Energy Justice and Territory: Present and Futures of Wind Energy in Brazil
Bibliographic record
Abstract
Transitioning towards renewable energy sources is crucial in mitigating climate change and reducing greenhouse gas emissions. Global energy consumption is expected to increase 50 % by 2050, meaning one of today’s main challenges is complying with those demands without tampering with the uncertainties of global climate change. To address climate change renewable energy sources are essential and wind power plays a great role in the energy matrix. Brazil is one of the front runners in the energy transition, where wind power has expanded since the early 2000’s. The state of Bahia, in Northeastern Brazil, is currently the region where wind energy is expanding the most. However, conflicts related to territoriality and justice aspects are increasing in the state due to the fast-expanding wind energy sector. This study applies document and content analysis to explore the multiple narratives regarding the wind energy expansion in the state of Bahia, Brazil. Framing theory and theories addressing power struggles and conflicts in relation to the energy transition will guide the analysis of the documents included in the material. Based on the analysis of the Bahian case, this study shows that different actors frame the matter of wind energy expansion differently depending on their positionings. Civil society and local perspectives are not present in policies and decision-making processes, including the planning and installation of wind energy parks in the studied case. The results suggest that inclusion and participation of local actors, stakeholders and the civil society is essential to ensure a just and sustainable transition to clean energy sources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".