Water transfer and losses embodied in the West–East electricity transmission project in China
Bibliographic record
Abstract
Electricity is an important output of the global energy system. Large amounts of water can be consumed in the process of producing electricity. This article focuses on how that water is virtually transferred from power-generating regions to electricity-consuming areas. We propose two metrics, i.e., water substitution ratio and virtual water transfer loss, to assess the efficiency of water use for power generation and virtual transmission of water through the power transmission system, respectively. These metrics are used to estimate the effects of the West–East Electricity Transmission project in China on the water resources used in power-generating regions. Results show that the electricity delivered by the project increased from 228 TWh in 2008 to 683 TWh in 2017. With the construction of wind and solar energy projects, the growth rate of virtual water was slightly slower than that of the electricity transmitted. In 2017, 2.4 km3 of virtual water was transmitted eastward. The corresponding virtual water transfer loss throughout the transmission system was approximately 100 million m3. We estimate that the virtual water footprint of the project will exceed 4.4 km3 by 2030, which may affect the sustainability of water resources and the ecological environment in western regions of China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".