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Implications of the EU Emissions Trading System for the South-East Europe Regional Electricity Market

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  • Višković, Verena
  • Chen, Yihsu
  • Siddiqui, Afzal S.

Abstract

As part of its climate policy, the European Union (EU) aims to reduce greenhouse gas (GHG) emissions levels by 20% by the year 2020 compared to 1990 levels. Although the EU is projected to reach this goal, its achievement of objectives under its Emissions Trading System (ETS) may be delayed by carbon leakage, which is defined as a situation in which the reduction in emissions in the ETS region is partially offset by an increase in carbon emissions in the non-ETS regions. We study the interaction between emissions and hydropower availability in order to estimate the magnitude of carbon leakage in the South-East Europe Regional Electricity Market (SEE-REM) via a bottom-up partial equilibrium framework. We find that 6.3% to 40.5% of the emissions reduction achieved in the ETS part of SEE-REM could be leaked to the non-ETS part depending on the price of allowances. Somewhat surprisingly, greater hydropower availability may increase emissions in the ETS part of SEE-REM. However, carbon leakage might be limited by demand response to higher electricity prices in the non-ETS area of SEE-REM. Such carbon leakage can affect both the competitiveness of producers in ETS member countries on the periphery of the ETS and the achievement of EU targets for CO2 emissions reduction. Meanwhile, higher non-ETS electricity prices imply that the current policy can have undesirable outcomes for consumers in non-ETS countries, while non-ETS producers would experience an increase in their profits due to higher power prices as well as exports. The presence of carbon leakage in SEE-REM suggests that current EU policy might become more effective when it is expanded to cover more countries in the future.

Suggested Citation

  • Višković, Verena & Chen, Yihsu & Siddiqui, Afzal S., 2017. "Implications of the EU Emissions Trading System for the South-East Europe Regional Electricity Market," Energy Economics, Elsevier, vol. 65(C), pages 251-261.
  • Handle: RePEc:eee:eneeco:v:65:y:2017:i:c:p:251-261
    DOI: 10.1016/j.eneco.2017.04.033
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    Cited by:

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    3. Osorio, Sebastian & Pietzcker, Robert Carl & Pahle, Michael & Edenhofer, Ottmar, 2018. "How to deal with the risks of phasing out coal in Germany through national carbon pricing," EconStor Preprints 190771, ZBW - Leibniz Information Centre for Economics.
    4. Lin, Boqiang & Jia, Zhijie, 2019. "What will China's carbon emission trading market affect with only electricity sector involvement? A CGE based study," Energy Economics, Elsevier, vol. 78(C), pages 301-311.
    5. Višković, Verena & Chen, Yihsu & Siddiqui, Afzal S. & Tanaka, Makoto, 2019. "Regional carbon policies in an interconnected power system: How expanded coverage could exacerbate emission leakage," Energy Policy, Elsevier, vol. 134(C).
    6. Pan, Wenqi & Kim, Man-Keun & Ning, Zhuo & Yang, Hongqiang, 2020. "Carbon leakage in energy/forest sectors and climate policy implications using meta-analysis," Forest Policy and Economics, Elsevier, vol. 115(C).
    7. Feng Liu & Tao Lv & Yuan Meng & Xiaoran Hou & Jie Xu & Xu Deng, 2022. "Low-Carbon Transition Paths of Coal Power in China’s Provinces under the Context of the Carbon Trading Scheme," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    8. Lange, Ian & Maniloff, Peter, 2021. "Updating allowance allocations in cap-and-trade: Evidence from the NOx Budget Program," Journal of Environmental Economics and Management, Elsevier, vol. 105(C).

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    More about this item

    Keywords

    Carbon leakage; CO2 emissions; EU ETS; Energy Community;
    All these keywords.

    JEL classification:

    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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