Impacts of Climate Change on Hydroelectric Power Generation – A Case Study Focused in the Paranapanema Basin, Brazil
Bibliographic record
Abstract
Climate change is taking special attention among the economic agents, especially due to the uncertainties and risks associated with it. In countries with a significant share of renewables in their energy matrix, this phenomenon implies on challenges for the energy planning in future scenarios. In this context, this study establishes a correlation between energy security and climate change by understanding the ability to generate hydroelectric power in large-scale hydroelectric (HEP) and small hydroelectric plants (SHP), in the Alto Paranapanema Basin (São Paulo, Brazil), a region with rainfall anomalies and water flow changes due to climate change. This region was chosen based on its future scenarios on climate change, especially those of rainfall anomalies and change in water flow, using the Soil Moisture Accounting Procedure (SMAP) mathematical model. The water flow was simulated in the HidroLab model, resulting in the generation of hydroelectric power. The results indicated a loss of generation capacity, that can be attributed to negative anomalies of rainfall and its direct influence on river flow, which is a fundamental factor in hydropower generation. Thus, this study draws attention to the importance of considering climate vulnerability in energy planning now and in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".