplies to large power plants, which are owned by European and international energy concerns, as a rule, owning controlling stakes in pellet production in the USA and Canada. If for the generation of industrial steam at industrial enterprises pellets can be used all year round, sometimes around the clock, then at thermal power facilities they are used depending on weather conditions, primarily temperature.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".