Learning Curves in Prospective Life Cycle Assessment.

Environmental learning curves have great potential to predict future changes in environmental footprints of technologies as part of prospective life cycle assessments. However, concrete guidance is currently missing on how to integrate environmental learning curves into prospective life cycle assessments. Here, we propose a method to combine (i) process-specific environmental learning curves for key technology parameters and (ii) projections from integrated assessment models to include relevant changes in background processes, such as expected decarbonization of the electricity grid. Our method enables process contribution analyses, uncertainty and sensitivity analyses, and flexibility in the assessment of various impact categories. Application of our proposed method is demonstrated in a case study assessing various environmental footprints of producing monocrystalline silicon photovoltaic panels. We showed that environmental footprints reduce 21-80% between 2020 and 2050 through a synergy of (i) and (ii). Footprint reductions were mostly driven by background changes when decarbonization is extensive, whereas process-specific environmental learning curves become the major driver for footprint reductions when developments in background processes follow a similar trajectory as charted by the past. Our method may also be used in the assessment of emerging technologies by applying process-specific environmental learning curves to mature parts of their supply chain.