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How should government and users share the investment costs and benefits of a solar PV power generation project in China?

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  • Shuai, Jing
  • Cheng, Xin
  • Ding, Liping
  • Yang, Jun
  • Leng, Zhihui

Abstract

Under the circumstances of global carbon emissions reduction, it has become a trend to promote the adoption of clean energies, such as solar energy. With the increasing maturity of photovoltaic (PV) technology, household-type distributed solar PV power generation projects are increasingly popular in China. Nevertheless, compared with conventional power generation, the initial cost of a solar PV project remains relatively high. Therefore, to mobilize the incentives of the general public, there is an urgent need for studies on how to share the costs and benefits of a solar PV power generation project between the government and users. By adopting the Shapley Game methodology, this paper has conducted a theoretical analysis of the cost-sharing among the central government, local government, and users and has built an investment cost-sharing model (Ci′=P(i)−Vs(i)−Vri,i=1,2,3), which is able to coordinate the benefit of all three stakeholders. This is followed by a case study. The results show that, under China's central government subsidy of 0.42 yuan per kWh, the best strategy for the local government to encourage the public to install solar PV facilities is to provide a one-off compensation equal to 30% of the initial investment. Finally, this paper proposes relevant policy recommendations to promote the development of solar PV power generation for emissions reduction in China.

Suggested Citation

  • Shuai, Jing & Cheng, Xin & Ding, Liping & Yang, Jun & Leng, Zhihui, 2019. "How should government and users share the investment costs and benefits of a solar PV power generation project in China?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 86-94.
    • Handle: RePEc:eee:rensus:v:104:y:2019:i:c:p:86-94
    DOI: 10.1016/j.rser.2019.01.003
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    References listed on IDEAS

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    1. Xiong, Yongqing & Yang, Xiaohan, 2016. "Government subsidies for the Chinese photovoltaic industry," Energy Policy, Elsevier, vol. 99(C), pages 111-119.
    2. Hasan, Kazi Nazmul & Saha, Tapan Kumar & Chattopadhyay, Deb & Eghbal, Mehdi, 2014. "Benefit-based expansion cost allocation for large scale remote renewable power integration into the Australian grid," Applied Energy, Elsevier, vol. 113(C), pages 836-847.
    3. Zhang, Yue-Jun & Wang, Ao-Dong & Da, Ya-Bin, 2014. "Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method," Energy Policy, Elsevier, vol. 74(C), pages 454-464.
    4. Yu, Shiwei & Wei, Yi-Ming & Wang, Ke, 2014. "Provincial allocation of carbon emission reduction targets in China: An approach based on improved fuzzy cluster and Shapley value decomposition," Energy Policy, Elsevier, vol. 66(C), pages 630-644.
    5. Timmer, Judith & Chessa, Michela & Boucherie, Richard J., 2013. "Cooperation and game-theoretic cost allocation in stochastic inventory models with continuous review," European Journal of Operational Research, Elsevier, vol. 231(3), pages 567-576.
    6. Zhang, Ling & Zhou, Peng & Newton, Sidney & Fang, Jian-xin & Zhou, De-qun & Zhang, Lu-ping, 2015. "Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method," Energy, Elsevier, vol. 90(P1), pages 953-964.
    7. Zhang, Lingxian & Wang, Jieqiong & Wen, Haojie & Fu, Zetian & Li, Xinxing, 2016. "Operating performance, industry agglomeration and its spatial characteristics of Chinese photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 373-386.
    8. Albrecht, Johan & Francois, Delphine & Schoors, Koen, 2002. "A Shapley decomposition of carbon emissions without residuals," Energy Policy, Elsevier, vol. 30(9), pages 727-736, July.
    9. Nasiri, Fuzhan & Zaccour, Georges, 2009. "An exploratory game-theoretic analysis of biomass electricity generation supply chain," Energy Policy, Elsevier, vol. 37(11), pages 4514-4522, November.
    10. Zhang, Ni & Yan, Yu & Su, Wencong, 2015. "A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers," Applied Energy, Elsevier, vol. 154(C), pages 471-479.
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    2. Fan, Jing-Li & Xu, Mao & Yang, Lin & Zhang, Xian, 2019. "Benefit evaluation of investment in CCS retrofitting of coal-fired power plants and PV power plants in China based on real options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
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    5. Shuai Wang & Yao Li & Junjun Jia, 2022. "How to promote sustainable adoption of residential distributed photovoltaic generation in China? An employment of incentive and punitive policies," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(2), pages 1-26, February.
    6. Xue, Yan & Lindkvist, Carmel Margaret & Temeljotov-Salaj, Alenka, 2021. "Barriers and potential solutions to the diffusion of solar photovoltaics from the public-private-people partnership perspective – Case study of Norway," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. Luo, Shihua & Hu, Weihao & Liu, Wen & Zhang, Zhenyuan & Bai, Chunguang & Huang, Qi & Chen, Zhe, 2022. "Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    8. Gao, Jianwei & Wang, Yaping & Guo, Fengjia & Chen, Jiayi, 2024. "A two-stage decision framework for GIS-based site selection of wind-photovoltaic-hybrid energy storage project using LSGDM method," Renewable Energy, Elsevier, vol. 222(C).
    9. Zhang, Hao & Wang, Mingyue & Cheng, Zhixuan & Wan, Ling, 2020. "Technology-sharing strategy and incentive mechanism for R&D teams of manufacturing enterprises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    10. Yingfeng Zhu, 2023. "Industry Stakeholders Perspectives on Assessing the Effect of Government Policy on Renewable Energy Investment in China," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 563-573, July.
    11. Zhao, Jing & Zhang, Qin, 2021. "The effect of contract methods on the lead time of a two-level photovoltaic supply chain: revenue-sharing vs. cost-sharing," Energy, Elsevier, vol. 231(C).
    12. Liu, Jing-Yue & Zhang, Yue-Jun, 2021. "Has carbon emissions trading system promoted non-fossil energy development in China?," Applied Energy, Elsevier, vol. 302(C).
    13. Cheng, Cheng & Dong, Kangyin & Wang, Zhen & Liu, Shulin & Jurasz, Jakub & Zhang, Haoran, 2023. "Rethinking the evaluation of solar photovoltaic projects under YieldCo mode: A real option perspective," Applied Energy, Elsevier, vol. 336(C).
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    15. Li, Yunwei & Chen, Kui & Ding, Ruixin & Zhang, Jing & Hao, Yu, 2023. "How do photovoltaic poverty alleviation projects relieve household energy poverty? Evidence from China," Energy Economics, Elsevier, vol. 118(C).

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