Cross-Country Analysis of Energy Subsidies Efficiency
Bibliographic record
Abstract
The subject of the research is energy subsidies of states for fossil fuels that remain high, which constitutes according to IMF 6.5% of the world GDP and is used by many states as an important instrument for agriculture and industry development, for job creation, as well as for energy safeguarding. However, energy subsidies often cause energy overconsumption, natural resources exhaustion acceleration and decrease stimuli for investments into green power engineering and renewable energy, which resulted in the 2009 agreement of G20 countries to start stage-by stage reducing fossil fuels subsidies. The purpose of the article is developing a model for quantitative assessment of oil extraction public support. On the basis of the empirical model developed, a cross-country analysis of comparative oil extraction public support efficiency in five countries (three of them developed economies: the USA, Canada, Norway; two countries with developing economies and emerging markets: Brazil, Russia) in 2000–2017 using analysis of the functioning surroundings Data Envelopment Analysis (DEA) that allows to uncover not only technical, but also cost effectiveness of budgetary oil extraction support. The data for the empirical model are selected from the statistical database of OECD. The results obtained demonstrate that the intensifying of oil and gas sector development practically does not correlate with public policy actions in Russia, and urgent measures to eliminate ineffective energy subsidies are necessary.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".