Empirically grounded technology forecasts and the energy transition

TLDR

Rapid decarbonization is essential to combat climate change, yet cost concerns and historically inaccurate energy‑economy models have hindered progress, prompting a need for more reliable technology forecasts. The study applies statistically validated probabilistic cost forecasting to evaluate renewable technologies. Probabilistic cost forecasts for solar, wind, batteries, and electrolyzers were generated and used to estimate future energy system costs, examining how technology cost uncertainty propagates across three 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.

Abstract

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. 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 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.