Exploring PV Circularity by Modeling Socio-Technical Dynamics of Modules' End-of-Life Management: Preprint

Research output: Contribution to conferencePaper


The circular economy (CE) tackles environmental and resource scarcity issues by maximizing value retention in the economy. The concept implies design strategies such as reducing the use of materials or improving products’ durability and end-of-life (EOL) strategies such as reusing products and components and recycling materials. With an estimated 80 million tons of global cumulative EOL photovoltaic (PV) modules, applying CE principles to the PV industry could alleviate resource scarcity issues while also providing economic benefits. However, transitioning to a CE may imply changes in organizations and consumer behaviors. In this context, assessment of CE strategies may require accounting for behavioral change, a requirement that methods from complex system science such as agent-based modeling meet. Thus, this paper uses an agent-based modeling (ABM) approach to study circularity in the photovoltaics supply chain. Four types of agents are represented in the ABM: PV owners, installers, recyclers, and manufacturers. Moreover, five possible EOL options – including three CE strategies – are modeled. Departing from traditional techno-economic analysis, the model includes techno-economic factors as well as social factors to model EOL management decisions. Results show that each dollar decrease in the recycling fees improves the recycling rate by roughly 1.1%. However, excluding social factors underestimates the effect that lower recycling prices have on material circularity.
Original languageAmerican English
Number of pages6
StatePublished - 2021
Event48th IEEE Photovoltaic Specialists Conference (PVSC 48) -
Duration: 20 Jun 202125 Jun 2021


Conference48th IEEE Photovoltaic Specialists Conference (PVSC 48)

Bibliographical note

See NREL/CP-6A20-81264 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-6A20-79042


  • agent-based modeling
  • circular economy
  • circular strategies
  • machine learning
  • socio-technical systems


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