Learning rates are a central concept in energy system models and integrated assessment models, as they allow researchers to project the future costs of new technologies and to optimize energy system costs. Here we argue that exchange rate fluctuations are an important, but thus far overlooked, determinant of the learning-rate variance observed in the literature. We explore how empirically observed global learning rates depend on where technologies are installed and which currency is used to calculate the learning rate. Using global data of large-scale photovoltaic (≥5 MW) plants, we show that the currency choice can result in learning-rate differences of up to 16 percentage points. We then introduce an adjustment factor to correct for the effect of exchange rate and market focus fluctuations and discuss the implications of our findings for innovation scholars, energy modellers and decision makers.
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The data that support the findings of this study are available from BNEF, but restrictions apply to the availability of these data, which were used under license for the current study and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of BNEF.
The source code (in R) and supporting documents are available at Zenodo (https://doi.org/10.5281/zenodo.3553796) and can be freely used and manipulated by all users, without restriction, under the MIT licence.
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J.L., L.O. and M.M. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 715132). We thank F. Egli and M. Meyer for helpful comments on earlier drafts of the paper.
The authors declare no competing interests.
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Lilliestam, J., Melliger, M., Ollier, L. et al. Understanding and accounting for the effect of exchange rate fluctuations on global learning rates. Nat Energy 5, 71–78 (2020). https://doi.org/10.1038/s41560-019-0531-y
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