Strategic planning in energy saving at industrial enterprises of Russia
Bibliographic record
Abstract
Our country has been and remains one of the leaders in the world's energy industry. Russia's energy needs are fully met by its own resources. Annual export volume of mineral fuel, oil and petroleum products is about 600700 million tons in conventional terms. Currently, the situation with exports is complicated by sanctions and the geopolitical situation in Russia and in the world. Only taking into account the embargo imposed by the U.S., Britain, Australia and Canada, export losses are more than 40 million tons. In addition, Russia is the world leader in proven reserves of natural gas, its volume more than 50 billion cubic meters. At the same time, it should be noted that such high indicators and sufficient resource potential are present at an extremely low level of energy efficiency. Thus, the volume of energy costs for the production of the average Russian producer is about twice as much as the global average. On this basis, the need to improve the quality of strategic planning and to promote targeted activities in the field of energy conservation in industry in the Russian Federation becomes obvious. Such activities will have not only positive economic, but also social, political, environmental and other types of effects. The most energy-consuming branch of industry in Russia is the processing industry, which is about 30 % of all final energy consumption. Another 70% of the energy saving potential is represented by metallurgy, chemical and oil refining and other industries.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".