ADIA Lab Seminar Series
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Energy Transition: Applying Universality and Predictability of Technology Diffusion to the Forecast.
J. Doyne Farmer
Rapidly decarbonizing 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 underestimated deployment rates for renewable energy technologies and overestimated their costs. We use an approach based on probabilistic cost forecasting methods that have been statistically validated with more than 50 technologies ranging from monasteries and missiles to canals and mobile phones and show that the shape of their S-curves is remarkably universal. Our probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers indicate that the renewable energy transition will very likely happen quicker than the conventional forecasts in displacing fossil fuels.
Market Entry of Emerging Clean Technologies: Case Studies
Samuel Mao
Improvements in solar panel efficiency and energy storage technologies are making clean solutions more cost-effective across industrial sectors. However, while technological progress has accelerated, numerous barriers still hinder widespread adoption. Successful market entry for emerging clean technologies requires identifying competitive advantages, understanding market dynamics, and developing a robust strategy. Using case studies, this presentation will explore how to establish a profitable niche for clean technologies often perceived as costly.
This second session of the 2025 ADIA Lab Seminar Series, entitled “Energy Transition: Applying Universality and Predictability of Technology Diffusion to the Forecast”, is hosted by ADIA Lab, Abu Dhabi Machine Learning, and the ADGM Academy Research Centre, continuing our mission to explore research shaping the future of sustainability, climate action, and the digital economy.
About the Speakers
J. Doyne Farmer is Director of Complexity Economics at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor at the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute. His work spans financial stability, sustainability, technological change, and economic simulation. A pioneer in complexity science, Prof. Farmer co-founded Prediction Company, a trading firm acquired by UBS, and previously led the Complex Systems Group at Los Alamos National Laboratory.
Samuel Mao is the Senior Director of Masdar Institute and Professor of Practice at Khalifa University. A Ph.D. graduate of UC Berkeley, he previously served as a scientist at Lawrence Berkeley National Laboratory and an adjunct professor at UC Berkeley. He has published 160+ journal articles, holds 80+ patents, and has 45,000 citations. His research on sustainable energy and advanced materials includes pioneering disorder-engineering (Science) and leading the first hybrid electric heavy-duty truck. He has advised the U.S. Department of Energy, co-chaired major conferences, and received the R&D 100 Award.