 Carbon price prediction model based on multi-agent and environment co-evolutionZhang Xiande, Fu Chonghui, Xie Pengcheng, Bo Yajie, Pan Feng and Wang Wenjun Energy, 2025, vol. 328, issue C Abstract: Predictive models for carbon prices are of great significance for the development of a low-carbon economy. Many scholars have conducted carbon price prediction studies using various methods, but most of these studies have focused on the macro level without analyzing the micro level. The multi-agent and environment co-evolution model constructed in this paper uses neural networks to simulate enterprise decision-making. It forms market equilibrium prices based on the auction mechanism and adjusts the decision models of agents in a coordinated evolution manner based on these equilibrium prices, resulting in the final prediction outcomes. Additionally, this model can also be used to predict the prices of various futures, stocks, or other commodities, analyzing the preference changes of different agents from a micro-level perspective. This paper demonstrates through a case study that the multi-agent and environment co-evolution model with strong heterogeneity achieves higher accuracy in specific prediction tasks. Keywords: Multi-agent; Carbon price prediction; Neural network; Genetic algorithm; Evolutionary game theory (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225023217 DOI: 10.1016/j.energy.2025.136679 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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