Consignment auctions of emissions trading systems: An agent-based approach based on China’s practice

Baixue Wang and Maosheng Duan

Energy Economics, 2022, vol. 112, issue C

Abstract: Policymakers of emissions trading systems (ETSs) around the world are faced with the challenges of how to allocate carbon allowances. On one hand, the free allocation would undermine the efficiency and fairness of ETSs, while on the other hand, a precipitous shift to auctions would put enormous economic pressure on covered entities. The consignment auction is supposed to combine the free allocation and the auction. However, few studies have investigated the consignment auction’s impacts on ETSs and commodity markets from a quantitative perspective. In this paper, an agent-based model is proposed, considering both the primary and secondary carbon markets, the commodity market and the behavior of firms. Simulation results based on China’s data show that, compared with the free allocation, the consignment auction can achieve a higher and more stable carbon price without significantly increasing the cost borne by firms, increase the liquidity of ETSs, and stimulate the technological advances of firms. Compared with the auction, the consignment auction reduces the volatilities of the commodity market induced by ETSs and facilitates the smooth operation of ETSs. Our results also suggest that the consignment auction is particularly suitable for the early stage of ETSs, while the auction remains the best allocation method as the ETS matures, as it significantly promotes long-term technological advances.

Keywords: Consignment auction; Emissions trading system; Allowance allocation; Agent-based model; System design (search for similar items in EconPapers)
JEL-codes: C61 C63 D44 Q43 Q48 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:112:y:2022:i:c:s0140988322003322

DOI: 10.1016/j.eneco.2022.106187

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