Biogas as an alternative energy resource for Ukrainian companies: EU experience
Bibliographic record
Abstract
The paper deals with analysis of the preconditions of alternative energy market development in Ukraine. In this case study, the authors analyzed the EU experience. The results of analysis showed that the leader of the EU countries in renewable energy has already achieved the target (20%), which had been indicated. In addition, the findings showed that the share of renewable energy in gross final energy consumption has been increasing from year to year. The authors allocate that, according to the Ukrainian potential, biogas is the most perspective one among alternative resources. Moreover, results of analysis showed that Ukraine has the huge potential of agricultural sector. In this direction, the authors allocated the main types of the agricultural activities, which have the highest potential of biogas production: sugar factories, corn silage and poultry farms. The authors underlined that biogas spreading is restrained by the stereotypes that green investments are not attractive for investors. In order to analyze the economic efficiency of investments to the biogas installation, the authors calculated the profit from the biogas installation for poultry farm. The authors made two scenarios for calculation. The first – the whole volume of energy, which was generated from the biogas unit, will be sold with feed-in tariff. The second – the farm covers its own needs in electricity, the rest will be sold with feed-in tariff. The findings showed that the first scenario is more attractive. Moreover, the farm could receive higher profit if it installed the biogas in 2016, not in 2017. In addition, based on the EU experience and features of farm functioning, the authors approved that the biogas installation has not only the economic effect (profit and additional profit) for company, but also ecological and social effects for rural area, where this farm was located.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".