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

Abstract

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.

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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 https://github.com/poletresearch/RES_article.

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Acknowledgements

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.

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Authors

Contributions

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.

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Peer review information Nature Energy thanks Nuno Bento and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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.

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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). https://doi.org/10.1038/s41560-021-00863-0

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