Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Quantifying the cost savings of global solar photovoltaic supply chains


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.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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 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 ( and exchange rates from the Federal Reserve Bank ( 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


  1. Net Zero by 2050 (IEA, 2021).

  2. Doerr, J. & Panchadsaram, R. Speed & Scale: An Action Plan for Solving Our Climate Crisis Now (Portfolio, 2021).

  3. Sarah L. et al. Industrial Policy, Trade, And Clean Energy Supply Chains (CSIS & BloombergNEF, 2021);

  4. Goldthau, A. & Hughes, L. Protect global supply chains for low-carbon technologies. Nature 585, 28–30 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Renewable Power Generation Costs in 2021 (IRENA, 2021);

  6. Helveston, J. & Nahm, J. China’s key role in scaling low-carbon energy technologies. Science 366, 794–796 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. World Energy Outlook 2020 (IEA, 2020).

  8. Cherp, A., Vinichenko, V., Tosun, J., Gordon, J. A. & Jewell, J. National growth dynamics of wind and solar power compared to the growth required for global climate targets. Nat. Energy 6, 742–754 (2021).

    Article  Google Scholar 

  9. Jäger-Waldau, A. Snapshot of photovoltaics—February 2022. EPJ Photovolt. 13, 9 (2022).

    Article  Google Scholar 

  10. Special Report on Solar PV Global Supply Chains (IEA, 2022);

  11. Nemet, G. F. How Solar Energy Became Cheap: A Model for Low-Carbon Innovation (Routledge, 2019).

  12. Atkinson, R. D. Why China needs to end its economic mercantilism. HuffPost (2008).

  13. Fact Sheet: President Biden Takes Bold Executive Action to Spur Domestic Clean Energy Manufacturing (The White House, 2022).

  14. Green, M. A. How did solar cells get so cheap? Joule 3, 631–633 (2019).

    Article  Google Scholar 

  15. Tillman, B. Red scare or red herring: how the “China Initiative” strategy for non-traditional collectors is stifling innovation in the United States. Seattle J. Technol. Environ. Innov. Law 11, 6 (2020).

  16. Fu, R., Feldman, D. & Margolis, R. U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018 (NREL, 2018);

  17. Nemet, G. F., Lu, J., Rai, V. & Rao, R. Knowledge spillovers between PV installers can reduce the cost of installing solar PV. Energy Policy 144, 111600 (2020).

    Article  Google Scholar 

  18. Renewable Energy Statistics 2021 (IRENA, 2021);

  19. 2021 Annual Technology Baseline (NREL, 2021);

  20. Surana, K., Doblinger, C., Anadon, L. D. & Hultman, N. Effects of technology complexity on the emergence and evolution of wind industry manufacturing locations along global value chains. Nat. Energy 5, 811–821 (2020).

    Article  Google Scholar 

  21. Feldman, D. & Margolis, R. H2 2020 Solar Industry Update (NREL, 2021);

  22. Chung, D., Horowitz, K. & Kurup, P. On the Path to SunShot: Emerging Opportunities and Challenges in U.S. Solar Manufacturing (NREL, 2016);

  23. Hart, D. The Impact of China’s Production Surge on Innovation in the Global Solar Photovoltaics Industry (ITIF, 2020);

  24. Sivaram, V., Dabiri, J. O. & Hart, D. M. The need for continued innovation in solar, wind, and energy storage. Joule 2, 1639–1642 (2018).

    Article  Google Scholar 

  25. Fuchs, E. & Kirchain, R. Design for location? The impact of manufacturing offshore on technology competitiveness in the optoelectronics industry. Manage. Sci. 56, 2323–2349 (2010).

    Article  MATH  Google Scholar 

  26. Abernathy, W. J. & Utterback, J. M., others. Patterns of industrial innovation. Technol. Rev. 80, 40–47 (1978).

    Google Scholar 

  27. Gort, M. & Klepper, S. Time paths in the diffusion of product innovations. Econ. J. 92, 630–653 (1982).

    Article  Google Scholar 

  28. Utterback, J. M. & Suárez, F. F. Innovation, competition, and industry structure. Res. Policy 22, 1–21 (1993).

    Article  Google Scholar 

  29. Utterback, J. M. Mastering the Dynamics of Innovation: How Companies Can Seize Opportunities in the Face of Technological Change (Harvard Business School, 1994).

  30. Agarwal, R. & Gort, M. The evolution of markets and entry, exit and survival of firms. Rev. Econ. Stat. 78, 489–498 (1996).

    Article  Google Scholar 

  31. Carvalho, M., Dechezleprêtre, A. & Glachant, M. Understanding the Dynamics of Global Value Chains for Solar Photovoltaic Technologies. Economic Research Working Paper No. 40 (WIPO, 2017).

  32. Building Resilient Supply Chains, Revitalizing American Manufacturing, and Fostering Broad-Based Growth (The White House, 2021);

  33. Myslikova, Z. & Gallagher, K. S. Mission Innovation is mission critical. Nat. Energy 5, 732–734 (2020).

    Article  Google Scholar 

  34. Nahm, J. & Steinfeld, E. S. Scale-up nation: China’s specialization in innovative manufacturing. World Dev. 54, 288–300 (2014).

    Article  Google Scholar 

  35. Solar Supply Chain Traceability Protocol 1.0 (SIEA, 2021);

  36. McDonald, A. & Schrattenholzer, L. Learning rates for energy technologies. Energy Policy 29, 255–261 (2001).

    Article  Google Scholar 

  37. Nemet, G. F. Beyond the learning curve: factors influencing cost reductions in photovoltaics. Energy Policy 34, 3218–3232 (2006).

    Article  Google Scholar 

  38. Qiu, Y. & Anadon, L. D. The price of wind power in China during its expansion: technology adoption, learning-by-doing, economies of scale, and manufacturing localization. Energy Econ. 34, 772–785 (2012).

    Article  Google Scholar 

  39. Zheng, C. & Kammen, D. M. An innovation-focused roadmap for a sustainable global photovoltaic industry. Energy Policy 67, 159–169 (2014).

    Article  Google Scholar 

  40. Rubin, E. S., Azevedo, I. M. L., Jaramillo, P. & Yeh, S. A review of learning rates for electricity supply technologies. Energy Policy 86, 198–218 (2015).

    Article  Google Scholar 

  41. Yelle, L. E. The learning curve: historical review and comprehensive survey. Decis. Sci. 10, 302–328 (1979).

    Article  Google Scholar 

  42. Yu, C. F., van Sark, W. G. J. H. M. & Alsema, E. A. Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects. Renew. Sustain. Energy Rev. 15, 324–337 (2011).

    Article  Google Scholar 

  43. Zhang, C., Xie, L., Qiu, Y., (Lucy) & Wang, S. Learning-by-manufacturing and learning-by-operating mechanisms drive energy conservation and emission reduction in China’s coal power industry. Resour. Conserv. Recycl. 186, 106532 (2022).

    Article  Google Scholar 

  44. Lewis, J. I. & Nemet, G. F. Assessing learning in low carbon technologies: toward a more comprehensive approach. WIREs Clim. Change 12, e730 (2021).

    Article  Google Scholar 

  45. Meng, J., Way, R., Verdolini, E. & Anadon, L. D. Comparing expert elicitation and model-based probabilistic technology cost forecasts for the energy transition. Proc. Natl Acad. Sci. USA 118, e1917165118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kavlak, G., McNerney, J. & Trancik, J. E. Evaluating the causes of cost reduction in photovoltaic modules. Energy Policy 123, 700–710 (2018).

    Article  Google Scholar 

  47. Vartiainen, E., Masson, G., Breyer, C., Moser, D. & Román Medina, E. Impact of weighted average cost of capital, capital expenditure, and other parameters on future utility-scale PV levelised cost of electricity. Prog. Photovolt. Res. Appl. 28, 439–453 (2020).

    Article  Google Scholar 

  48. Solar Industry Research Data (SEIA, 2021);

  49. Barbose, G. L. & Darghouth, N. R. Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States—2019 Edition (LBNL, 2019);

  50. Wang, S. The Status and Perspectives of China’s PV Industry. Clean Energy Summit 2019. (2019).

  51. Wang, B. PV Industry in 2020, and Perspectives for 2021. China Photovoltaic Industry Association. (2020).

  52. Wirth, H. Recent Facts about Photovoltaics in Germany (Fraunhofer ISE, 2021);

Download references


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



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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Helveston, J.P., He, G. & Davidson, M.R. Quantifying the cost savings of global solar photovoltaic supply chains. Nature 612, 83–87 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing