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Quantifying the cost savings of global solar photovoltaic supply chains

Abstract

Achieving carbon neutrality requires deploying renewable energy at unprecedented speed and scale1,2, yet countries sometimes implement policies that increase costs by restricting the free flow of capital, talent and innovation in favour of localizing benefits such as economic growth, employment and trade surpluses3,4. Here we assess the cost savings from a globalized solar photovoltaic (PV) module supply chain. We develop a two-factor learning model using historical capacity, component and input material price data of solar PV deployment in the United States, Germany and China. We estimate that the globalized PV module market has saved PV installers US$24 (19–31) billion in the United States, US$7 (5–9) billion in Germany and US$36 (26–45) billion in China from 2008 to 2020 compared with a counterfactual scenario in which domestic manufacturers supply an increasing proportion of installed capacities over a ten-year period. Projecting the same scenario forwards from 2020 results in estimated solar module prices that are approximately 20–25 per cent higher in 2030 compared with a future with globalized supply chains. International climate policy benefits from a globalized low-carbon value chain4, and these results point to the need for complementary policies to mitigate welfare distribution effects and potential impacts on technological crowding out.

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Fig. 1: Annual solar PV cell production by origin, 2010–2021.
Fig. 2: Comparison of estimated solar PV module prices under global versus national market scenarios in China (2007–2020), and Germany and the United States (2006–2020).
Fig. 3: Estimated annual savings from deployed annual solar PV modules using global versus national market scenarios in China, Germany and the United States (2008–2020).
Fig. 4: Comparison of projected solar PV module prices (2020–2030) using global versus national market scenarios in China, Germany and the United States.

Data availability

We compile a comprehensive dataset of historical solar capacity and component price globally and in the United States, China and Germany. All data are publicly available at https://doi.org/10.5281/zenodo.6989075. Global installed PV capacity and price data are from the open database of the International Renewable Energy Agency (IRENA)18. For the United States, solar capacity data are from the Solar Energy Industries Association (SEIA)48, and module prices are assembled from two sources: the Lawrence Berkeley National Laboratory (LBNL)49 and the National Renewable Energy Laboratory (NREL)16. The LBNL data are used for the 2006–2018 period as this series ends in 2018, and the NREL data are used for 2019–2020 to extend the series to 2020. This was chosen because the NREL data only start in 2010, and thus the LBNL series covers a broader range (Extended Data Figs. 24). For China, both the installed capacity and module price data (2007–2018) were extracted from reports and presentations by the Energy Research Institute (ERI)50, and the 2019–2020 data were extracted from China Photovoltaic Industry Association where the historical data are identical to that of ERI51. For Germany, capacity data are from IRENA, and module price data were extracted from Fraunhofer ISE52. All prices are in 2020 US$, and we adopt inflation adjustments using the IMF (https://data.imf.org/) and exchange rates from the Federal Reserve Bank (https://www.federalreserve.gov/releases/h10/hist/). Source data are provided with this paper.

Code availability

All of the code used to process the data and produce all analyses and figures is publicly available at https://doi.org/10.5281/zenodo.6989075.

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Acknowledgements

We thank G. Nemet, G. Barbose, N. Darghouth, H. Bahar, A. Jäger-Waldau and P. Mints for their help in data sharing and answering our data questions; and D. Hart and the Information Technology and Innovation Foundation for hosting the Energy Innovation and Climate-tech ‘Boot Camp’ for early career scholars (funded by the Alfred P. Sloan Foundation) where many of the initial conversations around this study began.

Author information

Authors and Affiliations

Authors

Contributions

G.H. initiated the research idea. J.P.H. led data curation. M.R.D. wrote the initial analysis code, and J.P.H. wrote the final analysis and visualization code. All authors contributed equally to conceptualization and writing.

Corresponding author

Correspondence to Gang He.

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The authors declare no competing interests.

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Nature thanks Aleh Cherp, M. Chiesa, Paul Drummond and Yueming Qiu for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Comparison of projected annual savings (2020–2030) using global versus national market scenarios in China, Germany and the United States.

Savings are calculated by multiplying the installed national capacity in each year with the difference between the modelled prices from the national and global markets scenarios. Error bars represent 95% confidence intervals computed via simulation.

Extended Data Fig. 2 Comparison of the US installed solar PV capacity by type and data source.

The data largely agree between NREL16 and SEIA48. However, SEIA data are updated to 2020 and therefore are used in this study.

Extended Data Fig. 3 Comparison of the US cumulative installed solar PV capacity by data source.

The data largely agree between NREL16 and SEIA48 while the data from IRENA18 suggest slightly lower installed capacities in the last five years.

Extended Data Fig. 4 Comparison of the US solar PV module prices by data source.

We used the LBNL data49 for the 2006–2018 period in this study as this series ends in 2018, as well as the NREL data16 for 2019–2020 to extend the series to 2020. We opted for this because the NREL data only start in 2010, and thus the LBNL series covers a broader range.

Extended Data Fig. 5 Relationship between λ and the proportion of national to global cumulative installed capacity (2006–2020).

The same value of λ does not translate to the same proportion of national learning for each country. For example, if λ = 0.4, then the proportion of national learning is 15% in the United States, 44% in China and 40% in Germany.

Extended Data Fig. 6 Historical global silicon prices (1980–2020)8.

Silicon is a key material input but is not directly linked to learning. Silicon prices experienced a major spike from US$171 per kg in 2006 to a peak at US$395 per kg in 2008, which could influence module prices notably, so we include this in our two-factor learning model.

Extended Data Table 1 Estimated learning model coefficients
Extended Data Table 2 Estimated learning model coefficients from alternative model 1, which includes an additional covariate for cumulative national module production capacity
Extended Data Table 3 Estimated learning model coefficients from alternative model 2, which includes an additional covariate for cumulative national installed capacity
Extended Data Table 4 Estimated learning model coefficients from alternative model 3, which includes an additional covariate for global average plant size

Supplementary information

Source data

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Helveston, J.P., He, G. & Davidson, M.R. Quantifying the cost savings of global solar photovoltaic supply chains. Nature (2022). https://doi.org/10.1038/s41586-022-05316-6

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