Addressing water scarcity to support climate resilience and human health
Bibliographic record
Abstract
Water scarcity is projected to affect half of the world's population, gradually exacerbated by climate change. This article elaborates from a panel discussion at the 2023 United Nations Water Conference on "Addressing Water Scarcity to Achieve Climate Resilience and Human Health." Understanding and addressing water scarcity goes beyond hydrological water balances to also include societal and economic measures. We consider five categories of health impacts resulting from deteriorating water qualities and quantities: (1) water-related diseases and water for hygiene, (2) malnutrition and water for food, (3) livelihoods, income, development, and water for energy, (4) adverse air quality from drought-induced dust and wildfire smoke, and (5) mental health effects from water scarcity-related factors. A discussion on the barriers and opportunities for resilient water systems begins by reframing water scarcity as a "pathway to water bankruptcy" and introducing Water Partnerships to empower local water leaders with the awareness, education, and resources to devise and implement locally appropriate water management strategies. Other barriers include the (1) lack of tools to consider the socioeconomic implications of water scarcity, (2) lack of water information being in actionable formats for decision-makers, (3) lack of clarity in the application of water scarcity modeling to gain policy-relevant findings, and (4) inadequate drought adaptation planning. The article includes recommendations for local governments, national governments, international actors, researchers, nongovernmental organizations, and local constituents in addressing these barriers. The predominant theme in these recommendations is collaborative, multidisciplinary Water Partnerships, knowledge-sharing in accessible formats, and empowering participation by all. This article's central thesis is that addressing water scarcity must focus on people and their ability to lead healthy and productive lives.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".