Empirically grounded technology forecasts and the energy transition

Rapidly decarbonizing the global energy system is critical for address-ing climate change, but concerns about costs have been a barrier to im-plementation. Most energy-economy models have historically underes-timated deployment rates for renewable energy technologies and overestimated their costs. These issues have driven calls for alternative approaches and more reliable technology forecasting methods. Here, we use an approach based on probabilistic cost forecasting 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 and explore how technology cost uncertainty propagates through to sys-tem 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 account-ing for climate damages or co-benefits of climate policy.

Automated summary

Rapidly decarbonizing the global energy system is crucial for addressing climate change. This study presents probabilistic cost forecasts for solar energy, wind energy, batteries, and electrolyzers, which indicate that a swift transition to green energy can result in substantial savings.