Development of Method for Assessing Water Footprint Sustainability
Bibliographic record
Abstract
Large scale production of water-intensive industrial products can intensify water scarcity, resulting in potential unsustainable water use at local and regional scales. This study proposes a methodological framework for assessing the WF sustainability of multiple interdependent products in a system, and one of China’s four major large modern coal chemical industry bases is used as a case study. A Mixed-Unit Input-Output (MUIO) model was applied to calculate the blue water footprint (WF) for 19 major coal-based energy and chemicals in the study area, based on which the WF sustainability of production of the products were assessed using different indicators. Technical coefficient matrix and direct water consumption vector of the products were constructed based a database that were built by field research in the study area. Accounting result indicates that the blue WF of the coal-based products range from 2.5 × 10−4 m3/kWh for coal-fired power to 55.25 m3/t for Polytetrahydrofuran. The sustainability assessment reveals that the blue WF of all products produced in the study area are sustainable at both product and regional levels, while over half of them have reached the advanced level. However, the blue WF of a few products with large production capacities has just crossed the sustainable thresholds, posing potential threat to the local environment. This paper concludes with a discussion on the choice of blue WF accounting approach, methods to promote WF sustainability of coal-based products, and suggestions for the WF management in general.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".