Water Shortages, Water Allocation and Economic Growth: The Case of China
Bibliographic record
Abstract
Current projections indicate that by 2025, water scarcity will affect over one quarter of the world’s population. This suggests that the need to manage water more efficiently will become more pressing during the next few years as the demand for water increases along with the expansion of economies and their populations. This paper investigates the economic impacts of efficient intraregional and/or inter-regional water reallocation, and examines their corresponding economic gains. A Ramsey-type growth model of a small, open, competitive economy is fitted to year 2000 Chinese data andthe empirical model is used to perform policy experiments. Within region water reallocation increases per-capita Chinese GDP by about 1.5% per year over the period 2000-2060. The aggregate potential welfare gain due to this reallocation is 1002.51 billion RMB. Transferring water from southern to northern China via the South-North Water Transfer Project, on average, has a smaller impact on per-capita GDP over the period 2000-2060, with an aggregate welfare gain of 557.23 billion RMB. Combining intra-regional and inter-regional water reallocations, on average, increases per-capita GDP by 0.38% per year over the period and the aggregate welfare gain from this combination is 1148.06 billion RMB.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".