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Exploring China's carbon emissions peak for different carbon tax scenarios

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  • Ding, Suiting
  • Zhang, Ming
  • Song, Yan

Abstract

Under the United Nations Framework Convention on Climate Change, China has made a commitment to peak CO2 emissions around 2030. China is still interested in fulfilling its commitment to address climate change. In this study, a diffusion model of energy technology based on endogenous technology learning under bounded rationality is developed to explore the possible impacts of different carbon tax conditions on the diffusion of energy technologies in China. It is concluded that the substitution of energy technology is a long-term and slow process. In the case of a high-carbon tax, transition technologies and new technologies will appear earlier and replace the original technology (6–8 years earlier than in the case of a lower carbon tax). The transition technology will become dominant only for a short period of time (around 2030) but will be quickly replaced by new technology. The traditional technology will cease to exist in the last thirty years of this century in all carbon tax scenarios. In the low-carbon tax or high-carbon tax scenarios, carbon emissions will peak in 2030 and then decline significantly. The peak in carbon emissions is lower under the high-carbon tax scenario than the low-carbon tax scenario, representing a 28% decrease.

Suggested Citation

  • Ding, Suiting & Zhang, Ming & Song, Yan, 2019. "Exploring China's carbon emissions peak for different carbon tax scenarios," Energy Policy, Elsevier, vol. 129(C), pages 1245-1252.
  • Handle: RePEc:eee:enepol:v:129:y:2019:i:c:p:1245-1252
    DOI: 10.1016/j.enpol.2019.03.037
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    1. Zhou, Nan & Fridley, David & Khanna, Nina Zheng & Ke, Jing & McNeil, Michael & Levine, Mark, 2013. "China's energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model," Energy Policy, Elsevier, vol. 53(C), pages 51-62.
    2. Fergus Green & Nicholas Stern, 2017. "China's changing economy: implications for its carbon dioxide emissions," Climate Policy, Taylor & Francis Journals, vol. 17(4), pages 423-442, May.
    3. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    4. Lohwasser, Richard & Madlener, Reinhard, 2012. "Economics of CCS for coal plants: Impact of investment costs and efficiency on market diffusion in Europe," Energy Economics, Elsevier, vol. 34(3), pages 850-863.
    5. Socrates Kypreos & Leonardo Barreto & Pantelis Capros & Sabine Messner, 2000. "ERIS: A model prototype with endogenous technological change," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 347-397.
    6. Arthur, W Brian, 1989. "Competing Technologies, Increasing Returns, and Lock-In by Historical Events," Economic Journal, Royal Economic Society, vol. 99(394), pages 116-131, March.
    7. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," Energy Economics, Elsevier, vol. 70(C), pages 45-60.
    8. Elzen, Michel den & Fekete, Hanna & Höhne, Niklas & Admiraal, Annemiek & Forsell, Nicklas & Hof, Andries F. & Olivier, Jos G.J. & Roelfsema, Mark & van Soest, Heleen, 2016. "Greenhouse gas emissions from current and enhanced policies of China until 2030: Can emissions peak before 2030?," Energy Policy, Elsevier, vol. 89(C), pages 224-236.
    9. Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
    10. Yu, Shiwei & Zheng, Shuhong & Li, Xia, 2018. "The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization," Energy Economics, Elsevier, vol. 74(C), pages 693-707.
    11. Ma, Tieju & Nakamori, Yoshiteru, 2009. "Modeling technological change in energy systems – From optimization to agent-based modeling," Energy, Elsevier, vol. 34(7), pages 873-879.
    12. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
    13. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," LSE Research Online Documents on Economics 86481, London School of Economics and Political Science, LSE Library.
    14. Wang, Yu & Zhou, Sheng & Huo, Hong, 2014. "Cost and CO2 reductions of solar photovoltaic power generation in China: Perspectives for 2020," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 370-380.
    15. Zhu Liu & Dabo Guan & Wei Wei & Steven J. Davis & Philippe Ciais & Jin Bai & Shushi Peng & Qiang Zhang & Klaus Hubacek & Gregg Marland & Robert J. Andres & Douglas Crawford-Brown & Jintai Lin & Hongya, 2015. "Reduced carbon emission estimates from fossil fuel combustion and cement production in China," Nature, Nature, vol. 524(7565), pages 335-338, August.
    16. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    17. Wang, Tao & Watson, Jim, 2010. "Scenario analysis of China's emissions pathways in the 21st century for low carbon transition," Energy Policy, Elsevier, vol. 38(7), pages 3537-3546, July.
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