Review on Governance in Hydroelectric Projects and Impacts on Natural Resources
Bibliographic record
Abstract
This article reviewed the literature to highlight how governance in hydroelectric enterprises is configured and the impacts on natural resources resulting from this type of energy production. The methodological procedures were based on the PRISMA recommendation (Main Items for Reporting Systematic Reviews and Meta-analysis) and indexed articles were used from the SCOPUS database. It was evidenced that more than 1/3 of the studies were conducted in Brazil, highlighting the potential of the Amazon region of the country for the construction of hydroelectric dams. Four essential subjects were identified as to be observed by the governance of these enterprises: stakeholder participation, habitat fragmentation, social impacts, and impacts on fish species. These subjects constitute three categories that synthesize governance in hydroelectric projects and the impacts on natural resources: energy policies – which generate benefits for the private sector and contemplate very little the local communities and the environment; water resources and fish - the impacts are related to the type of enterprise to be built, which can compromise the migration and reproduction of fish, in addition to the increasing concentration of nutrients in reservoirs and changes of the water quality; and biodiversity and ecosystem – which are affected by the fragmentation and alteration of natural habitats caused by dam floods.
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 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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".