Fosszilis energia és gazdasági növekedés szénalapú gazdaságokban: empirikus modellezés és fenntarthatósági kihívások
Bibliographic record
Abstract
The study examines the relationship between fossil energy consumption and economic growth in carbon-based economies, with a particular focus on the role of energy intensity and energy efficiency. We aim to quantify the impact of fossil energy consumption on economic growth through model-based analysis and identify the development and sustainability challenges of energy-intensive economies. The study used a Random Forest model to analyze the relationship between fossil energy consumption and GDP growth. The data are from the World Bank’s World Development Indicators database for the period 2013–2023, considering the economic structure of different countries. Energy intensity showed a strong positive correlation with GDP growth in China, Russia and Mongolia, while in more developed economies such as Canada and the United States, a lower or negative relationship is observed. Fossil energy-intensive economies benefit in the short term, but diversification is needed in the long term for sustainability. Diversifying the economy and increasing energy efficiency are key to sustainable development. The results can help policymakers plan the energy transition, especially focusing on reducing fossil fuel dependence and integrating renewable energy sources for economic and environmental stability. JEL codes: O13, Q43, Q56, C53, O44
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".