Telecoupling China’s City-Level Water Withdrawal with Distant Consumption
Bibliographic record
Abstract
Trade causes the geospatial separation of production and consumption, which drives telecoupling between resource utilization and distant consumption. While benefiting the economy, trade can also exacerbate resource use inequality among regions. Here, we propose telecoupled water withdrawal (TWW) to examine the impact of distant consumption on local water resources, defined as local water withdrawal driven remotely by consumption in other regions. We characterize the TWW in China in 2015 at the city level (350 cities) using a nested multiregional input-output model. We find that approximately 20% of TWW is linked to foreign consumption, more than a quarter of which is driven by the United States. Moreover, cities with high TWW are concentrated on the Southeast coast and in Northwest China. Cities in Xinjiang province (Northwest China) account for 12% of the national TWW but only 0.7% of China's GDP. Our findings shed light on the telecoupling of pressure on local water resources in China cities and distant consumption at the global scale, calling for joint efforts by the contributors and beneficiaries of TWW to ensure the synergistic sustainability of water resources and trade.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".