By 2050, the cumulative mass of end-of-life photovoltaic (PV) modules may reach 80 Mt globally. The impacts could be mitigated by module recycling, repair and reuse; however, previous studies of PV circularity omit the consideration of critical social factors. Here we used an agent-based model to integrate social aspects with techno-economic factors, which provides a more realistic assessment of the circularity potential for previously studied interventions that assesses additional interventions that cannot be analysed using techno-economic analysis alone. We also performed a global sensitivity analysis using a machine-learning metamodel. We show that to exclude social factors underestimates the effect of lower recycling prices on PV material circularity, which highlights the relevance of considering social factors in future studies. Interventions aimed at changing customer attitudes about used PV boost the reuse of modules, although used modules can only satisfy one-third of the US demand during 2020–2050, which suggests that reuse should be complemented by recycling.

到 2050 年，全球报废光伏 (PV) 模块的累积质量可能达到 80 Mt。可以通过模块回收、维修和再利用来减轻影响；然而，先前对光伏循环性的研究忽略了关键社会因素的考虑。在这里，我们使用基于代理的模型将社会方面与技术经济因素相结合，这为先前研究的干预措施的循环潜力提供了更现实的评估，这些干预措施评估了仅使用技术经济分析无法分析的其他干预措施。我们还使用机器学习元模型进行了全局敏感性分析。我们表明，排除社会因素低估了较低的回收价格对光伏材料循环性的影响，这突出了在未来研究中考虑社会因素的相关性。