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Record W2112782608 · doi:10.3390/w2040726

An Integrated Assessment of Investments towards Global Water Sustainability

2010· article· en· W2112782608 on OpenAlexaff
Andrea M. Bassi, Zhuohua Tan, Sophie Goss

Bibliographic record

VenueWater · 2010
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcGill University
FundersWorld Bank Group
KeywordsSustainabilityIntegrated water resources managementContext (archaeology)BusinessWater supplyWater resourcesDemand managementNatural resource economicsEnvironmental economicsPopulationInvestment (military)Environmental resource managementEnvironmental scienceEconomicsEnvironmental engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.242
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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