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Water quality trading with asymmetric information, uncertainty and transaction costs: A stochastic agent-based simulation

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  • Nguyen, N.P.
  • Shortle, J.S.
  • Reed, P.M.
  • Nguyen, T.T.

Abstract

We examine the efficiency of emissions trading in bilateral and clearinghouse markets with heterogeneous, boundedly rational agents making decisions under imperfect and asymmetric information, and transaction costs. Results are derived using a stochastic agent-based simulation model of agents’ decision-making and interactions. Trading rules, market structures, and agent information structures are selected to represent emerging water quality trading programs. The analysis is designed to provide a strong test of the efficiency of trading occurring through the two market structures. The Differential Evolution algorithm is used to search for market trade strategies that perform well under multiple states of the world. Our findings suggest that trading under both bilateral and clearinghouse markets yields cost savings relatively to no trading. The clearinghouse is found to be more efficient than bilateral negotiations in coordinating point–nonpoint trading under uncertainty and transaction costs. However, the market under both structures is unlikely to achieve or even approximate least-cost pollution control allocations. Expectations of gains from water quality trading should, therefore, be tempered.

Suggested Citation

  • Nguyen, N.P. & Shortle, J.S. & Reed, P.M. & Nguyen, T.T., 2013. "Water quality trading with asymmetric information, uncertainty and transaction costs: A stochastic agent-based simulation," Resource and Energy Economics, Elsevier, vol. 35(1), pages 60-90.
    • Handle: RePEc:eee:resene:v:35:y:2013:i:1:p:60-90
    DOI: 10.1016/j.reseneeco.2012.09.002
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    References listed on IDEAS

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    3. Liang Liu & Cong Feng & Hongwei Zhang & Xuehua Zhang, 2015. "Game Analysis and Simulation of the River Basin Sustainable Development Strategy Integrating Water Emission Trading," Sustainability, MDPI, vol. 7(5), pages 1-21, April.
    4. Basaure, Arturo & Suomi, Henna & Hämmäinen, Heikki, 2014. "Effects of transaction and switching costs on mobile market performance," 20th ITS Biennial Conference, Rio de Janeiro 2014: The Net and the Internet - Emerging Markets and Policies 106830, International Telecommunications Society (ITS).
    5. Shortle, James, 2013. "Economics and Environmental Markets: Lessons from Water-Quality Trading," Agricultural and Resource Economics Review, Cambridge University Press, vol. 42(1), pages 57-74, April.
    6. Wang, Zhiyu, 2018. "Permit trading with flow pollution and stock pollution," Journal of Environmental Economics and Management, Elsevier, vol. 91(C), pages 118-132.
    7. Drechsler, Martin & Wätzold, Frank & Grimm, Volker, 2022. "The hitchhiker's guide to generic ecological-economic modelling of land-use-based biodiversity conservation policies," Ecological Modelling, Elsevier, vol. 465(C).
    8. James Shortle & Richard D. Horan, 2017. "Nutrient Pollution: A Wicked Challenge for Economic Instruments," Water Economics and Policy (WEP), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 1-39, April.
    9. Johanna Kangas & Markku Ollikainen, 2023. "Behavioural and Welfare Analysis of an Intermediary in Biodiversity Offset Markets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 1127-1154, April.
    10. Anbari, Mohammad Javad & Zarghami, Mahdi & Nadiri, Ata-Allah, 2021. "An uncertain agent-based model for socio-ecological simulation of groundwater use in irrigation: A case study of Lake Urmia Basin, Iran," Agricultural Water Management, Elsevier, vol. 249(C).
    11. Jeong, Dawoon & Sesmero, Juan Pablo & Reeling, Carson, 2023. "Experimental Evidence of Efficiency and Equity of Posted Price Markets for Irrigation Water," 2023 Annual Meeting, July 23-25, Washington D.C. 335840, Agricultural and Applied Economics Association.
    12. Duke, Joshua M. & Liu, Hongxing & Monteith, Tyler & McGrath, Joshua & Fiorellino, Nicole M., 2020. "A method for predicting participation in a performance-based water quality trading program," Ecological Economics, Elsevier, vol. 177(C).
    13. James Shortle & Richard D. Horan, 2013. "Policy Instruments for Water Quality Protection," Annual Review of Resource Economics, Annual Reviews, vol. 5(1), pages 111-138, June.

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    More about this item

    Keywords

    Water quality trading; Agent-based model; Environmental markets; Bilateral negotiations; Clearinghouse; Transaction costs; Asymmetric information; Uncertainty; Bounded rationality; Differential Evolutionary algorithm;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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