Water resources management in Beijing using economic input–output modeling
Bibliographic record
Abstract
To support more sustainable development of a region, decision support tools must consider local and global systems level impacts on the economy, environment, and society. Through the development and application of "economic input–output water resources" models for Beijing, China for the years 1985, 1990, and 1992, historical trends related to the economy structure and its water use are investigated. The study finds that the economy of Beijing and water use are highly concentrated in agriculture and heavy industry, but this intensive water use is indirectly reflected in the production of most other goods and services throughout the economy because of the interrelationships among various sectors of the economy. In spite of progress during the time period observed (e.g., between 1985 and 1990 the output of the economy doubled, but water consumption increased only 12% partly because of a significant increase in the price of water in 1988) and given the seriousness of water resources issues in the region, it is critical that future regional development make progress toward a more water-efficient economic system.Key words: water resources planning, urban water management, input–output modeling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".