Water Availability as a Constraint on China's Future Growth
Bibliographic record
Abstract
Recent writings on China's water situation often portray China's water problems as severe and suggest that water availability could threaten the sustainability of China's future growth. However, China's high growth of the last 20 years or more has been obtained with relatively little increase in the physical volume of water. In this paper, we use a growth accounting approach to investigate both the contribution played in the past by water availability in constraining China's growth performance, and what would be involved in the future. We use a modified version of Solow growth accounting in which water in efficiency units enters the production technology, and investment in water management assets raises efficiency of water use. Our results suggest that if investments in water assets in the future were lower than they were in the past, growth might slightly increase by about 0.1 percentage points if non-water capital and water in efficiency units are close substitutes but growth rates could decrease by as much as 0.2-3.9 percentage points if investments in water assets were small, and if the elasticities of substitution were low. On the other hand, our experiments suggest that with faster growth of investments in water assets than in the past and a low elasticity of substitution growth rates could increase. But if non-water capital and water in efficiency units are close substitutes growth rates could even decrease, as in other cases.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".