Analysis of Factors Influencing Energy Intensity in G20 Countries
Bibliographic record
Abstract
Aims: The purpose of this study is to analyze the impact of Gross Domestic Product (GDP), Industry Value Added (IVA), Urban Population (UP), Trade, and Foreign Direct Investment (FDI) on Energy Intensity in G20 countries. Study Design: This research used a quantitative descriptive method using panel data analysis. Place and Duration of Study: The scope of this research extends to G20 member countries such as Argentina, Brazil, Canada, China, Germany, European Union, France, United Kingdom, Indonesia, India, Italy, Japan, Korea, Mexico, Rusia, Saudi Arabia, Turki, United States, and South Africa, between 1990-2021. Methodology: This research uses descriptive method combined with panel data analysis, analyze determine of GDP, IVA, UP, Trade, and FDI on Energy Intensity in G20 countries. Furthermore, the data uses is secondary data that has a regression model on panel data from 1990-2021. Results: The result of this research show that IVA has a positive relationship and has a significant effect on increasing energy intensity in G20 countries. GDP, Trade and UP variables have a negative relationship and have a significant effect on Energy Intensity in G20 countries. Meanwhile, the FDI variable has no significant effect on Energy Intensity in G20 countries. Conclusion: Based on research result, Energy Intensity in G20 countries is influenced by various factors, The IVA factor has a positive and significant relationship with energy intensity, can be utilized to increase productivity and economic growth, but need to be balanced with effort to increase energy efficiency. While the GDP, Trade and Urban Population factors have a negative and significant relationship to energy intensity. However, FDI does not have a significant effect on energy intensity in G20 countries. The government should consider policies to reduce dependence on intensive energy, especially in sector that have a negative relation with energy intensity such as GDP, trade and urban population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".