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Record W2732841989 · doi:10.1002/2017wr020640

Water security, risk, and economic growth: Insights from a dynamical systems model

2017· article· en· W2732841989 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Resources Research · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaDepartment for International DevelopmentNatural Environment Research CouncilSight Research UK
KeywordsPoverty trapInvestment (military)Natural resource economicsWater securityPovertyProductivityEconomicsContext (archaeology)BusinessWater resourcesCapital (architecture)Economic growthGeography

Abstract

fetched live from OpenAlex

Abstract Investments in the physical infrastructure, human capital, and institutions needed for water resources management have been noteworthy in the development of most civilizations. These investments affect the economy in two distinct ways: (i) by improving the factor productivity of water in multiple economic sectors, especially those that are water intensive such as agriculture and energy and (ii) by reducing acute and chronic harmful effects of water‐related hazards like floods, droughts, and water‐related diseases. The need for capital investment to mitigate risks and promote economic growth is widely acknowledged, but prior conceptual work on the relationship between water‐related investments and economic growth has focused on the productive and harmful roles of water in the economy independently. Here the two influences are combined using a simple, dynamical systems model of water‐related investment, risk, and growth. In cases where initial water security is low, initial investment in water‐related assets enables growth. Without such investment, losses due to water‐related hazards exert a drag on economic growth and may create a poverty trap. The presence and location of the poverty trap is context‐specific and depends on the exposure of productive water‐related assets to water‐related risk. Exogenous changes in water‐related risk can potentially push an economy away from a growth path toward a poverty trap. Our investigation shows that an inverted‐U‐shaped investment relation between the level of investment in water security and the current level of water security leads to faster rates of growth than the alternatives that we consider here, and that this relation is responsible for the “S”‐curve that is posited in the literature. These results illustrate the importance of accounting for environmental and health risks in economic models and offer insights for the design of robust policies for investment in water‐related productive assets to manage risk, in the face of environmental change.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.001
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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