Bibliographic record
Abstract
The transition from fossil fuels to renewable energy is crucial for achieving environmental sustainability and carbon neutrality. The research focuses on the global landscape of renewable energy capacity, utilizing data from the 2024 report by the International Renewable Energy Agency (IRENA). The data was meticulously cleaned and organized based on countries and renewable energy sources, followed by sorting in descending order and performing Pareto analysis to identify the top 80% user countries. Graphical analyses, including bar and pie charts, were employed alongside linear percentage calculations to determine frequency distribution. The findings reveal that 15 countries—China, the United States, Brazil, India, Germany, Japan, Canada, Spain, France, Italy, Türkiye, Russia, the United Kingdom, Australia, and Vietnam—account for over 80% (3,099,959 MW) of the world's total installed renewable energy capacity. China leads with 1,453,701.25 MW, followed by the USA with 387,548.59 MW, and Brazil with 194,084.66 MW. Solar energy is the largest contributor, representing 36.67% of global renewable capacity, followed by hydropower at 32.76% and wind energy at 26.29%. Bioenergy, geothermal, and marine energy contribute 3.88%, 0.38%, and 0.01%, respectively. The concentration of renewable energy capacity in a few countries and key sources underscores significant disparities in adoption and investment. The research emphasizes the need for tailored energy policies that consider regional resource availability, socio-economic structures, and geopolitical contexts to ensure equitable and sustainable energy development. Addressing these disparities is crucial for achieving the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 7, which focuses on affordable and clean energy for all. This study provides valuable insights for policymakers, highlighting the importance of a diversified and balanced approach to renewable energy adoption to contribute to global carbon neutrality and environmental sustainability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.029 |
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".