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Water transfer and losses embodied in the West–East electricity transmission project in China

2020· article· en· W3036547320 on OpenAlexfundno aff
Yongnan Zhu, Ke Jing, Jianhua Wang, He Liu, Shan Jiang, Hélcio Blum, Yong Zhao, Guohua He, Yuan Meng, Jian Su

Bibliographic record

VenueApplied Energy · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of ChinaNational Key Research and Development Program of ChinaCanada Excellence Research Chairs, Government of CanadaU.S. Department of Energy
KeywordsVirtual waterElectricityElectricity generationWater useEnvironmental scienceWater-energy nexusElectric power transmissionEnvironmental economicsTransmission (telecommunications)Water resourcesPower transmissionEnvironmental engineeringWater transferNatural resource economicsPower (physics)Water resource managementEngineeringWater scarcityTelecommunicationsEconomicsEcologyElectrical engineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.195
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations55
Published2020
Admission routes1
Has abstractyes

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