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Record W4406308826 · doi:10.1093/inteam/vjaf001

Addressing water scarcity to support climate resilience and human health

2025· article· en· W4406308826 on OpenAlexafffund
Karl Zimmermann, Azar M. Abadi, Kate A. Brauman, Josefina Maestu, Gualbert Oude Essink, Corinne J. Schuster‐Wallace, Ryan Smith, Kaveh Madani, Zafar Adeel, Matthew O. Gribble

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2025
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsUnited Nations University Institute for Water, Environment, and HealthGlobal Institute for Water SecurityUniversity of SaskatchewanUniversity of British Columbia
FundersNational Institute of Environmental Health SciencesSimon Fraser UniversityUniversity of Alabama
KeywordsWater scarcityScarcityWater resourcesBusinessEnvironmental planningNatural resource economicsWater securityClimate changeEnvironmental resource managementGeographyEnvironmental scienceEconomicsEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0060.007
Open science0.0020.015
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.013
GPT teacher head0.273
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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