Trends in Renewable Electricity Generation in the G20 Countries: An Analysis of the 1990–2020 Period
Bibliographic record
Abstract
The use of electricity from non-renewable sources increases environmental impacts. Therefore, several countries have committed to increase the use of renewable sources. Considering the importance of the Group of Twenty (G20), this article aims to identify trends in renewable electricity supply in these countries. The data collected are from the International Energy Agency (IEA) between 1990 and 2020. The methods measured the production of each type of electricity in the G20 matrices and identified the influence of the population, Gross Domestic Product (GDP), and CO2 emissions in renewable electricity production using multiple linear regression. In terms of results, Brazil and Canada have the most renewable electricity matrices and higher per capita renewable production than non-renewable. Saudi Arabia presented the smallest renewable matrix throughout the analyzed period. All 20 countries have varied electrical production, with different amounts of solar, wind, hydro, biomass, geothermal, and tidal energy. Countries with the highest GDP are not necessarily the largest producers of renewable electricity. Hydroelectric energy, the biggest highlight in renewable production, is making room for other sources such as wind and solar, which grew the most in terms of participation in the electrical matrices. The waste, geothermal, and tidal energy participation have shown a timid but constant growth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.001 | 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".