Abstract
In this paper, the uniform price and discriminative price methods are compared in the carbon auction market using multi-agent Q-learning. The government and different firms are considered as agents. The government as auctioneer allocates initial permits in the carbon auction market, and the firms as bidders compete with each other to obtain a larger share of the auction. The carbon trading market, penalty, reserve price, and bidding volume limitation are considered. The simulation analysis demonstrates that bidders have different behavior in two pricing methods under different amounts of carbon permits. In the uniform price, the value of bidding volume, firms’ profit, and the trading volume for low permits and the value of the government revenue, clearing price, the trading price, and auction efficiency for high permits are greater than ones in the discriminative price method. Bidding prices have a higher dispersion in the uniform price than the discriminative price method for different amounts of carbon permits.
Highlights
Reducing the amount of carbon emissions has been a major concern in many countries
The carbon permits gained of firm i in the carbon auction market for two pricing method is calculated according to the following equation:
If total emissions generated by firm i (T emissioni) exceed the total permits obtained from the carbon auction and carbon trading market (Gpermiti = gvi + nvi), it should pay the penalty for its non-compliance emissions
Summary
Reducing the amount of carbon emissions has been a major concern in many countries. To curb carbon emissions, some policies are adopted by governments [8]. Tang et al [28] design a carbon allowance auction market using the multi-agent-based model They consider two agents: the government as the regulator of emission trading scheme and different firms in all parts of China. This paper is going to compare the uniform pricing method and discriminative pricing method using the multi-agent-based model in which each adaptive agent illustrates a firm that takes part in carbon auction in a cap-and-trade scheme and determines its bid based on Q-learning. An agent can predict the long-term outcomes of its actions and the actions of other agents, and it can be able to correctly model the other agents and achieve the optimal bidding strategy [29] This algorithm has received increasing attention in the electricity auction market and has become a major tool in solving this problem [23, 24, 32, 33].
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