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Potential for reducing global carbon emissions from electricity production--A benchmarking analysis

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  • Ang, B.W.
  • Zhou, P.
  • Tay, L.P.

Abstract

We present five performance indicators for electricity generation for 129 countries using the 2005 data. These indicators, measured at the national level, are the aggregate CO2 intensity of electricity production, the efficiencies of coal, oil and gas generation and the share of electricity produced from non-fossil fuels. We conduct a study on the potential for reducing global energy-related CO2 emissions from electricity production through simple benchmarking. This is performed based on the last four performance indicators and the construction of a cumulative curve for each of these indicators. It is found that global CO2 emissions from electricity production would be reduced by 19% if all these indicators are benchmarked at the 50th percentile. Not surprisingly, the emission reduction potential measured in absolute terms is the highest for large countries such as China, India, Russia and the United States. When the potential is expressed as a percentage of a country's own emissions, few of these countries appear in the top-five list.

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  • Ang, B.W. & Zhou, P. & Tay, L.P., 2011. "Potential for reducing global carbon emissions from electricity production--A benchmarking analysis," Energy Policy, Elsevier, vol. 39(5), pages 2482-2489, May.
  • Handle: RePEc:eee:enepol:v:39:y:2011:i:5:p:2482-2489
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    3. Nabavieh, Alireza & Gholamiangonabadi, Davoud & Ahangaran, Ali Asghar, 2015. "Dynamic changes in CO2 emission performance of different types of Iranian fossil-fuel power plants," Energy Economics, Elsevier, vol. 52(PA), pages 142-150.
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    5. Chen, Jiandong & Cheng, Shulei & Song, Malin & Wu, Yinyin, 2016. "A carbon emissions reduction index: Integrating the volume and allocation of regional emissions," Applied Energy, Elsevier, vol. 184(C), pages 1154-1164.
    6. Goh, Tian & Ang, B.W. & Su, Bin & Wang, H., 2018. "Drivers of stagnating global carbon intensity of electricity and the way forward," Energy Policy, Elsevier, vol. 113(C), pages 149-156.
    7. Michas, Serafeim & Stavrakas, Vassilis & Papadelis, Sotiris & Flamos, Alexandros, 2020. "A transdisciplinary modeling framework for the participatory design of dynamic adaptive policy pathways," Energy Policy, Elsevier, vol. 139(C).
    8. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    9. Zhang, Yue-Jun & Hao, Jun-Fang & Song, Juan, 2016. "The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level," Applied Energy, Elsevier, vol. 174(C), pages 213-223.
    10. Ang, B.W. & Su, Bin, 2016. "Carbon emission intensity in electricity production: A global analysis," Energy Policy, Elsevier, vol. 94(C), pages 56-63.
    11. Wang, Nannan & Chen, Ji & Yao, Shengnan & Chang, Yen-Chiang, 2018. "A meta-frontier DEA approach to efficiency comparison of carbon reduction technologies on project level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2606-2612.
    12. Chaturvedi, Vaibhav & Clarke, Leon & Edmonds, James & Calvin, Katherine & Kyle, Page, 2014. "Capital investment requirements for greenhouse gas emissions mitigation in power generation on near term to century time scales and global to regional spatial scales," Energy Economics, Elsevier, vol. 46(C), pages 267-278.
    13. Wang, Yongpei & Li, Jun, 2019. "Spatial spillover effect of non-fossil fuel power generation on carbon dioxide emissions across China's provinces," Renewable Energy, Elsevier, vol. 136(C), pages 317-330.
    14. Ang, B.W. & Goh, Tian, 2016. "Carbon intensity of electricity in ASEAN: Drivers, performance and outlook," Energy Policy, Elsevier, vol. 98(C), pages 170-179.
    15. Wang, Ning & Wen, Zongguo & Liu, Mingqi & Guo, Jie, 2016. "Constructing an energy efficiency benchmarking system for coal production," Applied Energy, Elsevier, vol. 169(C), pages 301-308.
    16. Malin Song & Kuangnan Fang & Jing Zhang & Jianbin Wu, 2019. "The Co-movement Between Chinese Oil Market and Other Main International Oil Markets: A DCC-MGARCH Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1303-1318, December.
    17. Goh, Tian & Ang, B.W. & Xu, X.Y., 2018. "Quantifying drivers of CO2 emissions from electricity generation – Current practices and future extensions," Applied Energy, Elsevier, vol. 231(C), pages 1191-1204.

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