The Brazilian Electricity Supply for 2030: A Projection Based on Economic, Environmental and Technical Criteria
Bibliographic record
Abstract
The expansion of the Brazilian energy supply from fossil sources prompted environmental concerns about the emission of Green House Gases (GHG). Furthermore, the Brazilian government was committed to the United Nations Framework Convention on Climate Change (UNFCCC) to reduce GHG emissions by 43% by 2030, compared to 2005. The aim of this study was to design the Brazilian electricity mix for 2030, while taking into account economic, technical and environmental criteria. In order to get this, Linear Programming optimization has been applied to obtain an electricity matrix with minimum cost of the Brazilian electricity generation system, considering GHG emission constraints – defined via the Life Cycle Assessment (LCA) technique –, as well as capacity generation and supply needs. In addition, LCA was also applied to obtain the environmental performance of the projected scenario and results were compared with those of 2005 and 2015. The analysis depicted that renewable sources represent 88% of the projected Brazilian electricity production in 2030, mainly hydropower, which accounts for 66%. In terms of Climate Change there is an impact reduction of 12% compared to 2005, while other categories such as Ionized Radiation and Terrestrial Ecotoxicity doubled and upped more than forty times. These findings led to conclude that environmental management should not be limited to GHG analysis, and must encompass other adverse effects. Moreover, this reinforces the importance of conducting analyses such as those provided by the LCA approach and include these results in the planning and decision-making processes of the energy sector.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".