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Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data

Author

Listed:
  • Bart Sweerts

    (ETH Zürich
    University of Amsterdam)

  • Stefan Pfenninger

    (ETH Zürich)

  • Su Yang

    (ETH Zürich
    China Meteorological Administration)

  • Doris Folini

    (ETH Zürich)

  • Bob Zwaan

    (University of Amsterdam
    Netherlands Organisation for Applied Scientific Research (ECN-TNO)
    Johns Hopkins University)

  • Martin Wild

    (ETH Zürich)

Abstract

China is the largest worldwide consumer of solar photovoltaic (PV) electricity, with 130 GW of installed capacity as of 2017. China’s PV capacity is expected to reach at least 400 GW by 2030, to provide 10% of its primary energy. However, anthropogenic aerosol emissions and changes in cloud cover affect solar radiation in China. Here, we use observational radiation data from 119 stations across China to show that the PV potential decreased on average by 11–15% between 1960 and 2015. The relationship between observed surface radiation and emissions of sulfur dioxide and black carbon suggests that strict air pollution control measures, combined with reduced fossil fuel consumption, would allow surface radiation to increase. We find that reverting back to 1960s radiation levels in China could yield a 12–13% increase in electricity generation, equivalent to an additional 14 TWh produced with 2016 PV capacities, and 51–74 TWh with the expected 2030 capacities. The corresponding economic benefits could amount to US$1.9 billion in 2016 and US$4.6–6.7 billion in 2030.

Suggested Citation

  • Bart Sweerts & Stefan Pfenninger & Su Yang & Doris Folini & Bob Zwaan & Martin Wild, 2019. "Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data," Nature Energy, Nature, vol. 4(8), pages 657-663, August.
    • Handle: RePEc:nat:natene:v:4:y:2019:i:8:d:10.1038_s41560-019-0412-4
    DOI: 10.1038/s41560-019-0412-4
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    Cited by:

    1. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    2. Li, Fen & Lin, Yilun & Guo, Jianping & Wang, Yue & Mao, Ling & Cui, Yang & Bai, Yongqing, 2020. "Novel models to estimate hourly diffuse radiation fraction for global radiation based on weather type classification," Renewable Energy, Elsevier, vol. 157(C), pages 1222-1232.
    3. Arifa Tanveer & Shihong Zeng & Muhammad Irfan & Rui Peng, 2021. "Do Perceived Risk, Perception of Self-Efficacy, and Openness to Technology Matter for Solar PV Adoption? An Application of the Extended Theory of Planned Behavior," Energies, MDPI, vol. 14(16), pages 1-24, August.
    4. WenminQin, & Wang, Lunche & Gueymard, Christian A. & Bilal, Muhammad & Lin, Aiwen & Wei, Jing & Zhang, Ming & Yang, Xuefang, 2020. "Constructing a gridded direct normal irradiance dataset in China during 1981–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Sadat, Seyyed Ali & Hoex, Bram & Pearce, Joshua M., 2022. "A Review of the Effects of Haze on Solar Photovoltaic Performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    7. Zeng, Shihong & Tanveer, Arifa & Fu, Xiaolan & Gu, Yuxiao & Irfan, Muhammad, 2022. "Modeling the influence of critical factors on the adoption of green energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Phuong Minh Khuong & Russell McKenna & Wolf Fichtner, 2020. "A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries," Energies, MDPI, vol. 13(10), pages 1-46, May.
    9. Xiao, Han & Song, Feng & Zheng, Xinye & Chen, Jiaying, 2023. "Community-based energy revolution: An evaluation of China's photovoltaic poverty alleviation Program's economic and social benefits," Energy Policy, Elsevier, vol. 177(C).
    10. Jiang, Hou & Lu, Ning & Qin, Jun & Yao, Ling, 2021. "Hierarchical identification of solar radiation zones in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    11. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).
    12. Zhao, Xiaohu & Huang, Guohe & Lu, Chen & Zhou, Xiong & Li, Yongping, 2020. "Impacts of climate change on photovoltaic energy potential: A case study of China," Applied Energy, Elsevier, vol. 280(C).
    13. Cheng, Xinghong & Ye, Dong & Shen, Yanbo & Li, Deping & Feng, Jinming, 2022. "Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China," Renewable Energy, Elsevier, vol. 196(C), pages 358-365.
    14. Kuang-Sheng Liu & Iskandar Muda & Ming-Hung Lin & Ngakan Ketut Acwin Dwijendra & Gaylord Carrillo Caballero & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room," Sustainability, MDPI, vol. 15(2), pages 1-14, January.

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