Prospects for the Development of the Oil and Gas Industry in the Regional and Global Economy
Bibliographic record
Abstract
Problems of energy efficiency, along with increasing environmental safety of production and increasing social responsibility, are becoming a central object of research. Therefore, the main goal of the work is to analyze the prospects for the development of the oil and gas industry. It was established that innovative technologies play an important role in the development of energy. In the structure of public administration, the definition of the place in the close relationship with commodity-money relations, mediating its implementation. But refusal of oil resources will lead to negative consequences. It is established that the maximum production of conventional oil in the world in the amount of 4.5-4.8 billion tons per year will be achieved in 2020-2030. Major areas of conventional oil production in this period will be oil and gas basins of the Persian Gulf, Western and Eastern Siberia, the Caspian sea, the Atlantic shelves of Africa and South America. The achieved level of oil production can be maintained by large-scale involvement in the development of non-traditional sources (bitumen and shale oil). According to the raw material base, the leaders of unconventional oil production should be Venezuela, Canada, Russia and the United States. Keywords: environmental policy, oil production, forecast, coal-fired power plants, economic crisis. JEL Classifications: L100, Q400, Q430.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".