Investigation of energy consumption and renewable energy resources in top ten countries with most energy consumption
Bibliographic record
Abstract
Top ten countries with the most energy consumption (China, United States of America, India, Russia, Japan, Canada, Germany, Brazil, South Korea and Iran) - which consume 65% of total world energy- is selected to analyze their energy resources and consumption. Data were collected from different world's banks as well as RETScreen software and then data are analyzed using statistical Methods. The results of the study demonstrated that India has the least ratio of energy consumption per population (0.534 Million tones oil equivalent per Million numbers of people). In the other hand, U.S. (7.095) as well as Canada (9.202) has the highest ratio of energy consumption per population among studied countries because of their high rates of Gross Domestic Product (GDP). Moreover, Germany and Iran have the least (0.095 Million tones oil equivalent per billions of US dollars) and the highest (0.628) ratio of energy consumption per GDP, respectively which represent Germany has the best energy efficiency in production unlike Iran. This is due to the lack of suitable energy audit and management, and modern technologies. Analyzing the RETScreen data locations indicated that Brazil (5.057 kWh/m 2 /d) and Iran (5.010) have the highest and Germany (2.866) has the least daily solar radiation horizontal averages. However, Germany uses the most (5.939 %) and in contrast Brazil (0.003 %) and Iran (0.037 %) use the minimum percentage of solar energy for their electricity generation. Also, results illustrated Canada (4.391 m/s), Iran (4.233) and U.S. (3.948) have the highest Wind speed average at level of 10 m, but Germany use the most (13.595 %) and Russia (0.001 %) and Iran (0.087 %) use the least of wind energy in their electricity generation. All in all, this study would suggest that Brazil increase solar energy usage and Iran to utilize more solar as well as wind energy resources instead of fossil fuels.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".