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.

  • Analysis
  • Published:

Sources of uncertainty in long-term global scenarios of solar photovoltaic technology


The deployment of solar photovoltaic (PV) technology has consistently outpaced expectations over the past decade. However, long-term prospects for PV remain deeply uncertain, as recent global scenarios span two orders of magnitude in installed PV capacity by 2050. Here we systematically compile an ensemble of 1,550 scenarios from peer-reviewed and influential grey literature, including IPCC and non-IPCC scenarios, and apply a statistical learning framework to link scenario characteristics with foreseen PV outcomes. We show that a large portion of the uncertainty in the global scenarios is associated with general features such as the type of organization, energy model and policy assumptions, without referring to specific techno-economic assumptions. IPCC scenarios consistently project lower PV adoption pathways and higher capital costs than non-IPCC scenarios. We thus recommend increasing the diversity of models and scenario methods included in IPCC assessments to represent the multiple perspectives present in the PV scenario literature.

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

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the analysed scenario ensemble.
Fig. 2: Workflow of the analysis and data sources.
Fig. 3: Visualization of projected PV growth in the overall ensemble, grouped by indicators.
Fig. 4: PV cost data in the overall scenario ensemble.
Fig. 5: Scenario archetypes and text perspectives in the overall ensemble.
Fig. 6: Relative importance of scenario indicators for explaining PV growth, estimated using Shapley additive explanation values for the classification of scenarios into CAGR quintiles.

Similar content being viewed by others

Data availability

Data for IPCC SR1.5 and AR5 scenarios are available through the International Institute for Applied Systems Analysis portal25,26. Data for non-IPCC scenarios are available in the original sources; metadata for these sources are provided in Supplementary Data 1.

Code availability

The code used for analysis in this study is available from the corresponding author upon request. A code notebook presenting key steps of the analysis is available for download74.


  1. Rogelj, J. et al. in Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) Ch. 2 (IPCC, WMO, 2018).

  2. Davis, S. J. et al. Net-zero emissions energy systems. Science 360, eaas9793 (2018).

    Article  Google Scholar 

  3. Global Trends in Renewable Energy Investment 2019 (Frankfurt School-UNEP Centre/BNEF, 2019).

  4. World Energy Outlook 2010 (International Energy Agency, 2010).

  5. Arvizu, D. et al. in Special Report on Renewable Energy Sources and Climate Change Mitigation (eds Edenhofer, O. et al.) 34–212 (IPCC, Cambridge Univ. Press, 2011).

  6. Bogdanov, D. et al. Radical transformation pathway towards sustainable electricity via evolutionary steps. Nat. Commun. 10, 1077 (2019).

    Article  Google Scholar 

  7. Haegel, N. M. et al. Terawatt-scale photovoltaics: transform global energy. Science 364, 836–838 (2019).

    Article  CAS  Google Scholar 

  8. Jacobson, M. Z. et al. Impacts of green new deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries. One Earth 1, 449–463 (2019).

    Article  Google Scholar 

  9. Hansen, J. P., Narbel, P. A. & Aksnes, D. L. Limits to growth in the renewable energy sector. Renew. Sustain. Energy Rev. 70, 769–774 (2017).

    Article  CAS  Google Scholar 

  10. World Energy Outlook 2019 (International Energy Agency, 2019).

  11. Future of Solar Photovoltaic: Deployment, Investment, Technology, Grid Integration and Socio-Economic Aspects (A Global Energy Transformation) (International Renewable Energy Agency, 2019).

  12. DeCarolis, J. et al. Formalizing best practice for energy system optimization modelling. Appl. Energy 194, 184–198 (2017).

    Article  Google Scholar 

  13. Strachan, N., Fais, B. & Daly, H. Reinventing the energy modelling–policy interface. Nat. Energy 1, 16012 (2016).

    Article  Google Scholar 

  14. Trutnevyte, E., Guivarch, C., Lempert, R. & Strachan, N. Reinvigorating the scenario technique to expand uncertainty consideration. Climatic Change 135, 373–379 (2016).

    Article  Google Scholar 

  15. Craig, P. P., Gadgil, A. & Koomey, J. G. What can history teach us? A retrospective examination of long-term energy forecasts for the United States. Annu. Rev. Energy Environ. 27, 83–118 (2002).

    Article  Google Scholar 

  16. Morgan, M. G. & Keith, D. W. Improving the way we think about projecting future energy use and emissions of carbon dioxide. Climatic Change 90, 189–215 (2008).

    Article  CAS  Google Scholar 

  17. Konrad, K., Van Lente, H., Groves, C. & Selin, C. in The Handbook of Science and Technology Studies (eds Felt, U. et al.) 465–493 (MIT Press, 2017).

  18. Wüstenhagen, R. & Menichetti, E. Strategic choices for renewable energy investment: conceptual framework and opportunities for further research. Energy Policy 40, 1–10 (2012).

    Article  Google Scholar 

  19. Creutzig, F. et al. The underestimated potential of solar energy to mitigate climate change. Nat. Energy 2, 17140 (2017).

    Article  Google Scholar 

  20. Pietzcker, R., Stetter, D., Manger, S. & Luderer, G. Using the Sun to decarbonize the power sector: the economic potential of photovoltaics and concentrating solar power. Appl. Energy 135, 704–720 (2014).

    Article  Google Scholar 

  21. Cole, W. et al. Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective (National Renewable Energy Laboratory, 2017).

  22. Pietzcker, R. et al. System integration of wind and solar power in integrated assessment models: a cross-model evaluation of new approaches. Energy Econ. 64, 583–599 (2017).

    Article  Google Scholar 

  23. Trutnevyte, E., McDowall, W., Tomei, J. & Keppo, I. Energy scenario choices: insights from a retrospective review of UK energy futures. Renew. Sustain. Energy Rev. 55, 326–337 (2016).

    Article  Google Scholar 

  24. Carrington, G. & Stephenson, J. The politics of energy scenarios: are International Energy Agency and other conservative projections hampering the renewable energy transition? Energy Res. Soc. Sci. 46, 103–113 (2018).

    Article  Google Scholar 

  25. Huppmann, D. et al. IAMC 1.5°C Scenario Explorer and Data Hosted by IIASA (IAMC and IIASA, 2018);

  26. IAMC AR5 Scenario Database (IAMC and IIASA, 2014).

  27. Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  Google Scholar 

  28. Wilson, C., Grubler, A., Bauer, N., Krey, V. & Riahi, K. Future capacity growth of energy technologies: are scenarios consistent with historical evidence? Climatic Change 118, 381–395 (2013).

    Article  Google Scholar 

  29. O’Neill, B. C. & Desai, M. Accuracy of past projections of US energy consumption. Energy Policy 33, 979–993 (2005).

    Article  Google Scholar 

  30. O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122, 387–400 (2014).

    Article  Google Scholar 

  31. Breyer, C. et al. On the role of solar photovoltaics in global energy transition scenarios. Prog. Photovoltaics 25, 727–745 (2017).

    Article  Google Scholar 

  32. Weber, C. et al. Mitigation scenarios must cater to new users. Nat. Clim. Change 8, 845–848 (2018).

    Article  Google Scholar 

  33. Gilbert, A. Q. & Sovacool, B. K. Looking the wrong way: bias, renewable electricity, and energy modelling in the United States. Energy 94, 533–541 (2016).

    Article  Google Scholar 

  34. van Vuuren, D. P. et al. What do near-term observations tell us about long-term developments in greenhouse gas emissions? Climatic Change 103, 635–642 (2010).

    Article  Google Scholar 

  35. Wilson, C. et al. Evaluating Process-Based Integrated Assessment Models of Climate Change Mitigation (IIASA, 2017).

  36. Trutnevyte, E. Does cost optimization approximate the real-world energy transition? Energy 106, 182–193 (2016).

    Article  Google Scholar 

  37. Lempert, R. J. Values and uncertainty. Nat. Clim. Change 5, 914–915 (2015).

    Article  Google Scholar 

  38. Wilson, C. et al. Granular technologies to accelerate decarbonization. Science 368, 36–39 (2020).

    Article  CAS  Google Scholar 

  39. McCollum, D. L. et al. Improving the behavioral realism of global integrated assessment models: an application to consumers’ vehicle choices. Transportation Res. D 55, 322–342 (2017).

    Article  Google Scholar 

  40. Breyer, C. in Future Energy 3rd edn (ed. Letcher, T. M.) 727–756 (Elsevier, 2020).

  41. Newell, R. G. & Raimi, D. Global Energy Outlook Comparison Methods: 2019 Update (Resources for the Future, 2019).

  42. World Energy Insights Brief 2019: Global Energy Scenarios Comparison Review (World Energy Council, 2019).

  43. Rohatgi, A. WebPlotDigitizer (2019).

  44. Africa Energy Outlook 2019 (International Energy Agency, 2019).

  45. World Energy Outlook 2011 (International Energy Agency, 2011).

  46. Exchange Rates (Indicator). OECD iLibrary (OECD, 2020).

  47. McKinney, W. in Proceedings of the 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 51–56 (SciPy, 2010).

  48. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  49. Densing, M., Panos, E. & Hirschberg, S. Meta-analysis of energy scenario studies: example of electricity scenarios for Switzerland. Energy 109, 998–1015 (2016).

    Article  Google Scholar 

  50. Krey, V. Global energy–climate scenarios and models: a review. WIREs Energy Environ. 3, 363–383 (2014).

    Article  Google Scholar 

  51. Li, F. G. N., Trutnevyte, E. & Strachan, N. A review of socio-technical energy transition (STET) models. Technol. Forecast. Soc. Change 100, 290–305 (2015).

    Article  Google Scholar 

  52. Seabold, S. & Perktold, J. in Proceedings of the 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 92–96 (SciPy, 2010).

  53. Lucińska, M. & Wierzchoń, S. T. in Computer Information Systems and Industrial Management (eds Cortesi, A. et al.) 254–265 (Springer, 2012).

  54. Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).

    Article  Google Scholar 

  55. Pedregosa, F. et al. scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  56. Steyvers, M. & Griffiths, T. in Handbook of Latent Semantic Analysis (eds Landauer, T. K. et al.) 424–440 (Routledge, 2007).

  57. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).

    Google Scholar 

  58. Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).

    Article  CAS  Google Scholar 

  59. Bakharia, A. Interactive Content Analysis: Evaluating Interactive Variants of Non-Negative Matrix Factorisation and Latent Dirichlet Allocation as Qualitative Content Analysis Aids. PhD thesis, Queensland Univ. Technology (2014).

  60. Řehůřek, R. & Sojka, P. in Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (eds Calzolari, N. et al.) 45–50 (ELRA, 2010).

  61. Honnibal, M. & Montani, I. spaCy 2: natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. spaCy (2017).

  62. McCallum, A. K. MALLET: A Machine Learning for Language Toolkit (2002).

  63. Röder, M., Both, A. & Hinneburg, A. in Proceedings of the 8th ACM International Conference on Web Search and Data Mining (eds Cheng, X. et al.) 399–408 (Association for Computing Machinery, 2015).

  64. Griffiths, T. L. & Steyvers, M. Finding scientific topics. Proc. Natl Acad. Sci. USA 101, 5228–5235 (2004).

    Article  CAS  Google Scholar 

  65. Heinrich, G. Parameter Estimation for Text Analysis (vsonix GmbH and University of Leipzig, 2005).

  66. Eker, S., Rovenskaya, E., Obersteiner, M. & Langan, S. Practice and perspectives in the validation of resource management models. Nat. Commun. 9, 5359 (2018).

    Article  CAS  Google Scholar 

  67. Torgerson, W. S. Multidimensional scaling: I. Theory and method. Psychometrika 17, 401–419 (1952).

    Article  Google Scholar 

  68. Sievert, C. & Shirley, K. in Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces (eds Chuang, J. et al.) 63–70 (Association for Computational Linguistics, 2014).

  69. Taddy, M. in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS) (eds Lawrence, N. et al.) 1184–1193 (PMLR, 2012).

  70. Chen, T. & Guestrin, C. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (eds Krishnapuram, B. et al.) 785–794 (Association for Computing Machinery, 2016).

  71. Altmann, A., Tolosi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature importance measure. Bioinformatics 26, 1340–1347 (2010).

    Article  CAS  Google Scholar 

  72. Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T. & Zeileis, A. Conditional variable importance for random forests. BMC Bioinformatics 9, 307 (2008).

    Article  Google Scholar 

  73. Lundberg, S. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).

    Article  Google Scholar 

  74. Jaxa-Rozen, M. & Trutnevyte, E. Scenario analysis workflow for the manuscript ‘Sources of uncertainty in long-term global scenarios of solar photovoltaic technology’. Zenodo (2020).

Download references


This work received funding from the University of Geneva as well as from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 821124 (NAVIGATE). We thank G. Luderer and L. Hirt for their helpful comments on the analysis.

Author information

Authors and Affiliations



M.J.-R. and E.T. designed the research; M.J.-R. performed the analysis; M.J.-R. and E.T. wrote the article.

Corresponding author

Correspondence to Marc Jaxa-Rozen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Wesley Cole, Felix Creutzig, Sibel Eker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Tables 1–4 and Figs. 1–18.

Supplementary Data 1

References for non-IPCC scenarios included in the analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaxa-Rozen, M., Trutnevyte, E. Sources of uncertainty in long-term global scenarios of solar photovoltaic technology. Nat. Clim. Chang. 11, 266–273 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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