Abstract
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.
Original language | American English |
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Pages (from-to) | 913-924 |
Number of pages | 12 |
Journal | Nature Energy |
Volume | 6 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2021 |
Bibliographical note
Publisher Copyright:© 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
NREL Publication Number
- NREL/JA-6A20-78430
Keywords
- agent-based modeling
- circular economy
- circular strategies
- machine learning
- socio-technical systems