Domestic and International Drivers of the Demand for Water Resources in the Context of Water Scarcity: A Cross-Country Study
Bibliographic record
Abstract
Global warming, while increasing human demand for water, is reducing water availability by reducing runoff flows and the effective amount of water between seasons, making water scarcity a growing problem globally. Water management plays an important role in mitigating global warming, improving the water cycle, reducing carbon emissions, and providing clean energy, and pricing water is considered a good approach to water management. Pricing water needs to take into account all sectors and aspects of society, such as domestic water, food and agriculture, energy, transport, industry, urban provision, human health, ecosystems, and the environment, and their interrelationships through water, within the context of the fundamental human rights to water and sanitation. This requires that every stakeholder should contribute to the development of water-related policies at every stage of the water interrelationship. This study investigated the relationship between water demand across different sectors of the economy using indicators for China, Australia, Japan, and the UK. Using panel analyses, this study finds that economic growth and population expansion increases the demand for water in all aspects. These findings have significant policy implications for water management. Because water prices can have an impact on global trade and, more importantly, are a major solution to global warming, water management policies should be considered at the global level, not only at the national level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".