The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE)
Bibliographic record
Abstract
Life cycle assessment (LCA) has been used to assess freshwater-related impacts according to a new water footprint framework formalized in the ISO 14046 standard. To date, no consensus-based approach exists for applying this standard and results are not always comparable when different scarcity or stress indicators are used for characterization of impacts. This paper presents the outcome of a 2-year consensus building process by the Water Use in Life Cycle Assessment (WULCA), a working group of the UNEP-SETAC Life Cycle Initiative, on a water scarcity midpoint method for use in LCA and for water scarcity footprint assessments. In the previous work, the question to be answered was identified and different expert workshops around the world led to three different proposals. After eliminating one proposal showing low relevance for the question to be answered, the remaining two were evaluated against four criteria: stakeholder acceptance, robustness with closed basins, main normative choice, and physical meaning. The recommended method, AWARE, is based on the quantification of the relative available water remaining per area once the demand of humans and aquatic ecosystems has been met, answering the question “What is the potential to deprive another user (human or ecosystem) when consuming water in this area?” The resulting characterization factor (CF) ranges between 0.1 and 100 and can be used to calculate water scarcity footprints as defined in the ISO standard. After 8 years of development on water use impact assessment methods, and 2 years of consensus building, this method represents the state of the art of the current knowledge on how to assess potential impacts from water use in LCA, assessing both human and ecosystem users’ potential deprivation, at the midpoint level, and provides a consensus-based methodology for the calculation of a water scarcity footprint as per ISO 14046.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".