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Speaker: Doyne Farmer, Director of Complexity Economics, Institute for New Economic Thinking, Oxford Martin School; Baillie Gifford Professor, Mathematical Institute, University of Oxford
New research from Doyne Farmer and others shows that wind, solar, and other renewables would deliver energy security to the world and save $12 trillion by 2050.
Abstract: Rapidly decarbonising the global energy system is critical for addressing climate change, but concerns about costs have been a barrier to implementation. Most energy-economy models have historically dramatically underestimated deployment rates for renewable energy technologies and overestimated their costs. Using methods that have been statistically validated by backtesting on more than 50 technologies, we generate probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers, conditional on deployment. We use these methods to estimate future energy system costs in three different scenarios. Compared to continuing with a fossil-fuel-based system, a rapid green energy transition will likely result in overall net savings of many trillions of dollars - even without accounting for climate damages or co-benefits of climate policy. This substantially changes the incentives to combat climate change, with important geopolitical consequences.
Whether attending in-person or virtually, please register in advance, and contact Chuck McKenney with any questions.
Room attendance is limited to the Harvard community. Seating availability is based on a first-come, first-served basis. The Zoom webinar is open to the public.
About the speaker:
J. Doyne Farmer is Director of Complexity Economics at the Institute for New Economic Thinking at the Oxford Martin School, and is the Baillie Gifford Professor at Mathematical Institute at the University of Oxford, as well as an External Professor at the Santa Fe Institute. His current research is in economics, including financial stability, sustainability, technological change and economic simulation. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research spans complex systems, dynamical systems, time series analysis and theoretical biology. He founded the Complex Systems Group at Los Alamos National Laboratory, and while a graduate student in the 70’s he built the first wearable digital computer, which was successfully used to predict the game of roulette.