Evaluating renewable energy adoption in G7 countries: a TOPSIS-based multi-criteria decision analysis
Bibliographic record
Abstract
The transition from conventional non-renewable and fossil fuels to renewable energies represents an innovative approach toward achieving environmental, economic, and social sustainability. This transition has gained more importance due to global warming and environmental issues. A significant portion of carbon emissions and energy consumption is attributed to developed countries. Despite the many similarities, these countries have different performance in the field of energy. Thus, these countries must be ranked according to the attitude of renewables. To be able to identify the beneficial and harmful factors in the transition to renewable energy among them. In this article, The G7 countries were ranked by using the TOPSIS method. The use of TOPSIS method is used for the first time in the survey of G7 countries and it shows that this method is new. For each country, a set of primary data such as total power generation, power generated by each renewable source, carbon dioxide emissions, etc. were collected. Subsequently, five parameters were calculated based on primary data for each country: carbon emissions per dollar of GDP, the ratio of renewables per capita to the total production capacity per capita, population density, warming impact per dollar of GDP, and investment in renewable energies per dollar of GDP. A score was assigned for each of the five parameters and seven countries were ranked using the TOPSIS method. In this ranking, Britain ranked first place and Canada took the last place. Notably European countries, which rely less on fossil resources, generally outperformed other G7 nations, which rely less on fossil resources, outperformed other G7 nations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".