Spatiotemporal evolution of decommissioned photovoltaic distribution and integrated energy-economic-environmental-social sustainable benefit assessment in China

Jianli Zhou, Zihan Xu, Juan He, Dandan Liu, Yaqi Wang, Cheng Yang, Zhiming Zhong and Yunna Wu

Applied Energy, 2025, vol. 384, issue C, No S0306261925001898

Abstract: A clear and detailed understanding of the temporal and spatial distribution of decommissioned photovoltaic (PV) systems in China, combined with a comprehensive evaluation of the sustainability benefits related to recycling and reusing these decommissioned PV components, is crucial for effectively addressing the upcoming wave of PV decommissioning in the country. There is currently insufficient research on the temporal and spatial distribution of decommissioned PV in China and its recycling. This study constructs a prediction model using random forest and BP neural network methods, characterizing the temporal and spatial evolution from 2024 to 2050 under eight scenarios. A comprehensive assessment of sustainable benefits is conducted from energy, economy, environment, and society dimensions. The Fuzzy Analytic Hierarchy Process (FAHP)-Criteria Importance Through Inter-criteria Correlation (CRITIC)-Multi-Attribute Based Analytic Compromise (MABAC) method ranks the comprehensive sustainability benefits of decommissioned PV recycling in each province. Results show that by 2050, decommissioned PV will range from 8.32E+10 to 1.65E+11 kg (670 to 1600 GW), or 1 to 3 times the current installed capacity. By 2030, Qinghai, Xinjiang, and Gansu will be the main decommissioning areas. After 2040, Shandong, Hebei, Henan, Zhejiang, Jiangsu, and other areas will be the main decommissioning regions. Recycling decommissioned PV will save 1.49E+15 to 3.58E+15 MJ of primary energy, create a trillion-level market, reduce GWP by 3.81E+11 to 9.11E+11 kg CO2 eq, and create millions of jobs. Shandong Province has the highest sustainable benefits. These studies provide detailed data, theoretical support, and methodological thinking for policy formulation. The research is applicable to other countries facing decommissioned PV disposal.

Keywords: Decommissioned PV; Spatiotemporal evolution; Comprehensive sustainability benefits; Machine Learning; FAHP-CRITIC-MABAC method; Scenario analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125459

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