Data to 'The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0'
Bibliographic record
Abstract
This dataset includes the AWARE2.0 characterization factors as documented in the article "The updated and improved method for water scarcity impact assessment in LCA, AWARE2.0" (DOI: 10.1111/jiec.70023). When using the dataset in your own work, please cite the article and provide reference to this zenodo repository. A Python code to reproduce AWARE2.0 on basin resolution is provided on GitHub. For importing the country-level characterization factors into LCA software, please see the files for openLCA and SimaPro below. To integrate AWARE2.0 into a brightway workflow, the edges tool (https://github.com/Laboratory-for-Energy-Systems-Analysis/edges) may be useful. Content - native resolution (monthly, watershed scale): AWARE20_Native_CFs_geospatial.gpkg: Geospatial file containing the AWARE2.0 basins as polygons with associated monthly and annual CFs AWARE20_Native_CFs_geospatial.kmz: Version of AWARE20_Native_CFs_geospatial.gpkg for GoogleEarth AWARE20_Native_CFs.xlsx: AWARE2.0 CFs on basin level (monthly and annual) and associated water consumption used for weighting AWARE20_Intermediate_Variables.xlsx: Intermediate Variables from the calculation of the AWARE2.0 CFs, such as the longterm average natural and actual water availability, the AMDs, the EFRs, etc. figures_AWARE_AWARE20_comparison_all_basins.zip: Figures comparing CFs, AMDs, Natural and Actual Availability, EWRs, and EFR coefficients between AWARE and AWARE2.0, for each of the 8149 basins individually. Consult these figures for a visual impression of how and why CFs might have changed between AWARE and AWARE2.0. - spatiotemporal aggregations: AWARE20_Countries_and_Regions.xlsx: AWARE2.0 CFs aggregated according to geography definitions of GLAM and ecoinvent (version 2.5, applicable to ecoinvent 3.10) AWARE20_Subnational_Resolution.xlsx: AWARE2.0 CFs aggregated to subnational resolution, using the GADM dataset version 4.1 (https://gadm.org/old_versions.html) AWARE20_Crop_Specific.xlsx: AWARE2.0 CFs aggregated according to geography definitions of ecoinvent (version 2.5, applicable to ecoinvent 3.10), using crop-specific irrigation water consumption for 27 crop classes as spatiotemporal weights. See readme sheet in Excel file for more information. - files for import into LCA software (based on the implementation for IMPACT World+*): AWARE2.0_for_simapro.csv: regionalized CFs for unspecified water use sectors to be imported into SimaPro† AWARE2.0_for_openLCA.zip: regionalized CFs for unspecified water use sectors to be imported into openLCA * IMPACT World+ version 2.2.1, see https://doi.org/10.5281/zenodo.18892673† SimaPro also provides a version of AWARE2.0 by default. However, that implementation is not tested by the authors of this repository. Changes: v1.0.2: added files for SimaPro and openLCA v1.0.1: addition of crop-specific spatiotemporal aggregations (AWARE20_Crop_Specific.xlsx) v1.0.0 (corresponding to published article): update of readme sheets with appropriate references to corresponding article update of reference "Müller Schmied et al. (2024)" added file: AWARE20_Subnational_Resolution.xlsx v0.0.3: use bug-fixed WaterGAP2.2e data from Sept 2023 added country and subnational aggregations changed "NoData" to "NotDefined" in the tables added gridcell pHWC to intermediate variables corrected table of water consumption data without post-processing in "Intermediate_Variables" Note: The the metadata of files are not adjusted to a new repository version number if the file content does not change in that new repository version. Caveats: Spatial CF aggregations for treaties: Due to the creation date of the data set, the BRICS aggregations in AWARE20_Countries_and_Regions.xlsx do not include the states that joined after 2023. In AWARE20_Crop_Specific.xlsx, the 10-member BRICS is labeled BRICS+.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.172 | 0.118 |
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".