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A global analysis of the progress and failure of electric utilities to adapt their portfolios of power-generation assets to the energy transition


The penetration of low-carbon technologies in power generation has challenged fossil-fuel-focused electric utilities. While the extant, predominantly qualitative, literature highlights diversification into renewables among possible adaptation strategies, comprehensive quantitative understanding of utilities’ portfolio decarbonization has been missing. This study bridges this gap, systematically quantifying the transitions of over 3,000 utilities worldwide from fossil-fuelled capacity to renewables over the past two decades. It applies a machine-learning-based clustering algorithm to a historical global asset-level dataset, distilling four macro-behaviours and sub-patterns within them. Three-quarters of the utilities did not expand their portfolios. Of the remaining companies, a handful grew coal ahead of other assets, while half favoured gas and the rest prioritized renewables growth. Strikingly, 60% of the renewables-prioritizing utilities had not ceased concurrently expanding their fossil-fuel portfolio, compared to 15% reducing it. These findings point to electricity system inertia and the utility-driven risk of carbon lock-in and asset stranding.

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Fig. 1: Utilities’ portfolio growth patterns by fuel type.
Fig. 2: The share of RE in the RE-prioritizing utilities’ portfolios, 2018.
Fig. 3: Portfolio composition of select companies.
Fig. 4: Utilities’ transition between clusters.

Data availability

The data on power-generation assets from the UDI WEPP database were received under licence from S&P Global Market Intelligence and could be obtained under similar arrangements from The historical data on the countries’ policies were retrieved from the annual REN21 Renewables Global Status Reports publicly available at and from the World Bank’s Carbon Pricing Dashboard publicly available at The data that support the graphs within this article and other findings of the study are available from the author upon reasonable request.

Code availability

The study employs a hierarchical clustering algorithm by the free open-source SciPy library in Python. The code is available from the author upon reasonable request.


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I thank B. Caldecott, A. Haney, C. Hepburn and D. Tulloch for comments on the manuscript. I am also thankful for the financial support to my doctoral research offered by the ESRC Grand Union Doctoral Training Partnership, a Scatcherd European Scholarship by the University of Oxford and the 73 Scholarship Fund for Geography by Hertford College, Oxford, established through the generosity of the college’s alumni—P. Newman and M. Teversham.

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Correspondence to Galina Alova.

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Supplementary Tables 1–2, Figs. 1–4, Note 1 and refs. 1–6.

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Alova, G. A global analysis of the progress and failure of electric utilities to adapt their portfolios of power-generation assets to the energy transition. Nat Energy 5, 920–927 (2020).

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