Abstract
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.
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Bell, R., Beare, S. (2002). Emulating Trade in Emissions Permits: An Application of Genetic Algorithms. In: Chen, SH. (eds) Evolutionary Computation in Economics and Finance. Studies in Fuzziness and Soft Computing, vol 100. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1784-3_9
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DOI: https://doi.org/10.1007/978-3-7908-1784-3_9
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-2512-1
Online ISBN: 978-3-7908-1784-3
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