Water footprint assessment considering intermediate products: model and a 2016 case study of China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Analysing intermediate products within a water footprint (WF) across different economic sectors can show the root causes of water usage and is helpful for water resource management and policy making. However, conventional methods and data for a WF rarely assess the input and output of intermediate products directly and comprehensively. Therefore, this study proposes an approach to access the WF of intermediate products as well as final products in each sector of an economy’s water sustainability profile. An Economic Input‐output‐based Life‐Cycle Assessment (EIO‐LCA) framework is designed for the accounting, which describes the intermediate WF products of each sector in a material‐product network. This method is implemented into a 2016 case study for a comprehensive Chinese WF. The results showed that the total WF of Chinese inhabitants (consumers) in 2016 was 5.76 × 10 11 m 3 , and the top three sectors with the largest WF were agriculture (1.78 × 10 11 m 3 ), food (1.05 × 10 11 m 3 ) and machinery manufacturing (5.68 × 10 10 m 3 ); agriculture provided the largest quantity of virtual water contained in its intermediate product for the other sectors. From the perspective of producers, the total WF of the Chinese economic sectors in 2016 was 5.84 × 10 11 m 3 . The sectors with the largest direct water use were agriculture (2.20 × 10 11 m 3 ), electricity (7.64 × 10 10 m 3 ) and chemical Industry (2.35 × 10 10 m 3 ); and large parts of their direct water usage were consumed to prepare intermediate products for other sectors. The results of this study show that a more inclusive approach provides an enhanced qualitative and resource‐ethical view for water accounting and management.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it