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. For importing the country-level characterization factors into LCA software, please see the AWARE2.0 implementations (openLCA, SimaPro, brightway2) in IMPACT World+, version 2.1: https://doi.org/10.5281/zenodo.14041258 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. Changes: 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" 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 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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".