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Prediction of Carbon Price in EU-ETS Using a Geometric Brownian Motion Model and Its Application to Analyze the Economic Competitiveness of Carbon Capture and Storage

Author

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  • Gwang Goo Lee

    (Department of Mechanical Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Republic of Korea)

  • Sung-Won Ham

    (Department of Chemical Engineering, Kyungil University, 50 Gamasil-gil, Hayang-eup, Gyeongsan 38428, Republic of Korea)

Abstract

To achieve carbon neutrality, many countries and regions are making efforts to promote the commercialization of greenhouse gas (GHG) mitigation technologies using emissions trading systems (ETSs). Accurate predictions of when the cost of GHG reduction technologies will become competitive below carbon prices could be invaluable to engineers and policy makers. In this study, carbon price movement in the EU-ETS was analyzed using a geometric Brownian motion (GBM) model. Using daily price data for the last 10 years, it tested whether the price pattern of the latter three years could be predicted by applying the first seven years of data to the GBM model. The results showed that the GBM model could well predict the upper and lower bounds of the actual carbon price. Based on the acceptable predictability of the GBM model, simulations were performed using carbon price data over the last decade, showing that carbon prices would reach around 200 EUR/tCO 2 by the start of 2026. This is higher than the cost of CO 2 avoided evaluated from the costs of commercial-scale carbon capture facilities for coal-fired power plants. This means that carbon capture technologies in the coal-fired power sector could become economically competitive within the next several years.

Suggested Citation

  • Gwang Goo Lee & Sung-Won Ham, 2023. "Prediction of Carbon Price in EU-ETS Using a Geometric Brownian Motion Model and Its Application to Analyze the Economic Competitiveness of Carbon Capture and Storage," Energies, MDPI, vol. 16(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6333-:d:1230219
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    References listed on IDEAS

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    Cited by:

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