Technology costRecent Technology Cost Forecasts Underestimate Pace of Technological Change
A comparison of observed global energy technology costs, with forecasts generated by models and forecasts predicted by human experts, showed that both forecasting methods underestimated cost reductions. This suggests that decisions based on forecasts may be overestimating the cost of climate mitigation and points to the need to further improve forecasting methods.
A team of researchers from the University of Cambridge, University College London, University of Oxford, and University of Brescia/RFF-CMCC European Institute on Economics and the Environment carried out the first systematic analysis of the relative performance of probabilistic cost forecasts from expert-based methods and model-based methods.
They specifically focused on one expert-based method — expert elicitations — and four model-based methods which model costs either as a function of cumulative installed capacity or as a function of time. The results of this comparison are published in PNAS.
Accurately forecasting energy technology costs is a requirement for the design of robust and cost-effective decarbonization policies and business plans. The future of these and other technologies is notoriously hard to predict because the process by which technology is conceived, developed, codified, and deployed is part of a complex adaptive system and is made up of interconnected actors and institutions.
A range of probabilistic forecasting methods have been developed and used to generate estimates of future technology costs. Two high-level types of approaches have been most often used to generate quantitative forecasts: expert-based and model-based approaches. Broadly speaking, expert-based approaches involve different ways of obtaining information from knowledgeable individuals who may have differing opinions and/or knowledge about the relative importance of various drivers of innovation and how they may evolve. Experts make implicit judgments about the underlying drivers of change when producing their forecasts and can take into account both public information about observed costs as well as information that may not yet be widely available or codified. Expert-based approaches are often the only source of information available to analysts when data, on a given technology, has not yet been collected—as is generally the case for emerging technologies.
By contrast, model-based approaches explicitly use one or more variables from available observed data to approximate the impact of the full set of drivers of innovation on technology costs, implicitly assuming that the rate of change in the past will be the best predictor of the rate of change in the future.
“The increased availability of information on future energy technology costs allowed us to conduct the first systematic comparison of the relative performance of probabilistic technology cost forecasts generated by different expert-based and model-based methodologies with observed costs” notes senior and corresponding author Prof. Diaz Anadon, Professor of Climate Change Policy at the University of Cambridge and Director of the University’s Centre for Environment, Energy and Natural Resource
