Learning Curves in Prospective Life Cycle Assessment.

Area of Science:

• Environmental Science
• Technology Assessment
• Sustainable Engineering

Background:

• Prospective life cycle assessments (LCAs) benefit from environmental learning curves for predicting technological environmental footprints.
• Current methods lack clear guidance on integrating these learning curves into prospective LCAs.
• Background process changes, like electricity grid decarbonization, are crucial but often simplified.

Purpose of the Study:

• To propose and demonstrate a method for integrating environmental learning curves into prospective LCAs.
• To combine process-specific learning curves with integrated assessment model (IAM) projections.
• To enable robust analysis of future environmental footprints for emerging and established technologies.

Main Methods:

• Combining process-specific environmental learning curves for key technology parameters.
• Incorporating projections from integrated assessment models for background processes (e.g., grid decarbonization).
• Applying the integrated method to a case study of monocrystalline silicon photovoltaic panel production.

Main Results:

• Environmental footprints of photovoltaic panels are projected to reduce by 21-80% between 2020 and 2050.
• Footprint reductions are driven by background changes (decarbonization) or process-specific learning curves, depending on development trajectories.
• The method allows for process contribution, uncertainty, sensitivity, and flexibility in impact category assessments.

Conclusions:

• The proposed method effectively integrates environmental learning curves and IAMs for prospective LCA.
• It provides a synergistic approach to predict significant reductions in technology environmental footprints.
• The methodology is applicable to assessing emerging technologies and understanding drivers of environmental improvement.