A framework for technological learning in the supply chain: A case study on CdTe photovoltaics. (1st May 2016)

• Record Type: Journal Article
• Authors: Bergesen, Joseph D.; Suh, Sangwon
• Is Part Of: Applied energy. Volume 169(2016)
• Page Start: 721
• Page End: 728
• Publication Date: 2016-05-01
• Subjects: Life cycle assessment (LCA) -- Learning curve -- Technological learning -- Photovoltaics -- Supply chain; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.02.013
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: A framework for technological learning in the supply chain is proposed. This framework separates learning effects on value added and intermediate inputs. Supply-chain learning can project both changing environmental impacts and costs. Learning upstream in the supply chain can influence observed learning rates. An example for CdTe photovoltaics illustrates how this framework can be implemented. Abstract: Accounting for technological changes and innovation is important when assessing the implications of rapidly-developing greenhouse gas (GHG) mitigation technologies. Technological learning curves have been commonly used as a tool to understand technological change as a function of cumulative production. Traditional learning curve approaches, however, do not distinguish the direct and upstream, supply chain technological changes by which cost reductions are achieved. While recent advances in learning curves have focused on distinguishing the different physical and economic drivers of learning, forecasted technological changes have not been applied to estimate the potential changes in the environmental performance of a technology. This article illustrates how distinguishing the different effects of technological learning throughout the supply chain can help assess the changing costs, environmental impacts and natural resource implications of technologies as they develop. We propose a mathematical framework to distinguish the effects of learning on the direct inputs to a technology from the effects of learning on value added, and we incorporate those effects throughout the supply chain of a technology using a life cycle assessment (LCA) framework. An example for cadmium telluride (CdTe) photovoltaics (PV) illustrates how the proposed framework can be implemented. Results show that that life cycle GHG emissions can decrease at least 40% and costs can decrease at least 50% as cumulative production of CdTe reaches 100 GW. Technological learning in supply chain processes can further reduce emissions and costs by up to 1–2%. Lastly, we discuss the implications of using this new technological learning framework in the long-term assessment of the costs, environmental impacts and resource requirements of technologies using life-cycle assessment.