Water security, risk, and economic growth: Insights from a dynamical systems model
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".