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Comprehensive comparison of multiple renewable power generation methods: A combination analysis of life cycle assessment and ecological footprint

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  • Gao, Chengkang
  • Zhu, Sulong
  • An, Nan
  • Na, Hongming
  • You, Huan
  • Gao, Chengbo

Abstract

As the goal of carbon neutrality has been proposed consecutively, renewable energy sources such as wind, solar, and bioenergy are gaining close attention in national strategic and sustainable development plans. Along with the development of renewable energy generation technologies, it is necessary to assess the environmental burden of renewable energy generation. This study analyzed the economic and environmental benefits of wind power, solar power, and biomass power generation methods over their life cycle. This paper comprehensively assessed and compared their environmental impacts based on a life cycle assessment (LCA) framework combined with ecological footprint analysis for the three renewable energy generation in China, and compared the results with those of thermal power generation to make strategic suggestions for the renewable energy power generation industry. The results show that: (1) wind power consumed the least energy, only 6.80 kJ/kW·h, and had the lowest emissions of CO2, SO2, NOx and CO and environmental impact potential. (2) Based on the ecological footprint analysis on the provincial level, only Anhui, Shandong and Jiangsu provinces showed ecological deficits, while the other provinces were in ecological surplus, mostly above 50% of their ecological areas. (3) Compared to thermal power generation, which consumed 1170.911 kJ/kW·h, wind power was the only one of the three renewable energy achieved a reduction in energy consumption. Wind power is currently the most efficient way of generating renewable energy.

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  • Gao, Chengkang & Zhu, Sulong & An, Nan & Na, Hongming & You, Huan & Gao, Chengbo, 2021. "Comprehensive comparison of multiple renewable power generation methods: A combination analysis of life cycle assessment and ecological footprint," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  • Handle: RePEc:eee:rensus:v:147:y:2021:i:c:s1364032121005426
    DOI: 10.1016/j.rser.2021.111255
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    6. Katarzyna Klimkiewicz & Anna Dubel & Katarzyna Południak-Gierz, 2023. "Supporting Environmentally Conscious Consumer Sales Law by Life-cycle Thinking," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 17(2), June.
    7. Scott, Connor & Ahsan, Mominul & Albarbar, Alhussein, 2023. "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Elsevier, vol. 278(C).
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