The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0
Bibliographic record
Abstract
Abstract Supported by the Life Cycle Initiative, in 2018, the Water Use in Life Cycle Assessment (WULCA) working group published Available Water Remaining (AWARE), a consensus‐based method for water scarcity impact assessment. This article presents AWARE2.0, an update based on new data and an improved calculation process and recommended by the authors of the original AWARE publication. Water availability for 1990–2019 and the global water consumption inventory of 2019 are modeled with the global hydrological model WaterGAP2.2e. AWARE2.0 refines the calculations for river deltas, inland sinks, and subdivided river basins and furthermore benefits from an improved representation of basin area, increased responsiveness of environmental water requirements to seasonal flow patterns, and a more appropriate water consumption definition. This work analyses differences between AWARE and AWARE2.0 and the influence of the improvements on the characterization factors (CFs). The update is relevant to life cycle assessment, since more than half of the water consumption inventory is linked to CFs changing by more than 10%. Globally relevant changes mainly result from the new input data including the temporal reference period, whereas other improvements target individual types of basins, sometimes changing their CFs by two orders of magnitude. The AWARE2.0 CFs are provided for 9406 basins and the country definitions of ecoinvent and GLAM. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges .
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".