Integrating Economic and Ecological Benchmarking for a Sustainable Development of Hydropower
Bibliographic record
Abstract
Hydropower reservoirs play an increasingly important role for the global electricity supply. Reservoirs are anthropogenically-dominated ecosystems because hydropower operations induce artificial water level fluctuations (WLF) that exceed natural fluctuations in frequency and amplitude. These WLF have detrimental ecological effects, which can be quantified as losses to ecosystem primary production due to lake bottoms that fall dry. To allow for a sustainable development of hydropower, these “ecological costs” of WLF need to be weighed against the “economic benefits” of hydropower that can balance and store intermittent renewable energy. We designed an economic hydropower operation model to derive WLF in large and small reservoirs for three different future energy market scenarios and quantified the according losses in ecosystem primary production in semi-natural outdoor experiments. Our results show that variations in market conditions affect WLF differently in small and large hydropower reservoirs and that increasing price volatility magnified WLF and reduced primary production. Our model allows an assessment of the trade-off between the objectives of preserving environmental resources and economic development, which lies at the core of emerging sustainability issues.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".