A Bibliometric Study on the Nexus of Economic Growth and Renewable Energy in Brazil
Bibliographic record
Abstract
The nexus between economic growth and energy consumption is essential in energy economics and economic development literature. The recent urgency in accelerating the decarbonization processes of economies has enhanced relevance to analyzing this empirical relationship in the face of technological advances, regulatory changes, and the expanding uptake of renewable energy technologies worldwide. This article presents a bibliometric analysis of the literature on economic growth, energy consumption, and renewable energies in Brazil using clustering as a support tool. Between 1995 and 2022, 177 Energy-Growth, Brazil, and Sustainability studies were published. It was found that China leads the ranking of publications, taking part in 28.84% of the production related to the link between economic growth and consumption of renewable energy in Brazil, followed by Turkey (21.52%) and Brazil (21.31%). The participation of other countries in the literature adds up to 32.29%. Keywords such as “ecological footprint,” “environmental sustainability,” “environmental Kuznets curve,” and “emissions” show how in recent years, the literature has been guided by a discussion related to economic-environmental factors. Another result was that the Granger causality test is a research frontier with the most significant associated strength.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.062 | 0.141 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".