IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v150y2021ics0301421521000197.html
   My bibliography  Save this article

Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission

Author

Listed:
  • Xiao, Jin
  • Li, Guohao
  • Xie, Ling
  • Wang, Shouyang
  • Yu, Lean

Abstract

The power sector has tremendous technological potential for decarbonization, hence, the decarbonization of China's power sector is crucial to the successful implementation of national carbon emissions reduction plan. In this study, a decarbonization model with consideration of both technological progress and cross-regional power transmission for China's power sector is built to explore the potential and possible pathway of decarbonization under the constraint of optimal cost. The model is operated at different carbon prices in three economic growth scenarios. Our findings show that the power generation structure is undergoing a shift from coal power to hydropower, nuclear power, and wind power, and both the coal power generation and carbon emissions can peak by 2030. Carbon pricing can speed up this process and reduce the peak height. Additionally, an appropriate carbon price has a significant promoting effect on decarbonization in the power sector; we conclude that the optimal carbon price is 21 USD/t. Moreover, the higher the economic development level, the lower the final average cost of power generation mix and the more significant the carbon emissions reduction, which implies that it is more cost-effective to carry out decarbonization related policy in the power sector during the stage of rapid economic growth.

Suggested Citation

  • Xiao, Jin & Li, Guohao & Xie, Ling & Wang, Shouyang & Yu, Lean, 2021. "Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission," Energy Policy, Elsevier, vol. 150(C).
  • Handle: RePEc:eee:enepol:v:150:y:2021:i:c:s0301421521000197
    DOI: 10.1016/j.enpol.2021.112150
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421521000197
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2021.112150?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mercure, J.-F. & Pollitt, H. & Chewpreecha, U. & Salas, P. & Foley, A.M. & Holden, P.B. & Edwards, N.R., 2014. "The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector," Energy Policy, Elsevier, vol. 73(C), pages 686-700.
    2. Chen, Hao & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2016. "A multi-period power generation planning model incorporating the non-carbon external costs: A case study of China," Applied Energy, Elsevier, vol. 183(C), pages 1333-1345.
    3. Hayashi, Daisuke & Huenteler, Joern & Lewis, Joanna I., 2018. "Gone with the wind: A learning curve analysis of China's wind power industry," Energy Policy, Elsevier, vol. 120(C), pages 38-51.
    4. Hui, Jingxuan & Cai, Wenjia & Wang, Can & Ye, Minhua, 2017. "Analyzing the penetration barriers of clean generation technologies in China’s power sector using a multi-region optimization model," Applied Energy, Elsevier, vol. 185(P2), pages 1809-1820.
    5. Chen, Qixin & Kang, Chongqing & Xia, Qing & Guan, Dabo, 2011. "Preliminary exploration on low-carbon technology roadmap of China’s power sector," Energy, Elsevier, vol. 36(3), pages 1500-1512.
    6. Vaillancourt, Kathleen & Bahn, Olivier & Frenette, Erik & Sigvaldason, Oskar, 2017. "Exploring deep decarbonization pathways to 2050 for Canada using an optimization energy model framework," Applied Energy, Elsevier, vol. 195(C), pages 774-785.
    7. Xiao, Jin & Li, Yuxi & Xie, Ling & Liu, Dunhu & Huang, Jing, 2018. "A hybrid model based on selective ensemble for energy consumption forecasting in China," Energy, Elsevier, vol. 159(C), pages 534-546.
    8. Zhang, Yaru & Ma, Tieju & Guo, Fei, 2018. "A multi-regional energy transport and structure model for China’s electricity system," Energy, Elsevier, vol. 161(C), pages 907-919.
    9. McNerney, James & Doyne Farmer, J. & Trancik, Jessika E., 2011. "Historical costs of coal-fired electricity and implications for the future," Energy Policy, Elsevier, vol. 39(6), pages 3042-3054, June.
    10. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    11. Li, Ying & Lukszo, Zofia & Weijnen, Margot, 2016. "The impact of inter-regional transmission grid expansion on China’s power sector decarbonization," Applied Energy, Elsevier, vol. 183(C), pages 853-873.
    12. Tu, Qiang & Betz, Regina & Mo, Jianlei & Fan, Ying & Liu, Yu, 2019. "Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution," Applied Energy, Elsevier, vol. 250(C), pages 1053-1064.
    13. Greg H. Rau & Heather D. Willauer & Zhiyong Jason Ren, 2018. "The global potential for converting renewable electricity to negative-CO2-emissions hydrogen," Nature Climate Change, Nature, vol. 8(7), pages 621-625, July.
    14. Kannan, R., 2009. "Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets," Applied Energy, Elsevier, vol. 86(10), pages 1873-1886, October.
    15. Zhang, Dongjie & Ma, Linwei & Liu, Pei & Zhang, Lili & Li, Zheng, 2012. "A multi-period superstructure optimisation model for the optimal planning of China's power sector considering carbon dioxide mitigation," Energy Policy, Elsevier, vol. 41(C), pages 173-183.
    16. Chen, Wenying & Li, Hualin & Wu, Zongxin, 2010. "Western China energy development and west to east energy transfer: Application of the Western China Sustainable Energy Development Model," Energy Policy, Elsevier, vol. 38(11), pages 7106-7120, November.
    17. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    18. Zhang, Qiang & Chen, Wenying, 2020. "Modeling China’s interprovincial electricity transmission under low carbon transition," Applied Energy, Elsevier, vol. 279(C).
    19. Zhou, Nan & Price, Lynn & Yande, Dai & Creyts, Jon & Khanna, Nina & Fridley, David & Lu, Hongyou & Feng, Wei & Liu, Xu & Hasanbeigi, Ali & Tian, Zhiyu & Yang, Hongwei & Bai, Quan & Zhu, Yuezhong & Xio, 2019. "A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030," Applied Energy, Elsevier, vol. 239(C), pages 793-819.
    20. Liu, Junling & Wang, Ke & Zou, Ji & Kong, Ying, 2019. "The implications of coal consumption in the power sector for China’s CO2 peaking target," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    21. Handayani, Kamia & Filatova, Tatiana & Krozer, Yoram & Anugrah, Pinto, 2020. "Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion," Applied Energy, Elsevier, vol. 262(C).
    22. Yu, Yang & Li, Hong & Che, Yuyuan & Zheng, Qiongjie, 2017. "The price evolution of wind turbines in China: A study based on the modified multi-factor learning curve," Renewable Energy, Elsevier, vol. 103(C), pages 522-536.
    23. Cai, Wenjia & Wang, Can & Wang, Ke & Zhang, Ying & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's electricity sector," Energy Policy, Elsevier, vol. 35(12), pages 6445-6456, December.
    24. Michaja Pehl & Anders Arvesen & Florian Humpenöder & Alexander Popp & Edgar G. Hertwich & Gunnar Luderer, 2017. "Understanding future emissions from low-carbon power systems by integration of life-cycle assessment and integrated energy modelling," Nature Energy, Nature, vol. 2(12), pages 939-945, December.
    25. Noah Kittner & Felix Lill & Daniel M. Kammen, 2017. "Energy storage deployment and innovation for the clean energy transition," Nature Energy, Nature, vol. 2(9), pages 1-6, September.
    26. Steve Pye & Francis G. N. Li & James Price & Birgit Fais, 2017. "Erratum: Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era," Nature Energy, Nature, vol. 2(6), pages 1-1, June.
    27. Steve Pye & Francis G. N. Li & James Price & Birgit Fais, 2017. "Achieving net-zero emissions through the reframing of UK national targets in the post-Paris Agreement era," Nature Energy, Nature, vol. 2(3), pages 1-7, March.
    28. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    29. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    30. Tang, Baojun & Li, Ru & Yu, Biying & An, Runying & Wei, Yi-Ming, 2018. "How to peak carbon emissions in China's power sector: A regional perspective," Energy Policy, Elsevier, vol. 120(C), pages 365-381.
    31. Qi, Tianyu & Zhang, Xiliang & Karplus, Valerie J., 2014. "The energy and CO2 emissions impact of renewable energy development in China," Energy Policy, Elsevier, vol. 68(C), pages 60-69.
    32. Victor, Nadejda & Nichols, Christopher & Zelek, Charles, 2018. "The U.S. power sector decarbonization: Investigating technology options with MARKAL nine-region model," Energy Economics, Elsevier, vol. 73(C), pages 410-425.
    33. Lin, Boqiang & He, Jiaxin, 2016. "Learning curves for harnessing biomass power: What could explain the reduction of its cost during the expansion of China?," Renewable Energy, Elsevier, vol. 99(C), pages 280-288.
    34. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.
    35. Wang, Ke & Wang, Can & Lu, Xuedu & Chen, Jining, 2007. "Scenario analysis on CO2 emissions reduction potential in China's iron and steel industry," Energy Policy, Elsevier, vol. 35(4), pages 2320-2335, April.
    36. Marianne Zeyringer & James Price & Birgit Fais & Pei-Hao Li & Ed Sharp, 2018. "Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather," Nature Energy, Nature, vol. 3(5), pages 395-403, May.
    37. Chen, Qixin & Kang, Chongqing & Ming, Hao & Wang, Zeyu & Xia, Qing & Xu, Guoxin, 2014. "Assessing the low-carbon effects of inter-regional energy delivery in China's electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 671-683.
    38. Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian, 2015. "A multi-region optimization planning model for China’s power sector," Applied Energy, Elsevier, vol. 137(C), pages 413-426.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Guohao & Niu, Miaomiao & Xiao, Jin & Wu, Jiaqian & Li, Jinkai, 2023. "The rebound effect of decarbonization in China’s power sector under the carbon trading scheme," Energy Policy, Elsevier, vol. 177(C).
    2. Jiang, Hong-Dian & Dong, Kangyin & Qing, Jing & Teng, Qiang, 2023. "The role of technical change in low-carbon transformation and crises in the electricity market: A CGE analysis with R&D investment," Energy Economics, Elsevier, vol. 125(C).
    3. Jieran Feng & Hao Zhou, 2022. "Bi-Level Optimal Capacity Planning of Load-Side Electric Energy Storage Using an Emission-Considered Carbon Incentive Mechanism," Energies, MDPI, vol. 15(13), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Elia, A. & Kamidelivand, M. & Rogan, F. & Ó Gallachóir, B., 2021. "Impacts of innovation on renewable energy technology cost reductions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    2. Zhang, Shuang & Zhao, Tao & Xie, Bai-Chen, 2018. "What is the optimal power generation mix of China? An empirical analysis using portfolio theory," Applied Energy, Elsevier, vol. 229(C), pages 522-536.
    3. Elia, A. & Taylor, M. & Ó Gallachóir, B. & Rogan, F., 2020. "Wind turbine cost reduction: A detailed bottom-up analysis of innovation drivers," Energy Policy, Elsevier, vol. 147(C).
    4. Li, Ying & Lukszo, Zofia & Weijnen, Margot, 2015. "The implications of CO2 price for China’s power sector decarbonization," Applied Energy, Elsevier, vol. 146(C), pages 53-64.
    5. Liu, Hailiang & Brown, Tom & Andresen, Gorm Bruun & Schlachtberger, David P. & Greiner, Martin, 2019. "The role of hydro power, storage and transmission in the decarbonization of the Chinese power system," Applied Energy, Elsevier, vol. 239(C), pages 1308-1321.
    6. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    7. Li, Ying & Lukszo, Zofia & Weijnen, Margot, 2016. "The impact of inter-regional transmission grid expansion on China’s power sector decarbonization," Applied Energy, Elsevier, vol. 183(C), pages 853-873.
    8. Chen, Hao & Tang, Bao-Jun & Liao, Hua & Wei, Yi-Ming, 2016. "A multi-period power generation planning model incorporating the non-carbon external costs: A case study of China," Applied Energy, Elsevier, vol. 183(C), pages 1333-1345.
    9. Guo, Zheng & Ma, Linwei & Liu, Pei & Jones, Ian & Li, Zheng, 2016. "A multi-regional modelling and optimization approach to China's power generation and transmission planning," Energy, Elsevier, vol. 116(P2), pages 1348-1359.
    10. Wang, Junfeng & He, Shutong & Qiu, Ye & Liu, Nan & Li, Yongjian & Dong, Zhanfeng, 2018. "Investigating driving forces of aggregate carbon intensity of electricity generation in China," Energy Policy, Elsevier, vol. 113(C), pages 249-257.
    11. Schauf, Magnus & Schwenen, Sebastian, 2021. "Mills of progress grind slowly? Estimating learning rates for onshore wind energy," Energy Economics, Elsevier, vol. 104(C).
    12. Zhenyu Zhuo & Ershun Du & Ning Zhang & Chris P. Nielsen & Xi Lu & Jinyu Xiao & Jiawei Wu & Chongqing Kang, 2022. "Cost increase in the electricity supply to achieve carbon neutrality in China," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    13. Wang, Hongye & Su, Bin & Mu, Hailin & Li, Nan & Jiang, Bo & Kong, Xue, 2019. "Optimization of electricity generation and interprovincial trading strategies in Southern China," Energy, Elsevier, vol. 174(C), pages 696-707.
    14. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    15. Guo, Zheng & Cheng, Rui & Xu, Zhaofeng & Liu, Pei & Wang, Zhe & Li, Zheng & Jones, Ian & Sun, Yong, 2017. "A multi-region load dispatch model for the long-term optimum planning of China’s electricity sector," Applied Energy, Elsevier, vol. 185(P1), pages 556-572.
    16. Yi, Bo-Wen & Xu, Jin-Hua & Fan, Ying, 2016. "Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model," Applied Energy, Elsevier, vol. 184(C), pages 641-658.
    17. Gunther Glenk & Rebecca Meier & Stefan Reichelstein, 2021. "Cost Dynamics of Clean Energy Technologies," Schmalenbach Journal of Business Research, Springer, vol. 73(2), pages 179-206, June.
    18. Abdul Ghani Olabi & Tabbi Wilberforce & Khaled Elsaid & Enas Taha Sayed & Tareq Salameh & Mohammad Ali Abdelkareem & Ahmad Baroutaji, 2021. "A Review on Failure Modes of Wind Turbine Components," Energies, MDPI, vol. 14(17), pages 1-44, August.
    19. Tu, Qiang & Betz, Regina & Mo, Jianlei & Fan, Ying & Liu, Yu, 2019. "Achieving grid parity of wind power in China – Present levelized cost of electricity and future evolution," Applied Energy, Elsevier, vol. 250(C), pages 1053-1064.
    20. Zhang, Yaru & Ma, Tieju & Guo, Fei, 2018. "A multi-regional energy transport and structure model for China’s electricity system," Energy, Elsevier, vol. 161(C), pages 907-919.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:150:y:2021:i:c:s0301421521000197. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.