Emulating Trade in Emissions Permits: An Application of Genetic Algorithms

  • Rosalyn Bell
  • Stephen Beare
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 100)


Emissions permits are generally a second best option for dealing with site specific pollution. The outcome of trade in emissions permits when the economic welfare of market participants is linked spatially through production externalities is unclear. Trade will reflect the interaction of bargaining agents whose incentives vary with the relative physical location of both the buyer and seller. For the permit system to internalise the costs of pollution, information on who are the current buyers and sellers is necessary. This information corresponds to an understanding of the economic impacts of the physical externality, which in turn allows an improvement in the level and distribution of resource access or use. However, provision of such information is not characteristic of a competitive market in which rents associated with reducing the net cost of an externality are competed away. To achieve a more efficient distribution of entitlements, through the internalisation of pollution costs, the market structure must allow agents to capture these rents.

A genetic algorithm is used to emulate trading behaviour of individual agents for emission entitlements. Agents are assumed to operate independently, with each attempting to find their own optimal combination of inputs and emissions permits. The agents are linked in a simulation model through market outcomes and through a spatially dependent production externality. The process being modelled is essentially a non- cooperative evolutionary game. Agents learn that their best bidding strategy is not independent of the strategies of other market participants. In particular, the value of a permit depends on both the price and quantity of permits bought and sold by other market participants. The model is used to examine the effectiveness of emission permit schemes given a range of different market structures and trading strategies employed by market participants. The results suggest an effective emissions permit scheme may require institutional arrangements that preserve market power as opposed to atomistic competition.


Market Participant Trading Strategy Tradable Emission Scheme Permit Price Emission Permit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alemdar N., Ozyildirim S., (1998) A Genetic Game of Trade, Growth and Externalities. Journal of Economic Dynamics and Control 22, 811–832MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Atkinson S., Tietenberg T., (1987) Economic Implications of Emissions Trading Rules for Local and Regional Pollutants. Canadian Journal of Econom. 20, 370–86CrossRefGoogle Scholar
  3. 3.
    Beare S., Bell R., Fisher B. S. (1998) Determining the Value of Water: The Role of Risk. Infrastructure Constraints and Ownership. American Journal of Agriculture Economics 80, DecemberGoogle Scholar
  4. 4.
    Birchenhall C. (1995) Modular Technical Change in Genetic Algorithms, Computational Economics 8, 233–53MATHCrossRefGoogle Scholar
  5. 5.
    Bullard J., Duffy J. (1998) A Model of Learning and Emulation with Artificial Adaptive Agents. Journal of Economic Dynamics and Control, 22, 179–207MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chen S -H., Yeh C -H. (1997) Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming. Journal of Economic Dynamics and Control 21, 1043–1063MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Demetz H. (1969) Information and Efficiency: Another Viewpoint. Journal of Law and Economics 11, 1–22CrossRefGoogle Scholar
  8. 8.
    Goldberg D. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley Publishing Company, USAGoogle Scholar
  9. 9.
    Green J. (1977) The Non-existence of Informational Equilibria, Review of Economic Studies 44, 451–63MATHCrossRefGoogle Scholar
  10. 10.
    Hanley N., Shogren J., White B. (1997) Environmental Economics in Theory and Practice. MacMillan Press LtdGoogle Scholar
  11. 11.
    Heaney A., Beare S., Bell R. (2001) Evaluating Improvements in Water Use Efficiency as a Salinity Mitigation Option in the South Australian Mallee Areas. 45th Annual Conference of the Australian Agricultural Economics Society, Adelaide, January 21–25, 2001Google Scholar
  12. 12.
    Holland J. (1997) Adaptation in Natural and Artificial Systems. University of Michigan Press.Google Scholar
  13. 13.
    Imagine That Inc. (1997) Extend User’s Manual Version 4Google Scholar
  14. 14.
    Marks R. (1999) Breeding Hybrid Strategies: Optimal Behaviour for Oligopolists. Draft discussion paper.Google Scholar
  15. 15.
    Milgrom P. (1989) Auctions and Bidding: A Primer. Journal of Economic Perspectives 3, 3–22CrossRefGoogle Scholar
  16. 16.
    Pesendorfer W., Swinkels J. (1997) The Loser’s Curse and Information Aggregation in Common Value Auctions. Econometrica 65, 1247–1281MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Riechmann T. (1999) Learning and Behavioral Stability: An Economic Interpretation of Genetic Algorithms. Journal of Evolutionary Economics 9, 225–242CrossRefGoogle Scholar
  18. 18.
    Weibull J. (1996) Evolutionary Game Theory, MIT PressGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Rosalyn Bell
    • 1
  • Stephen Beare
    • 1
  1. 1.Australian Bureau of Agricultural and Resource EconomicsAustralia

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