Characterization Factors for Water Consumption and Greenhouse Gas Emissions Based on Freshwater Fish Species Extinction
Bibliographic record
Abstract
Human-induced changes in water consumption and global warming are likely to reduce the species richness of freshwater ecosystems. So far, these impacts have not been addressed in the context of life cycle assessment (LCA). Here, we derived characterization factors for water consumption and global warming based on freshwater fish species loss. Calculation of characterization factors for potential freshwater fish losses from water consumption were estimated using a generic species-river discharge curve for 214 global river basins. We also derived characterization factors for potential freshwater fish species losses per unit of greenhouse gas emission. Based on five global climate scenarios, characterization factors for 63 greenhouse gas emissions were calculated. Depending on the river considered, characterization factors for water consumption can differ up to 3 orders of magnitude. Characterization factors for greenhouse gas emissions can vary up to 5 orders of magnitude, depending on the atmospheric residence time and radiative forcing efficiency of greenhouse gas emissions. An emission of 1 ton of CO₂ is expected to cause the same impact on potential fish species disappearance as the water consumption of 10-1000 m³, depending on the river basin considered. Our results make it possible to compare the impact of water consumption with greenhouse gas emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".