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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".