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Mutual Influences Among the Electricity Market, Carbon Emission Market, and Renewable Energy Certificate Market

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  • Hongbo Zou

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China
    Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China)

  • Yuhong Luo

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Fushuan Wen

    (Hainan Institute, Zhejiang University, Sanya 572025, China)

  • Jiehao Chen

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Jinlong Yang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

  • Changhua Yang

    (College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China)

Abstract

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term.

Suggested Citation

  • Hongbo Zou & Yuhong Luo & Fushuan Wen & Jiehao Chen & Jinlong Yang & Changhua Yang, 2024. "Mutual Influences Among the Electricity Market, Carbon Emission Market, and Renewable Energy Certificate Market," Energies, MDPI, vol. 17(23), pages 1-15, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6139-:d:1537665
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    References listed on IDEAS

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    1. Schusser, Sandra & Jaraitė, Jūratė, 2018. "Explaining the interplay of three markets: Green certificates, carbon emissions and electricity," Energy Economics, Elsevier, vol. 71(C), pages 1-13.
    2. Zhang, Wei & Li, Jing & Li, Guoxiang & Guo, Shucen, 2020. "Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China," Energy, Elsevier, vol. 196(C).
    3. Wang, Hongye & Su, Bin & Mu, Hailin & Li, Nan & Gui, Shusen & Duan, Ye & Jiang, Bo & Kong, Xue, 2020. "Optimal way to achieve renewable portfolio standard policy goals from the electricity generation, transmission, and trading perspectives in southern China," Energy Policy, Elsevier, vol. 139(C).
    4. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    5. Helgesen, Per Ivar & Tomasgard, Asgeir, 2018. "An equilibrium market power model for power markets and tradable green certificates, including Kirchhoff's Laws and Nash-Cournot competition," Energy Economics, Elsevier, vol. 70(C), pages 270-288.
    6. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    7. Shahnazari, Mahdi & McHugh, Adam & Maybee, Bryan & Whale, Jonathan, 2017. "Overlapping carbon pricing and renewable support schemes under political uncertainty: Global lessons from an Australian case study," Applied Energy, Elsevier, vol. 200(C), pages 237-248.
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