Incorporating climate change risks in the environmental impact assessment of dams
Bibliographic record
Abstract
Climate change poses significant challenges, affecting communities, ecosystems, and crucial infrastructures like dams. Despite their importance in water management and energy production, dams are highly susceptible to climate change impacts due to their long lifespan. This research emphasizes the need for incorporating climate change analysis into the Environmental Impact Assessment (EIA) of new dams, including evaluating mitigation and adaptation strategies. This research reviews international guidelines and scientific literature from the past twenty years, revealing a general acknowledgment of the EIAs’ potential to address climate change risks. However, systematic studies on the actual inclusion of these risks in dam EIAs are rare. An analysis of EIA legislation, guidelines, and dam regulations in the context of three different countries - Canada, Oman, and Portugal – demonstrates that in these countries, climate change concerns are inadequately detailed in critical steps of the EIA process and adaptation measures are overlooked. The research develops an analytical model for assessing the integration of mitigation and adaptation strategies in the EIA and project development stages of three case studies. The findings suggest a need for explicit inclusion of climate change in EIAs and dam regulations to ensure that these matters are taken into consideration in the licensing process of new dams. The proposed model serves as a tool for EIA practitioners and regulators, guiding the incorporation of climate change considerations in the EIA process of dams in coordination with its development stages. Overall, this thesis aims to fill a research gap by exploring current practices, legislative frameworks, and practical guidelines, contributing to the advancement of climate-proofing of dams’ projects.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".