The economics of managing water crises

The economics of managing water crises

Publication Year:
Barbier, Edward B.
Resource Type:
Journal Article
Several examples of how the increasing environmental and social costs associated with freshwater scarcity are not routinely reflected in markets are explored to illustrate the economic challenge of the growing risk of water crises.
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Resource Information


The growing risk of water crises, including drought, is one of the greatest challenges in the coming decades. Averting such crises will be especially daunting, given that they are just as much a failure of water management as they are a result of scarcity. A major shortcoming is the persistent underpricing of water. The increasing environmental and social costs associated with freshwater scarcity are not routinely reflected in markets. Nor have we developed adequate policies and institutions to handle these costs. This creates perverse incentives that fail to balance water exttraction with supply, protect freshwater ecosystems and generate water-saving innovations. However, drought is proving to be a catalyst for governance and policy reform, and steps can be taken to overcome the underpricing of water. Several examples are explored to illustrate the economic challenge. They include removing the barriers to water markets and trading, reallocating subsidies for water supply and sanitation to expand delivery in developing countries and reforming environmentally harmful irrigation and agricultural policies. The article also explains how ending underpricing can foster a comprehensive strategy for water-saving innovation that can ‘bend’ the global water use curve.

Resource Type

Journal Article

Publication Year



Barbier, Edward B.



University Affiliation

Colorado State University

Business Connect Takeaways

The article discusses the potential of using artificial intelligence (AI) to improve the accuracy and efficiency of climate models, which are used to predict future climate patterns and inform policy decisions. The authors argue that AI can help address some of the limitations of traditional climate models, such as their reliance on simplified assumptions and limited data inputs.
The authors review several examples of how AI has been used to improve climate modeling, including the development of machine learning algorithms that can analyze large datasets and identify patterns and trends that may be missed by traditional models. The authors also discuss the potential of AI to improve the accuracy of weather forecasting and to help identify areas that are most vulnerable to climate change impacts.
The article highlights the importance of continued research and development in the field of AI and climate modeling, as well as the need for collaboration between scientists, policymakers, and other stakeholders to ensure that these tools are used effectively to address the challenges of climate change. The authors suggest that AI can help provide more accurate and timely information to decision-makers, which can in turn lead to more effective policies and interventions to mitigate and adapt to climate change.

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