Understanding and accounting for the effect of exchange rate fluctuations on global learning rates

Lilliestam, Johan; Melliger, Marc; Ollier, Lana; Schmidt, Tobias S.

doi:10.1038/s41560-019-0531-y

Cited by 17 publications

(9 citation statements)

References 41 publications

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“…In comparing the UK curves with those in the literature, the influence of currency exchange should be considered in interpreting the comparative analysis. The effect of exchange rate fluctuations and a method to correct the effect have been described for the case of large-scale photovoltaic plants (Lilliestam et al, 2020). Nevertheless, the method was proposed for a global experience rate, and thus less applicable for local rates focused in this study.…”

Section: Study Limitationsmentioning

confidence: 99%

Experience rates of low-carbon domestic heating technologies in the United Kingdom

Renaldi¹,

Hall²,

Jamasb³

et al. 2021

Energy Policy

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This paper presents the experience curves of low-carbon domestic heating technologies in the United Kingdom between 2010 and 2019. The deployment of these technologies has been acknowledged as one of the main actions toward decarbonising the heating sector. In the UK, several deployment oriented policies have been implemented, such as the Renewable Heat Incentive (RHI). In this study, we focus on the following domestic heating technologies: air-source heat pumps, ground-source heat pumps, solar thermal collectors, and biomass boilers. Condensing combination gas boilers are also included to act as the baseline/incumbent technology. Using UK installation cost data for 2010 to 2019, we found that low-carbon heating technologies had experience rates of; air source heat pumps -2.3± 5%, ground source heat pumps -0.8±4%, biomass boilers 0.1±2%, and solar thermal 13±5%, all significantly lower than the reported learning rates of similar technologies in the literature. Furthermore, we found that gas boilers have reached the floor price at approximately £30/kW. The resulting experience rates can be used in energy economics models and to inform policymakers in developing further deployment programs.

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Section: Study Limitationsmentioning

confidence: 99%

Experience rates of low-carbon domestic heating technologies in the United Kingdom

Renaldi¹,

Hall²,

Jamasb³

et al. 2021

Energy Policy

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“…Lilliestam et al. ( 2020 ), for instance, find that the choice of currency can lead to differences in the estimated learning rate of up to 16 percentage points.…”

Section: Dynamics Of System Pricesmentioning

confidence: 99%

Cost Dynamics of Clean Energy Technologies

Glenk

Meier

Reichelstein

2021

Schmalenbach J Bus Res

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The pace of the global decarbonization process is widely believed to hinge on the rate of cost improvements for clean energy technologies, in particular renewable power and energy storage. This paper adopts the classical learning-by-doing framework of Wright (1936), which predicts that cost will fall as a function of the cumulative volume of past deployments. We first examine the learning curves for solar photovoltaic modules, wind turbines and electrolyzers. These estimates then become the basis for estimating the dynamics of the life-cycle cost of generating the corresponding clean energy, i.e., electricity from solar and wind power as well as hydrogen. Our calculations point to significant and sustained learning curves, which, in some contexts, predict a much more rapid cost decline than suggested by the traditional 80% learning curve. Finally, we argue that the observed learning curves for individual clean energy technologies reinforce each other in advancing the transition to a decarbonized energy economy.

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“…Furthermore, currency fluctuations may have had a significant effect on the resulting parameter estimates. Lilliestam et al (2020), for instance, find that the choice of currency choice can lead to differences in the estimated learning rates of up to 16 percentage points.…”

Section: Solar Photovoltaic Modulesmentioning

confidence: 99%

Clean Energy Technologies: Dynamics of Cost and Price

2021

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The rapid transition to a decarbonized energy economy is widely believed to hinge on the rate of cost improvements for certain clean energy technologies, in particular renewable power and energy storage. This paper adopts the classical learning-by-doing framework of Wright (1936), which predicts cost (price) to fall as a function of the cumulative volume of past deployments. We examine the learning rates for key clean energy system components (e.g., solar photovoltaic modules) and the life-cycle cost of generating clean energy (e.g., wind energy and hydrogen obtained through electrolysis). Our calculations point to significant and sustained learning rates, which, in some contexts, are much faster than the traditional 20% learning rate observed in other industries. Finally, we argue that the observed learning rates for individual technologies reinforce each other in advancing the transition to a decarbonized energy economy.

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Understanding and accounting for the effect of exchange rate fluctuations on global learning rates

Cited by 17 publications

References 41 publications

Experience rates of low-carbon domestic heating technologies in the United Kingdom

Experience rates of low-carbon domestic heating technologies in the United Kingdom

Cost Dynamics of Clean Energy Technologies

Clean Energy Technologies: Dynamics of Cost and Price

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