An Integrated Assessment of Investments towards Global Water Sustainability
Bibliographic record
Abstract
To date there has been limited research on integrated water resource management, specifically regarding investments, from a global perspective, largely due to the complexity of the problem and to generally local water management practices. Water demand and supply are very often affected by international factors and with global climate change, population growth and increasing consumption, water management is now more than ever a global issue. This paper gives an overview of current and impending water problems while assessing investment needs for integrated water management as a possible solution to projected water challenges. The analysis compares a business as usual case (BAU) to a scenario in which investments improve water efficiency use across sectors to curb demand, increase innovative supply from desalination and enhance conventional water resources management measures. System dynamics modeling is employed to represent the structural factors influencing water demand and supply in the context of an integrated framework including cross-sectoral linkages. The analysis confirms that sustainable water management is feasible, but it requires investments in the range of $145 billion per year between 2011 and 2050 (0.16% of GDP or $17/person/year) and timely, effective action.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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".