Domestic Groundwater Depletion Supports China's Full Supply Chains
Bibliographic record
Abstract
Abstract Groundwater use underpins much economic production. The unsustainable use of groundwater threatens environmental flows in surface waters, sustainable development, and future food security. The connection between agricultural trade and groundwater depletion has been recently highlighted, but how groundwater depletion supports the production of industrial and tertiary goods, trade, and consumption remains less well understood. Here, we present the first analysis of groundwater use and depletion embedded in the complete supply chain of China (including primary, secondary, and tertiary products). We use a multiregion input‐output analysis coupled with the high‐resolution groundwater use modeling to track groundwater depletion from production to end consumer. Our modeling results show that groundwater depletion occurred primarily in water scarce North China for agricultural production, but the depleted resource was then incorporated throughout the supply chain and dispersed across Chinese and international consumers. ∼64 billion m 3 yr −1 (±1 billion m 3 yr −1 ) groundwater was depleted in China, in which more than a half was from Xinjiang, Hebei, Henan and Heilongjiang Provinces. Approximately 40% of the groundwater depletion can be traced to interprovincial transfer (21 billion m 3 yr −1 ) and export (4.8 billion m 3 yr −1 ). The hot spots for final consumption of groundwater depletion were major cities in both North and South China. Importantly, over 60% of the groundwater depletion was embodied in industrial and tertiary products for final consumption, highlighting the importance of tracing groundwater through the full economy. Groundwater depletion represents a long‐term risk to supply chains, and policy‐makers can use this understanding to sustainably manage groundwater and diversify supply chains.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".