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Price dynamics in the European Union Emissions Trading System and evaluation of its ability to boost emission-related investment decisions

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  • Flora, Maria
  • Vargiolu, Tiziano

Abstract

The price of permits in the European Union Emissions Trading System (EU ETS) has historically been highly sensitive and prone to jumps. We consider different stochastic processes to model the price of permits, and show that the Variance Gamma (VG) model provides the best fit for the price distribution, among a selection of infinite activity processes. Using this result as a starting point, we assess the effects of the EU ETS in delivering low-carbon investments at the firm level, by modeling a price taker electricity producer subject to the EU ETS jurisdiction. We compute, via Least Squares Monte Carlo, the value of the real option the greenhouse gas emitter has, consisting in the opportunity to switch from its current high-carbon technology to a cleaner one. We use a VG specification for carbon prices, and a mean-reverting (Brennan–Schwartz) process for the price of fuel. Moreover, we further analyze the investment decision problem, in case of a CO2 price stabilization mechanism in the form of a price floor, by explicitly computing the expected value of the investment project by means of Fourier methods. Our results show that the introduction of the price stabilization mechanism significantly affects the timing of the investment decision, and supports emission-related investments.

Suggested Citation

  • Flora, Maria & Vargiolu, Tiziano, 2020. "Price dynamics in the European Union Emissions Trading System and evaluation of its ability to boost emission-related investment decisions," European Journal of Operational Research, Elsevier, vol. 280(1), pages 383-394.
    • Handle: RePEc:eee:ejores:v:280:y:2020:i:1:p:383-394
    DOI: 10.1016/j.ejor.2019.07.026
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    Citations

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    Cited by:

    1. Zhang, Yue-Jun & Wang, Wei, 2021. "How does China's carbon emissions trading (CET) policy affect the investment of CET-covered enterprises?," Energy Economics, Elsevier, vol. 98(C).
    2. Yuan, Quan & Hua, Zhongsheng & Shen, Bin, 2021. "An automated system of emissions permit trading for transportation firms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Flora, Maria & Tankov, Peter, 2023. "Green investment and asset stranding under transition scenario uncertainty," Energy Economics, Elsevier, vol. 124(C).
    4. Pan, Di & Zhang, Chen & Zhu, Dandan & Ji, Yuanpu & Cao, Wei, 2022. "A novel method of detecting carbon asset price jump characteristics based on significant information shocks," Finance Research Letters, Elsevier, vol. 47(PA).
    5. Kang, Yicheng & Liao, Sha & Jiang, Changmin & D’Alfonso, Tiziana, 2022. "Synthetic control methods for policy analysis: Evaluating the effect of the European Emission Trading System on aviation supply," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 236-252.
    6. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    7. Nadarajah, Selvaprabu & Secomandi, Nicola, 2023. "A review of the operations literature on real options in energy," European Journal of Operational Research, Elsevier, vol. 309(2), pages 469-487.

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