Technology Adoption Optimization with Heterogeneous Agents and Carbon Emission Trading Mechanism

Fang, C. & Ma, T. (2018). Technology Adoption Optimization with Heterogeneous Agents and Carbon Emission Trading Mechanism. In: Integrated Uncertainty in Knowledge Modelling and Decision Making. Eds. Huynh, V.N., Inuiguchi, M., Tran, D., & Denoeux, T., pp. 238-249 Cham, Switzerland: Springer. ISBN 978-3-319-75429-1 10.1007/978-3-319-75429-1_20.

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The adoption of new technologies with high efficiency and low emission is of great importance in achieving sustainable development. Most studies of technology adoption have been criticized of idealistically assuming only one global decision agent. In this paper, a model of optimizing technology adoption with heterogeneous agents is proposed. Each agent attempts to identify the optimal solution for a portion of the entire system. The heterogeneity in agents is the different demands they face. In order to internalize the external effects of emission, a quantity-based market incentive policy instruments - Carbon Emission Trading is implemented. With two heterogeneous agents, a bargaining process is introduced to reasonably allocate the profit to them. Computational tests are conducted with different market shares and different discounting factors. Numerical results show the impact of heterogeneity and carbon emission trading mechanism on the optimal technology adoptions. It is suggested that a smaller gap of agents’ market shares leads to earlier and more adoptions. Besides, adoptions remain no change when both agents have a same discounting factor. A big discounting factor of the seller will accelerate the adoptions in the buyer agent and the entire system if agents have different discounting factors.

Item Type: Book Section
Uncontrolled Keywords: Technology adoption, Heterogeneous agents, Carbon emission trading, Bargaining process
Research Programs: Transitions to New Technologies (TNT)
Depositing User: Luke Kirwan
Date Deposited: 26 Mar 2018 14:24
Last Modified: 27 Aug 2021 17:30

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