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Quantum carbon finance: Carbon emission rights option pricing and investment decision

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  • Zhai, Dongsheng
  • Zhang, Tianrui
  • Liang, Guoqiang
  • Liu, Baoliu

Abstract

The carbon emission rights option (CERO), as a real option, can be used to measure market volatility risk and help enterprises obtain income. However, traditional option pricing models mostly operate under risk neutrality and are unsuitable for CERO pricing. Quantum finance, as a branch of economic physics, can effectively improve the uncertain behavior of financial markets. Therefore, in order to obtain more reasonable results, the study designs a quantum Monte Carlo simulation method for pricing CERO under risk uncertainty. Firstly, real options theory, quantum computing, Monte Carlo simulation, and stochastic volatility (SV) model are combined to achieve quantum state preparation and payoff calculation on quantum circuits. Then, quantum algorithms are used to achieve CERO dynamic pricing. Finally, a case study is conducted using data from the Beijing Green Exchange to verify the convergence and rationality of Quantum amplitude estimation (QAE) and its optimization algorithm in CERO pricing. The study results indicate that among the four QAE algorithms adopted, the results of two quantum algorithms are close to the traditional method and have good convergence. Compared to traditional method, the study solves the problem of difficult parameter determination in CERO pricing and reduces the results subjectivity. However, not all quantum algorithms are suitable for CERO pricing. The study further designed various carbon emission trading decisions among enterprises based on pricing results, providing a new approach for the management and operation of carbon assets in enterprises.

Suggested Citation

  • Zhai, Dongsheng & Zhang, Tianrui & Liang, Guoqiang & Liu, Baoliu, 2024. "Quantum carbon finance: Carbon emission rights option pricing and investment decision," Energy Economics, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324003360
    DOI: 10.1016/j.eneco.2024.107628
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    References listed on IDEAS

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    4. Zhang, Ying & Liu, Baoliu & Xue, Jinjun & Chen, Yiming & Zhao, Fang, 2025. "Would geopolitical risks be the new driver of the energy transition? An empirical study on renewable energy technology innovation," Energy Economics, Elsevier, vol. 141(C).
    5. Ghaemi Asl, Mahdi & Ben Jabeur, Sami & Nammouri, Hela & Bel Hadj Miled, Kamel, 2024. "Dynamic connectedness of quantum computing, artificial intelligence, and big data stocks on renewable and sustainable energy," Energy Economics, Elsevier, vol. 140(C).
    6. Chen, Jia & Wang, Ning & Lin, Tongzhi & Liu, Baoliu & Hu, Jin, 2024. "Shock or empowerment? Artificial intelligence technology and corporate ESG performance," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 1080-1096.
    7. Huang, Yujie & Liu, Shucheng & Gan, Jiawu & Liu, Baoliu & Wu, Yuxi, 2024. "How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China," Energy Economics, Elsevier, vol. 140(C).

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