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How reliable are forecasts of future energy technology costs?

Current forecasting methods underestimate technological progress in energy technologies, predicting higher costs than actually observed, a new study reports.

Digital Economy
Climate Change and Environment

Our vision of a carbon-neutral and sustainable global economy can only be realised through robust and cost-effective policies and business plans. To design such strategies, experts need reliable estimates of how much technologies that might play a key role in the energy transition will cost in the future. However, the innovation process is complex, involves different actors and is consequently hard to predict. So, how dependable are current forecasts of future energy technology costs? In search of an answer, researchers supported by the EU-funded INNOPATHS, COP21 RIPPLES and 2D4D projects conducted the first systematic comparison of different technology cost forecasts produced by expert- and model-based methods. They analysed how well the different forecasting methods performed by generating probabilistic technology cost forecasts from various dates in the past and then comparing them with observed costs in 2019. As senior researcher Prof. Laura Diaz Anadon of the University of Cambridge states in a ‘EurekAlert!’ news release, “[s]uch a comparison is essential to ensure researchers and analysts have more empirically-grounded evidence in integrated assessment models, cost benefit analyses and broader policy design efforts.” The study used one expert-based method – expert elicitations (EE), or structured surveys of experts – and four model-based methods that model costs either as a function of deployment or as a function of time. The researchers applied these methods to six technologies: nuclear electricity, photovoltaic modules, onshore wind, offshore wind, alkaline electrolysis cells, and proton exchange membrane electrolysis cells.

Model-based methods perform better

Study co-author Dr Rupert Way of the University of Oxford explained the results: “[T]he comparison of expert- and model-based forecasts with observed 2019 costs over a short time frame (a maximum of 10 years) shows that model-based approaches outperformed expert elicitations. More specifically, the 5th-95th percentile range of the four model-based approaches were much more likely to contain the observed value than that of EE forecasts. Among the model-based methods, some captured 2019 observed costs more often than others.” Lead author Dr Jing Meng of University College London added that “the 2019 medians of model-based forecasts were closer to the average observed 2019 cost for five out of the six technologies.” However, all the methods were found to underestimate technological progress in practically all technologies. This could be attributed to the structural changes driven by new climate and energy policies and social and market forces. “[I]n five out of six technologies analyzed, the methods produced 2019 cost forecast medians that were higher than the observed 2019 costs,” noted co-author Prof. Elena Verdolini of the University of Brescia, Italy. “This indicates that the rate of progress in cost reduction has been higher than what both historical data and expert opinions predicted. But the extent to which this faster pace of progress compared to forecasts will continue (or not) in the future remains to be seen.” The research findings were published in the ‘Proceedings of the National Academy of Sciences of the United States of America’. The study funded by INNOPATHS (Innovation pathways, strategies and policies for the Low-Carbon Transition in Europe), COP21 RIPPLES (COP21: Results and Implications for Pathways and Policies for Low Emissions European Societies), and 2D4D (Disruptive Digitalization for Decarbonization) highlights the need for further research that compares forecasting methods across a wider range of technologies. For more information, please see: INNOPATHS project website COP21 RIPPLES project website 2D4D project website


INNOPATHS, COP21 RIPPLES, 2D4D, energy, technology, expert elicitation, model-based forecast, energy transition, energy technology cost

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