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.

National growth dynamics of wind and solar power compared to the growth required for global climate targets


Climate mitigation scenarios envision considerable growth of wind and solar power, but scholars disagree on how this growth compares with historical trends. Here we fit growth models to wind and solar trajectories to identify countries in which growth has already stabilized after the initial acceleration. National growth has followed S-curves to reach maximum annual rates of 0.8% (interquartile range of 0.6–1.1%) of the total electricity supply for onshore wind and 0.6% (0.4–0.9%) for solar. In comparison, one-half of 1.5 °C-compatible scenarios envision global growth of wind power above 1.3% and of solar power above 1.4%, while one-quarter of these scenarios envision global growth of solar above 3.3% per year. Replicating or exceeding the fastest national growth globally may be challenging because, so far, countries that introduced wind and solar power later have not achieved higher maximum growth rates, despite their generally speedier progression through the technology adoption cycle.

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

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1: Phases, mechanisms, models and metrics of wind and solar power adoption.
Fig. 2: Wind and solar power take-off in the 60 largest electricity markets and the current generation worldwide.
Fig. 3: Historical deployment of wind and solar power and growth models for selected countries.
Fig. 4: Relationship between take-off year, G and ∆t.
Fig. 5: Maximum achieved growth rates versus those envisioned in climate stabilisation pathways.
Fig. 6: Illustration of feasibility spaces for solar and wind power deployment based on historical observations and growth models compared with the 1.5 and 2 °C pathways.

Data availability

We used data from IEA world energy balances53 for wind and solar power generation, data from refs. 53,78,79,80,81,82,83,84,85,86,87 for the independent variables used in the statistical analyses (see Supplementary Note 5 for details) and data from Huppmann et al.46 to calculate the growth rates in scenarios as reported in the Supplementary Data. Source data are provided with this paper.

Code availability

The code for curve fitting and the computational experiments is available at GitHub


  1. IPCC Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) 1–24 (WMO, 2018).

  2. Smil, V. Examining energy transitions: a dozen insights based on performance. Energy Res. Soc. Sci. 22, 194–197 (2016).

    Article  Google Scholar 

  3. Napp, T. et al. Exploring the feasibility of low-carbon scenarios using historical energy transitions analysis. Energies 10, 116 (2017).

    Article  Google Scholar 

  4. van Sluisveld, M. A. E. et al. Comparing future patterns of energy system change in 2 °C scenarios with historically observed rates of change. Glob. Environ. Change 35, 436–449 (2015).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  7. Jäger-Waldau, A. PV Status Report 2019 (Publications Office of the European Union, 2019).

  8. Grubb, M., Drummond, P. & Hughes, N. The Shape and Pace of Change in the Electricity Transition (UCL Institute for Sustainable Resource, 2020).

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

    Article  Google Scholar 

  10. Bond, K., Benham, H., Vaughan, E. & Butler-Sloss, S. The sky’s the limit. Carbon Tracker (23 April 2021);

  11. Griliches, Z. Hybrid corn: an exploration in the economics of technological change. Econometrica 25, 501–522 (1957).

    Article  Google Scholar 

  12. Hägerstrand, T. Innovation Diffusion as a Spatial Process (Univ. Chicago Press, 1967).

  13. Grubler, A. The Rise and Fall of Infrastructures: Dynamics of Evolution and Technological Change (Physica, 1990).

  14. Madsen, D. N. & Hansen, J. P. Outlook of solar energy in Europe based on economic growth characteristics. Renew. Sustain. Energy Rev. 114, 109306 (2019).

    Article  Google Scholar 

  15. Gosens, J., Hedenus, F. & Sandén, B. A. Faster market growth of wind and PV in late adopters due to global experience build-up. Energy 131, 267–278 (2017).

    Article  Google Scholar 

  16. Comin, D. & Mestieri, M. If technology has arrived everywhere, why has income diverged? Am. Econ. J. Macroecon. 10, 137–178 (2018).

    Article  Google Scholar 

  17. Fankhauser, S. & Jotzo, F. Economic growth and development with low-carbon energy. WIREs Clim. Change 9, e495 (2017).

    Google Scholar 

  18. Schaffer, L. M. & Bernauer, T. Explaining government choices for promoting renewable energy. Energy Policy 68, 15–27 (2014).

    Article  Google Scholar 

  19. Jacobsson, S. & Johnson, A. The diffusion of renewable energy technology: an analytical framework and key issues for research. Energy Policy 28, 625–640 (2000).

    Article  Google Scholar 

  20. Markard, J. The next phase of the energy transition and its implications for research and policy. Nat. Energy 3, 628–633 (2018).

    Article  Google Scholar 

  21. Bento, N., Wilson, C. & Anadon, L. D. Time to get ready: conceptualizing the temporal and spatial dynamics of formative phases for energy technologies. Energy Policy 119, 282–293 (2018).

    Article  Google Scholar 

  22. Rotmans, J., Kemp, R. & van Asselt, M. More evolution than revolution: transition management in public policy. Foresight 3, 15–31 (2001).

    Article  Google Scholar 

  23. Jacobsson, S. & Bergek, A. Transforming the energy sector: the evolution of technological systems in renewable energy technology. Ind. Corp. Change 13, 815–849 (2004).

    Article  Google Scholar 

  24. Arthur, W. Increasing Returns and Path Dependence in the Economy (Univ. Michigan Press, 1994).

  25. Blazquez, J., Fuentes-Bracamontes, R., Bollino, C. A. & Nezamuddin, N. The renewable energy policy paradox. Renew. Sustain. Energy Rev. 82, 1–5 (2018).

    Article  Google Scholar 

  26. Wanner, B. Is Exponential Growth of Solar PV the Obvious Conclusion? (IEA, 2019).

  27. Martinot, E. Grid integration of renewable energy: flexibility, innovation, and experience. Annu. Rev. Environ. Resour. 41, 223–251 (2016).

    Article  Google Scholar 

  28. Bird, L., Millligan, M. & Lew, D. Integrating Variable Renewable Energy: Challenges and Solutions NREL/TP-6A20-60451 (NREL, 2013).

  29. Kramer, G. J. & Haigh, M. No quick switch to low-carbon energy In the first of two pieces on reducing greenhouse-gas emissions. Nature 462, 568–569 (2009).

    Article  Google Scholar 

  30. Wüstenhagen, R., Wolsink, M. & Bürer, M. J. Social acceptance of renewable energy innovation: an introduction to the concept. Energy Policy 35, 2683–2691 (2007).

    Article  Google Scholar 

  31. Grubler, A., Wilson, C. & Nemet, G. Apples, oranges, and consistent comparisons of the temporal dynamics of energy transitions. Energy Res. Soc. Sci. 22, 18–25 (2016).

    Article  Google Scholar 

  32. Dobbin, F., Simmons, B. & Garrett, G. The global diffusion of public policies: social construction, coercion, competition, or learning? Annu. Rev. Sociol. 33, 449–472 (2007).

    Article  Google Scholar 

  33. Alizada, K. Rethinking the diffusion of renewable energy policies: a global assessment of feed-in tariffs and renewable portfolio standards. Energy Res. Soc. Sci. 44, 346–361 (2018).

    Article  Google Scholar 

  34. Fleig, A., Schmidt, N. M. & Tosun, J. Legislative dynamics of mitigation and adaptation framework policies in the EU. Eur. Policy Anal. 3, 101–124 (2017).

    Google Scholar 

  35. Steffen, B., Matsuo, T., Steinemann, D. & Schmidt, T. S. Opening new markets for clean energy: the role of project developers in the global diffusion of renewable energy technologies. Bus. Politics 20, 553–587 (2018).

    Article  Google Scholar 

  36. Rogers, E. M. Diffusion of Innovations 5th edn (The Free Press, 2003).

  37. Binz, C. & Truffer, B. Global innovation systems—a conceptual framework for innovation dynamics in transnational contexts. Res. Policy 46, 1284–1298 (2017).

    Article  Google Scholar 

  38. Jewell, J. Ready for nuclear energy? An assessment of capacities and motivations for launching new national nuclear power programs. Energy Policy 39, 1041–1055 (2011).

    Article  Google Scholar 

  39. Global Solar Atlas 2.0 (Solargis, accessed 30 December 2020);

  40. Global Wind Atlas 3.0 (DTU Wind Energy, accessed 30 December 2020);

  41. Jewell, J. et al. Limited emission reductions from fuel subsidy removal except in energy-exporting regions. Nature 554, 229–233 (2018).

    Article  Google Scholar 

  42. Colgan, J. D. Oil, domestic politics, and international conflict. Energy Res. Soc. Sci. 1, 198–205 (2014).

    Article  Google Scholar 

  43. Grubler, A. Time for a change: on the patterns of diffusion of innovation. Daedalus 125, 19–42 (1996).

    Google Scholar 

  44. van Ewijk, S. & McDowall, W. Diffusion of flue gas desulfurization reveals barriers and opportunities for carbon capture and storage. Nat. Commun. 11, 4298 (2020).

    Article  Google Scholar 

  45. Rogelj, J. et al. in Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) 93–174 (WMO, 2018).

  46. Huppmann, D. et al. IAMC 1.5°C Scenario Explorer and Data Hosted by IIASA (IAMC, 2019);

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

    Article  Google Scholar 

  48. Victoria, M. et al. Solar photovoltaics is ready to power a sustainable future. Joule 5, 1041–1056 (2021).

    Article  Google Scholar 

  49. Jewell, J. & Cherp, A. On the political feasibility of climate change mitigation pathways: is it too late to keep warming below 1.5 °C? WIREs Clim. Change 11, e621 (2020).

    Article  Google Scholar 

  50. Lauber, V. & Jacobsson, S. The politics and economics of constructing, contesting and restricting socio-political space for renewables—the German Renewable Energy Act. Environ. Innov. Soc. Transit. 18, 147–163 (2016).

    Article  Google Scholar 

  51. Yan, J., Yang, Y., Campana, P. E. & He, J. City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China. Nat. Energy 4, 709–717 (2019).

    Article  Google Scholar 

  52. Lund, P. Energy policy planning near grid parity using a price-driven technology penetration model. Technol. Forecast. Soc. 90, 389–399 (2015).

    Article  Google Scholar 

  53. World Energy Balances (IEA, accessed 1 May 2021);

  54. Mukasa, A. D., Mutambatsere, E., Arvanitis, Y. & Triki, T. Development of Wind Energy in Africa. African Development Bank Working Paper Series No. 170 (Tunis, 2013);

  55. Bento, N. & Wilson, C. Measuring the duration of formative phases for energy technologies. Environ. Innov. Soc. Transit. 21, 95–112 (2016).

    Article  Google Scholar 

  56. Marchetti, C. The automobile in a system context. Technol. Forecast. Soc. 23, 3–23 (1983).

    Article  Google Scholar 

  57. Grubler, A., Nakićenović, N. & Victor, D. G. Dynamics of energy technologies and global change. Energy Policy 27, 247–280 (1999).

    Article  Google Scholar 

  58. Box-Steffensmeier, J. M. & Jones, B. S. Event History Modeling: A Guide for Social Scientists (Cambridge Univ. Press, 2004).

  59. Beck, N., Katz, J. N. & Tucker, R. Taking time seriously: time-series-cross-section analysis with a binary dependent variable. Am. J. Political Sci. 42, 1260–1288 (1998).

    Article  Google Scholar 

  60. Carter, D. B. & Signorino, C. S. Back to the future: modeling time dependence in binary data. Political Anal. 18, 271–292 (2010).

    Article  Google Scholar 

  61. Borucka, J. Extensions of Cox model for non-proportional hazards purpose. Ekonometria 3, 85–101 (2014).

    Google Scholar 

  62. Myers, R. H. Classical and Modern Regression with Applications (PWS-Kent, 1990).

  63. Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).

    Article  MathSciNet  MATH  Google Scholar 

  64. Kleinbaum, D. G. & Klein, M. Logistic Regression—A Self-Learning Text. (Springer, 2010).

  65. Lekvall, P. & Wahlbin, C. A study of some assumptions underlying innovation diffusion functions. Swedish J. Econ. 75, 362–377 (1973).

    Article  Google Scholar 

  66. Geroski, P. A. Models of technology diffusion. Res. Policy 29, 603–625 (2000).

    Article  Google Scholar 

  67. Martino, J. P. A review of selected recent advances in technological forecasting. Technol. Forecast. Soc. 70, 719–733 (2003).

    Article  Google Scholar 

  68. Verhulst, P. F. Recherches Mathématiques sur La Loi D’Accroissement de la Population. Nouv. Mém. Acad. R. Sci. Belles-Lett. Brux. 18, 14–54 (1845).

    Google Scholar 

  69. Bento, N. & Fontes, M. Spatial diffusion and the formation of a technological innovation system in the receiving country: The case of wind energy in Portugal. Environ. Innov. Soc. Transit. 15, 158–179 (2015).

    Article  Google Scholar 

  70. Gompertz, B. On the nature of the function expressive of the law of human mortality and on a new mode of determining the value of life contingencies. Phil. Trans. R. Soc. 123, 513–585 (1825).

    Google Scholar 

  71. Winsor, C. The Gompertz curve as a growth equation. Proc. Natl Acad. Sci. USA 18, 1–8 (1932).

    Article  MATH  Google Scholar 

  72. Davies, S. W. & Diaz-Rainey, I. The patterns of induced diffusion: evidence from the international diffusion of wind energy. Technol. Forecast. Soc. 78, 1227–1241 (2011).

    Article  Google Scholar 

  73. Bates, D. M. & Chambers, J. M. in Statistical Models in S (eds Chambers, J. M. & Hastie, T. J.) 421–454 (Chapman and Hall, 1991).

  74. Bates, D. M. & Watts, D. G. Nonlinear Regression Analysis and its Applications (Wiley, 1988).

  75. Nash, J. C. Nonlinear Parameter Optimization using R Tools. (Wiley, 2014).

  76. Germany Policy Projections (Climate Action Tracker, accessed 30 December 2020);

  77. Trends in Renewable Energy (IRENA, accessed 30 December 2020);

  78. World Bank Open Data (World Bank, accessed 30 December 2020);

  79. European Union Countries (European Union, 2020);

  80. Member Countries (OECD, accessed 30 December 2020);

  81. Coppedge, M. et al. V-Dem Dataset V9 (SSRN, 2019);

  82. Armingeon, K. et al. Comparative Political Data Set 1960–2018 (Institute of Political Science, University of Zurich, 2020).

  83. Global CFFDA-Based Onshore and Offshore Wind Potential Supply Curves by Country, Class and Depth. (NREL, accessed 1 May 2021); (2013).

  84. Solar Resources by Class and Country (NREL, accessed 1 May 2021); (2008).

  85. Morris, M., Robbins, G., Hansen, U. E. & Nygaard, I. Energy and Industrial Policy Failure in the South African Wind Renewable Energy Global Value Chain: The Political Economy Dynamics Driving a Stuttering Localisation Process (DTU, Orbit, 2020).

  86. Tiseo, I. Global PV module manufacturing share by country 2018 (Statista, accessed 30 December 2020)

  87. Environmental Policy Stringency Index (Edition 2019) (OECD, accessed 30 December 2020);

Download references


The research that led to this publication received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 821471 (project Exploring National and Global Actions to Reduce Greenhouse Gas Emissions (ENGAGE)). V.V. received funding from the Norwegian Research Council no. 267528 Analysing past and future energy industry contractions: Towards a better understanding of the flip-side of energy transitions. J.J. received funding from the European Union’s Horizon 2020 ERC Starting Grant programme under grant agreement no. 950408 for Mechanisms and Actors of Feasible Energy Transitions (MANIFEST). The authors acknowledge G. Semieniuk for useful comments on the manuscript.

Author information

Authors and Affiliations



A.C., V.V. and J.J. jointly conceived and designed the study. V.V. designed and implemented the statistical analysis, modelling growth curves and comparison with scenarios, which included acquisition of data. A.C., V.V. and J.J. jointly interpreted the results. V.V. and J.J. visualized the results with input from A.C. A.C. and J.J. led the literature review and writing with contributions from V.V., J.T. and J.A.G. J.A.G. conducted the literature review on technology diffusion and contributed to the analysis of offshore wind power with V.V. and to the comparison of solar and wind with J.J. and A.C. J.T. contributed to the design and implementation of the EHA of take-off and the literature review.

Corresponding author

Correspondence to Aleh Cherp.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks Nuno Bento 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.

Extended data

Extended Data Fig. 1 Computational experiment for growth parameters of logistic and Gompertz growth models.

The vertical coordinates of each dot show the divergence in G (max growth rate) and horizontal – in ∆t (the duration of transition) for a single computational experiment. The divergence is the ratio between the ‘true’ growth parameter of a computer-generated logistic (a) and Gompertz (b) curve and the same parameter estimated by fitting the other model to the same data (Methods). The dashed line corresponds to equal divergence of G and ∆t. Experiments differ by the degree of curve ‘maturity’ (represented by different colors), that is to which extent the generated data approach the asymptote L of the curve (Methods). Large triangles show the relative differences between the ‘true’ curve and the fitted growth model. Dots show the divergence between the original curve with random noise added (up to 5% above or below the respective original value, uniformly distributed) and the fitted model (Methods). The data illustrate that estimates of the growth parameters converge across the two models with more complete data (high maturity), but that the max growth rate G becomes robust across the two models at lower levels of maturity than the duration of transition ∆t. See Supplementary Note 3 for more discussion on growth metrics.

Source data

Extended Data Fig. 2 Relative differences between parameters of logistic and Gompertz models fitted to empirical data (onshore wind and solar PV).

Panel (a) shows the divergence between parameters of logistic and Gompertz models for different maturity levels. Divergence is the ratio between the larger and the smaller value of the same parameter fitted to the same country data and estimated by two different models. Divergence is calculated for each country and then summary statistics – medians (dots) and full range (brackets) – are shown for sub-samples of countries with different maturity determined by the logistic fit. Median and maximum divergence for maturity <50% are beyond the vertical limits of the figure – see Supplementary Table 1. Panels (b) and (c) shows relative differences in G versus ∆t (panel b) and log(L)/∆t5,86 for all countries for maturity <50% and for all countries included in the analysis of G for maturity >50%. Maturity levels in panels (b) and (c) are depicted with colors. The dashed line in panels (b) and (c) shows the equal relative differences in G and ∆t and G and log(L)/∆t. Relative differences larger that 10 were limited to 10 to prevent the figure from being squeezed. See Supplementary Tables 17 and 18 for fitted values.

Source data

Extended Data Fig. 3 Takeoff years (1%) for onshore wind vs solar PV.

Each dot represents a country positioned according to its wind takeoff year on the horizontal axis and solar takeoff year on the vertical axis. The takeoff year is the year when the share of the given technology for the first time exceeds 1% of the total electricity supply. Countries where a particular technology has not taken off are placed in the grey bands on the top (no solar takeoff) and the right (no wind takeoff). The numbers in the top-right corner indicate the number of countries where neither wind nor solar takeoff has taken place. Colors indicate the country groups (Supplementary Table 5). See Supplementary Table 33 for country codes. The dashed line is based on a linear regression for the countries with both takeoff dates available (except for Denmark treated as an outlier with its very early wind takeoff). The regression coefficient is 0.33 (meaning that a country with wind takeoff three years earlier has, on average, solar takeoff one year earlier), R2 = 36%, p-value < 1%.

Source data

Extended Data Fig. 4 Historical deployment of wind power, growth models and maximum growth rates (G).

Gray dots show empirically observed electricity generation from onshore wind power, normalized to national electricity supply in the takeoff year to adjust for country size. The orange lines show Gompertz model fit and the dark blue lines show logistic model fit to these points (Methods). Stars indicate the takeoff year (T1%) and circles indicate the inflection points for each model (located in the future for accelerating growth). See Methods for definition of maturity and maximum growth rates (G) as well as the method for selecting countries for the analysis of G.

Source data

Extended Data Fig. 5 Historical deployment of wind power, growth models and maximum growth rates (G) for selected countries.

Gray dots show empirically observed electricity generation from solar PV power, normalized to national electricity supply in the takeoff year to adjust for country size. The orange lines show Gompertz model fit and the dark blue lines show logistic model fit to these points (Methods). Stars indicate the takeoff year (T1%) and circles indicate the inflection points for each model (located in the future for accelerating growth). See Methods for definition of maturity and maximum growth rates (G) as well as the method for selecting countries for the analysis of G.

Source data

Extended Data Fig. 6 Are wind and solar power on track to the Paris targets in 2030 and 2050? A comparison with an alternative approach.

The Figure contains the replication and analysis of ref’s8 assessment of whether wind and solar power are on track to attain Paris climate targets. On all panels, dashed lines replicate ref’s. 8 logistic curves fit to 2010 values and saturating at the ‘2050 Paris benchmarks’ defined by ref. 8 as median 2050 values for 1.5 °C scenarios (purple diamonds, also indicating median scenario values for 2030). For this replication we use the range of ‘emergence rates’ (year-on-year growth rates at the early stages) from ref. 8 of 15%, 20%, 25% for wind and 25%, 30%, 35% for solar. For each technology, we mark the central case in black and the high and low cases in grey. Panels (a) and (c) indicate yearly growth rates (G) at the inflection points of these curves normalised to the size of the global electricity system. The G’s for these considerably exceed the maximum growth rates we estimate for any large country so far (Supplementary Fig. 5). The orange and blue lines represent Gompertz and logistic model fits (with inflection points) to the empirical timeseries of global wind and solar power deployment using the approach in this paper (Methods). These models project much lower values for 2030 and 2050 than the models from ref. 8. Panels (b) and (d) zoom the same curves and data on 2010-2020 and indicate Residual Sum of Squares (RSS)74 for the replicated curves and our two model fits vs. 2010-2018 empirical data. The RSS for the replicated logistic curves are between 10 and 280 times larger than our best fit for wind and 400 and 1000 times larger than our best fit for solar, which indicate that the replicated curves from ref. 8 match the empirical data with considerably lower accuracy than our models.

Source data

Extended Data Fig. 7 Estimated vs. observed maximum growth rates.

Estimated growth rates (G) are for the maximum growth (inflection) year. Observed rates are the maximum 5-year moving average annual growth rates (panel a) or maximum observed growth 3-year moving average growth rates (panel b). Estimated maximum growth rates G are based on fitted growth models where the range depicts the difference between the logistic and the Gompertz models. All growth rates are expressed as % of the total electricity supply per year in which these rates are estimated or measured. 45° line depicts equal empirical and estimated rates. Only countries included in the analysis of G (Supplementary Table 18, Supplementary Table 19) are shown.

Source data

Supplementary information

Supplementary Information

Supplementary information includes Figs. 1–6, Tables 1–33 and Notes 1–6, as well as the references for these materials.

Supplementary Data 1

Wind and solar power growth rates in climate mitigation scenarios.

Source data

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 1

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 3

Statistical Source Data.

Source Data Extended Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 5

Statistical Source Data.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 7

Statistical Source Data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cherp, A., Vinichenko, V., Tosun, J. et al. National growth dynamics of wind and solar power compared to the growth required for global climate targets. Nat Energy 6, 742–754 (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