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A Deterministic Smart Market Model for Groundwater

Author

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  • John F. Raffensperger

    (Department of Management, University of Canterbury, Christchurch, New Zealand)

  • Mark W. Milke

    (Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand)

  • E. Grant Read

    (Department of Management, University of Canterbury, Christchurch, New Zealand)

Abstract

Efficient management of water requires balancing environmental needs, externality considerations, and economic efficiency. Toward that end, this paper presents a deterministic linear program that could be used to operate a smart spot market for groundwater. The market design uses the existing hydrological programs MODFLOW and GWM along with standard linear programming methods. In principle, a market could be set up anywhere that a MODFLOW model is available. The market design has parallels to markets in the electricity and gas sectors, which we discuss. We present a case study with notional bids for Marlborough, New Zealand. Our approach would reduce transaction costs for a water market, reduce users' risk, and increase the reliability of environmental flows. We discuss a number of cautions and limitations to the model and recommend further work on introducing a stochastic framework to the model.

Suggested Citation

  • John F. Raffensperger & Mark W. Milke & E. Grant Read, 2009. "A Deterministic Smart Market Model for Groundwater," Operations Research, INFORMS, vol. 57(6), pages 1333-1346, December.
    • Handle: RePEc:inm:oropre:v:57:y:2009:i:6:p:1333-1346
    DOI: 10.1287/opre.1090.0730
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    References listed on IDEAS

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    1. Murphy, James J. & Dinar, Ariel & Howitt, Richard E. & Rassenti, Stephen J. & Smith, Vernon L. & Weinberg, Marca, 2004. "Incorporating Instream Flow Values Into A Water Market," Working Paper Series 14525, University of Massachusetts, Amherst, Department of Resource Economics.
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    7. Easter, K William & Rosegrant, Mark W & Dinar, Ariel, 1999. "Formal and Informal Markets for Water: Institutions, Performance, and Constraints," The World Bank Research Observer, World Bank, vol. 14(1), pages 99-116, February.
    8. Bidwell, V.J., 2005. "Realistic forecasting of groundwater level, based on the eigenstructure of aquifer dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 12-20.
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    Cited by:

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    2. Filippelli, Raphael & Termansen, Mette & Hasan, Syezlin & Hasler, Berit & Hansen, Line & Smart, James C.R., 2022. "Water quality trading markets – Integrating land and marine based measures under a smart market approach," Ecological Economics, Elsevier, vol. 200(C).
    3. Zhiling Guo & Gary J. Koehler & Andrew B. Whinston, 2012. "A Computational Analysis of Bundle Trading Markets Design for Distributed Resource Allocation," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 823-843, September.
    4. Tajbakhsh, Alireza & Hassini, Elkafi, 2022. "A game-theoretic approach for pollution control initiatives," International Journal of Production Economics, Elsevier, vol. 254(C).
    5. Raffensperger, John F., 2011. "Matching users' rights to available groundwater," Ecological Economics, Elsevier, vol. 70(6), pages 1041-1050, April.
    6. Ellen M. Bruno & Richard J. Sexton, 2020. "The Gains from Agricultural Groundwater Trade and the Potential for Market Power: Theory and Application," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(3), pages 884-910, May.
    7. Biggar, Darryl R. & Hesamzadeh, Mohammad Reza, 2022. "Welfare-maximising dispatch and pricing of water in a gravity-fed river network," Energy Economics, Elsevier, vol. 108(C).
    8. Syezlin Hasan & Line Block Hansen & James C. R. Smart & Berit Hasler & Mette Termansen, 2022. "Tradeable Nitrogen Abatement Practices for Diffuse Agricultural Emissions: A ‘Smart Market’ Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(1), pages 29-63, May.

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