Carbon allowance decision optimization with multi-agent simulation: Incorporating behavioral drivers

Carbon trading markets play a vital role in reducing emissions, with the initial allocation of carbon allowances being a key issue. As many emerging markets shift from free allocation to auction mechanism, this study develops a carbon allowance decision optimization model based on multi-agent simulation under two commonly used auction mechanisms. The model considers both government's auction effectiveness and total companies' carbon compliance cost, and incorporates behavioral factors influencing corporate bidding behavior: risk attitude and information feedback. This paper further assesses how key auction parameters like reserve price, allowance supply, and secondary market transaction price affect auction efficiency, corporate compliance costs, and carbon reduction outcomes. The multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the model, and the TOPSIS method helps select ideal solutions from the Pareto set. The main results include: (1) Risk-seeking companies are more likely to win bids, highlighting the impact of bidding attitudes; (2) Under trusted social network, as the density of corporate social networks increases, auction information feedback helps improve auction efficiency, but excessive bid adjustments may lead to convergence and reduce efficiency; In contrast, the existence of false underreporting information will lead to a decrease in auction efficiency and total enterprise costs, which is particularly evident under the uniform-price auction mechanism. (3) The increase in auction reserve price and secondary market transaction price can both encourage companies to reduce carbon emissions; (4) Increasing allowance supply reduces compliance costs but may weaken companies' emission reduction incentives. This study provides insights for governments in designing carbon allowance auction mechanisms that balance auction efficiency and corporate compliance costs, as well as emission reduction outcomes. It also offers decision-making guidance for enterprises in optimizing carbon compliance strategies.