Who, How and How Far? Renewable Energy Transitions in Industrialized and Emerging Countries
Bibliographic record
Abstract
The purpose of this paper is to analyze the current implementation status of renewable energy projects, analyzing not only the five country leaders during the last decade, in terms of their installed capacity, but also a set of emerging countries playing an important role at regional levels (the who of energy transitions), the sources of green energy that they have chosen ( how the transitions are being done), and the trends in greenhouse gas reduction that they have achieved ( how far the transitions have progressed). Calculations based on various statistical sources—mainly the International Renewable Energy Agency (IRENA)—show that, while renewable energy installed capacity and implementation of renewable energy (RE) projects have grown in China, they have decreased to differing extents during the last ten years in the four other countries among the top five leaders in renewables (the US, Brazil, Germany and Canada). On the other hand, some non-industrialized countries have emerged in the regional renewable energy scene, and although they still do not stand out globally, they do play a significant role. In fact, in terms of transition toward a renewable energy matrix, the data shows that industrialized/high-GDP countries have made less progress at the national level than emerging ones. Regarding CO 2 emissions, there are contrasting trends among the latter set of countries; however, the data analysis also shows that although economic growth negatively affects the decrease of CO 2 emissions, individual energy consumption patterns might have an even greater positive impact in reducing emissions; current data from the US, China, Iran and the United Arab Emirates are good examples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".