Abstract
Energy technology learning curves are a foundational component of climate and energy models throughout the world. Unfortunately, due to a number of factors including different methods of producing the learning curves, the learning rates derived from these models differ drastically. This presentation outlines the various learning curves being used today, as well as recommendations for how to more accurately predict the performance of energy technologies with specific information for how policy measures factor into the learning curve.
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Brief Bio
Jessie Knapstein is part of UC Berkeley’s Renewable and Appropriate Energy Lab and an energy policy graduate student in the Energy and Resources Group as well as the Goldman School of Public Policy. Prior to UC Berkeley, Jessie worked for the US Department of Energy on energy efficient technology funding and proliferation. She is currently a Visiting Research Scholar with the UNSW Climate Change Research Centre where she is working with Donna Green and her UC Berkeley advisor, Dan Kammen, on the research presented.
