Keywords
water market, water rights trade, transaction costs, trading water, hydro-economic modelling
Start Date
1-7-2012 12:00 AM
Abstract
In many catchments in England no further licenses are available from the Environment Agency (EA). The possibility of trading water between license holders has been recognized as a potentially effective and economically efficient strategy to mitigate increasing scarcity. However it is not clear what potential trading has to meaningfully address the supply-demand imbalance in over-abstracted areas. A screening tool that could assess the potential and effectiveness of water trading in any catchment would be useful. We propose an optimization-driven water market simulator that predicts economically efficient pair-wise trade and represents its interaction with natural flows and engineered infrastructure. The model emulates license-holders’ willingness to engage in short-term trade transactions. In their initial form different ‘agents’ (license holders) are represented using an economic benefit function of water use. The working hypothesis is that trading behavior can be partially predicted based on differences in marginal values of water over space and time. A case study based on the river Dove Basin (UK) is made to test the model. The model which simulates the catchment weekly over several years can also consider user interactions with infrastructure (e.g. reservoirs) and user-defined transaction costs between user-types or specific license holder pairs.
An agent model to simulate water markets
In many catchments in England no further licenses are available from the Environment Agency (EA). The possibility of trading water between license holders has been recognized as a potentially effective and economically efficient strategy to mitigate increasing scarcity. However it is not clear what potential trading has to meaningfully address the supply-demand imbalance in over-abstracted areas. A screening tool that could assess the potential and effectiveness of water trading in any catchment would be useful. We propose an optimization-driven water market simulator that predicts economically efficient pair-wise trade and represents its interaction with natural flows and engineered infrastructure. The model emulates license-holders’ willingness to engage in short-term trade transactions. In their initial form different ‘agents’ (license holders) are represented using an economic benefit function of water use. The working hypothesis is that trading behavior can be partially predicted based on differences in marginal values of water over space and time. A case study based on the river Dove Basin (UK) is made to test the model. The model which simulates the catchment weekly over several years can also consider user interactions with infrastructure (e.g. reservoirs) and user-defined transaction costs between user-types or specific license holder pairs.
