Review of methods addressing freshwater use in life cycle inventory and impact assessment
Bibliographic record
Abstract
In recent years, several methods have been developed which propose different freshwater use inventory schemes and impact assessment characterization models considering various cause–effect chain relationships. This work reviewed a multitude of methods and indicators for freshwater use potentially applicable in life cycle assessment (LCA). This review is used as a basis to identify the key elements to build a scientific consensus for operational characterization methods for LCA. This evaluation builds on the criteria and procedure developed within the International Reference Life Cycle Data System Handbook and has been adapted for the purpose of this project. It therefore includes (1) description of relevant cause–effect chains, (2) definition of criteria to evaluate the existing methods, (3) development of sub-criteria specific to freshwater use, and (4) description and review of existing methods addressing freshwater in LCA. No single method is available which comprehensively describes all potential impacts derived from freshwater use. However, this review highlights several key findings to design a characterization method encompassing all the impact pathways of the assessment of freshwater use and consumption in life cycle assessment framework as the following: (1) in most of databases and methods, consistent freshwater balances are not reported either because output is not considered or because polluted freshwater is recalculated based on a critical dilution approach; (2) at the midpoint level, most methods are related to water scarcity index and correspond to the methodological choice of an indicator simplified in terms of the number of parameters (scarcity) and freshwater uses (freshwater consumption or freshwater withdrawal) considered. More comprehensive scarcity indices distinguish different freshwater types and functionalities. (3) At the endpoint level, several methods already exist which report results in units compatible with traditional human health and ecosystem quality damage and cover various cause–effect chains, e.g., the decrease of terrestrial biodiversity due to freshwater consumption. (4) Midpoint and endpoint indicators have various levels of spatial differentiation, i.e., generic factors with no differentiation at all, or country, watershed, and grid cell differentiation. Existing databases should be (1) completed with input and output freshwater flow differentiated according to water types based on its origin (surface water, groundwater, and precipitation water stored as soil moisture), (2) regionalized, and (3) if possible, characterized with a set of quality parameters. The assessment of impacts related to freshwater use is possible by assembling methods in a comprehensive methodology to characterize each use adequately.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".