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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.

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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.


  1. World Energy Outlook 2018: The Future is Electrifying (IEA/OECD, 2018).

  2. Kelsey, N. & Meckling, J. Who wins in renewable energy? Evidence from Europe and the United States. Energy Res. Soc. Sci. 37, 65–73 (2018).

    Google Scholar 

  3. Eberhard, A., Gratwick, K., Morella, E. & Antmann, P. Accelerating investments in power in sub-Saharan Africa. Nat. Energy 2, 17005 (2017).

    Google Scholar 

  4. S&P Global Market Intelligence World Electric Power Plants Database (S&P Global, 2018);

  5. Castaneda, M., Franco, C. J. & Dyner, I. Evaluating the effect of technology transformation on the electricity utility industry. Renew. Sustain. Energy Rev. 80, 341–351 (2017).

    Google Scholar 

  6. Hannon, M. J., Foxon, T. J. & Gale, W. F. The co-evolutionary relationship between energy service companies and the UK energy system: implications for a low-carbon transition. Energy Policy 61, 1031–1045 (2013).

    Google Scholar 

  7. Pollitt, M. G. & Anaya, K. L. Can current electricity markets cope with high shares of renewables? A comparison of approaches in Germany, the UK and the State of New York. Energy J. 37, 69–88 (2016).

    Google Scholar 

  8. Shomali, A. & Pinkse, J. The consequences of smart grids for the business model of electricity firms. J. Clean. Prod. 112, 3830–3841 (2016).

    Google Scholar 

  9. Wainstein, M. E. & Bumpus, A. G. Business models as drivers of the low carbon power system transition: a multi-level perspective. J. Clean. Prod. 126, 572–585 (2016).

    Google Scholar 

  10. Hall, S. & Roelich, K. Business model innovation in electricity supply markets: the role of complex value in the United Kingdom. Energy Policy 92, 286–298 (2016).

    Google Scholar 

  11. Midttun, A. & Piccini, P. B. Facing the climate and digital challenge: European energy industry from boom to crisis and transformation. Energy Policy 108, 330–343 (2017).

    Google Scholar 

  12. Kungl, G. Stewards or sticklers for change? Incumbent energy providers and the politics of the German energy transition. Energy Res. Soc. Sci. 8, 13–23 (2015).

    Google Scholar 

  13. Mitchell, C. Momentum is increasing towards a flexible electricity system based on renewables. Nat. Energy 1, 15030 (2016).

    Google Scholar 

  14. Kungl, G. & Geels, F. W. Sequence and alignment of external pressures in industry destabilisation: understanding the downfall of incumbent utilities in the German energy transition (1998–2015). Environ. Innov. Soc. Transit. 26, 78–100 (2018).

    Google Scholar 

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

    Google Scholar 

  16. Parag, Y. & Sovacool, B. K. Electricity market design for the prosumer era. Nat. Energy 1, 16032 (2016).

    Google Scholar 

  17. Richter, M. Utilities’ business models for renewable energy: a review. Renew. Sustain. Energy Rev. 16, 2483–2493 (2012).

    Google Scholar 

  18. Bryant, S. T., Straker, K. & Wrigley, C. The typologies of power: energy utility business models in an increasingly renewable sector. J. Clean. Prod. 195, 1032–1046 (2018).

    Google Scholar 

  19. Richter, M. Business model innovation for sustainable energy: German utilities and renewable energy. Energy Policy 62, 1226–1237 (2013).

    Google Scholar 

  20. Yi, H. & Feiock, R. C. Renewable energy politics: policy typologies, policy tools, and state deployment of renewables. Policy Stud. J. 42, 391–415 (2014).

    Google Scholar 

  21. Löbbe, S. & Jochum, G. in Future of Utilities—Utilities of the Future: How Technological Innovations in Distributed Energy Resources Will Reshape the Electric Power Sector (ed. Sioshansi, F. P.) 323–341 (Elsevier, 2016).

  22. Nillesen, P. & Pollitt, M. in Future of Utilities—Utilities of the Future: How Technological Innovations in Distributed Energy Resources Will Reshape the Electric Power Sector (ed. Sioshansi, F. P.) 283–301 (Elsevier, 2016).

  23. Carley, S., Davies, L. L., Spence, D. B. & Zirogiannis, N. Empirical evaluation of the stringency and design of renewable portfolio standards. Nat. Energy 3, 754–763 (2018).

    Google Scholar 

  24. Delmas, M. A. & Montes-Sancho, M. J. U.S. state policies for renewable energy: context and effectiveness. Energy Policy 39, 2273–2288 (2011).

    Google Scholar 

  25. Yin, H. & Powers, N. Do state renewable portfolio standards promote in-state renewable generation? Energy Policy 38, 1140–1149 (2010).

    Google Scholar 

  26. Ma, C. & He, L. From state monopoly to renewable portfolio: restructuring China’s electric utility. Energy Policy 36, 1697–1711 (2008).

    Google Scholar 

  27. Frei, F., Sinsel, S. R., Hanafy, A. & Hoppmann, J. Leaders or laggards? The evolution of electric utilities’ business portfolios during the energy transition. Energy Policy 120, 655–665 (2018).

    Google Scholar 

  28. Schleicher-Tappeser, R. How renewables will change electricity markets in the next five years. Energy Policy 48, 64–75 (2012).

    Google Scholar 

  29. Annual Report 2018 (Ørsted, 2019).

  30. Carley, S. State renewable energy electricity policies: an empirical evaluation of effectiveness. Energy Policy 37, 3071–3081 (2009).

    Google Scholar 

  31. Polzin, F., Migendt, M., Täube, F. A. & von Flotow, P. Public policy influence on renewable energy investments—a panel data study across OECD countries. Energy Policy 80, 98–111 (2015).

    Google Scholar 

  32. Solangi, K. H., Islam, M. R., Saidur, R., Rahim, N. A. & Fayaz, H. A review on global solar energy policy. Renew. Sustain. Energy Rev. 15, 2149–2163 (2011).

    Google Scholar 

  33. Best, R. & Burke, P. J. Adoption of solar and wind energy: the roles of carbon pricing and aggregate policy support. Energy Policy 118, 404–417 (2018).

    Google Scholar 

  34. Jotzo, F. Australia’s carbon price. Nat. Clim. Change 2, 475–476 (2012).

    Google Scholar 

  35. During the ten-year transition to gas, the power-generation assets of Far Eastern Generating Company have seen a three-fold reduction in their greenhouse gas emissions (in English). PrimaMedia (2017).

  36. Bringing the Best of the West to Our Customer’s Door (PacifiCorp, 2019).

  37. Climate Change Mitigation: Policies and Progress (OECD, 2015).

  38. Ossenbrink, J., Hoppmann, J. & Hoffmann, V. H. Hybrid ambidexterity: how the environment shapes incumbents’ use of structural and contextual approaches. Organ. Sci. 30, 1319–1348 (2019).

    Google Scholar 

  39. Effective Carbon Rates 2018: Pricing Carbon Emissions Through Taxes and Emissions Trading (OECD, 2018).

  40. Energy Prices and Costs in Europe (European Commission, 2019).

  41. Pfeiffer, A., Millar, R., Hepburn, C. & Beinhocker, E. The ‘2°C capital stock’ for electricity generation: committed cumulative carbon emissions from the electricity generation sector and the transition to a green economy. Appl. Energy 179, 1395–1408 (2016).

    Google Scholar 

  42. Mac Kinnon, M. A., Brouwer, J. & Samuelsen, S. The role of natural gas and its infrastructure in mitigating greenhouse gas emissions, improving regional air quality, and renewable resource integration. Prog. Energy Combust. Sci. 64, 62–92 (2018).

    Google Scholar 

  43. Fei, T. Coal Transition in China. Options to Move from Coal Cap to Managed Decline under an Early Emissions Peaking Scenario (IDDRI and Climate Strategies, 2018).

  44. Coady, D., Parry, I., Le, N.-P. & Shang, B. Global Fossil Fuel Subsidies Remain Large: an Update Based on Country-Level Estimates Working paper no. 19/89 (International Monetary Fund, 2019).

  45. China’s Efforts To Phase Out and Rationalise Its Inefficient Fossil-Fuel Subsidies: a Report on the G20 Peer Review of Inefficient Fossil-Fuel Subsidies that Encourage Wasteful Consumption in China (G20 Peer Review Team, 2016).

  46. Shih, G. China is back to building coal power plants, sparking fears over climate change. Washington Post (20 November 2019).

  47. Coal 2019 (IEA/OECD, 2019).

  48. Seto, K. C. et al. Carbon lock-in: types, causes, and policy implications. Annu. Rev. Environ. Resour. 41, 425–452 (2016).

    Google Scholar 

  49. Apajalahti, E. L., Lovio, R. & Heiskanen, E. From demand side management (DSM) to energy efficiency services: a Finnish case study. Energy Policy 81, 76–85 (2015).

    Google Scholar 

  50. Adams, R. & Jamasb, T. Optimal power generation portfolios with renewables: an application to the UK. Cambridge Working Papers in Economics 1646 (2016).

  51. Gotzens, F., Heinrichs, H., Hörsch, J. & Hofmann, F. Performing energy modelling exercises in a transparent way—the issue of data quality in power plant databases. Energy Strateg. Rev. 23, 1–12 (2019).

    Google Scholar 

  52. Data Base Description and Research Methodology: UDI World Electric Power Plants Data Base (S&P Global, 2015).

  53. Smouse, S. M., Jones, A., Fapohunda, B. O., Render, M. & Hindman, J. W. Coal- and gas-fired power construction and cancellation trends in countries with the most new coal power capacity since 2003. In Proc. ASME 2018 Power & Energy Conference & Exhibition 7466 (ASME, 2018).

  54. Data Base Description and Research Methodology: World Electric Power Plants Data Base (S&P Global, 2018).

  55. Liao, X., Chai, L., Jiang, Y., Ji, J. & Zhao, X. Inter-provincial electricity transmissions’ co-benefit of national water savings in China. J. Clean. Prod. 229, 350–357 (2019).

    Google Scholar 

  56. Reinartz, S. J. & Schmid, T. Production flexibility, product markets, and capital structure decisions. Rev. Financ. Stud. 29, 1501–1548 (2016).

    Google Scholar 

  57. IRENA releases world’s most comprehensive renewable energy capacity figures. IRENA (14 June 2015);

  58. Aboumahboub, T., Schaber, K., Wagner, U. & Hamacher, T. On the CO2 emissions of the global electricity supply sector and the influence of renewable power-modeling and optimization. Energy Policy 42, 297–314 (2012).

    Google Scholar 

  59. Peter, J. How does climate change affect electricity system planning and optimal allocation of variable renewable energy? Appl. Energy 252, 113397 (2019).

    Google Scholar 

  60. Mayer, K. & Trück, S. Electricity markets around the world. J. Commod. Mark. 9, 77–100 (2018).

    Google Scholar 

  61. Global Status Reports (REN21, 2005–2019).

  62. Carbon Pricing Dashboard (World Bank, accessed 1 July 2019);

  63. Maimon, O. & Rokach, L. Data Mining and Knowledge Discovery Handbook (Springer, 2010);

  64. Hastie, T., Tibshirani, R. & Friedman, J. H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2009).

  65. Backman, C. A., Verbeke, A. & Schulz, R. A. The drivers of corporate climate change strategies and public policy: a new resource-based view perspective. Bus. Soc. 56, 545–575 (2017).

    Google Scholar 

  66. Gasbarro, F., Iraldo, F. & Daddi, T. The drivers of multinational enterprises’ climate change strategies: a quantitative study on climate-related risks and opportunities. J. Clean. Prod. 160, 8–26 (2017).

    Google Scholar 

  67. Haney, A. B. Threat interpretation and innovation in the context of climate change: an ethical perspective. J. Bus. Ethics 143, 261–276 (2017).

    Google Scholar 

  68. Weinhofer, G. & Busch, T. Corporate strategies for managing climate risks. Bus. Strateg. Environ. 22, 121–144 (2013).

    Google Scholar 

  69. Kalkuhl, M., Edenhofer, O. & Lessmann, K. Learning or lock-in: optimal technology policies to support mitigation. Resour. Energy Econ. 34, 1–23 (2012).

    Google Scholar 

  70. Conway, D., Dalin, C., Landman, W. A. & Osborn, T. J. Hydropower plans in eastern and southern Africa increase risk of concurrent climate-related electricity supply disruption. Nat. Energy 2, 946–953 (2017).

    Google Scholar 

  71. Bellman, R. Dynamic Programming (Princeton Univ. Press, 1957).

  72. Bellman, R. Adaptive Control Processes: a Guided Tour (Princeton Univ. Press, 1961).

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

    MATH  Google Scholar 

  74. Caliński, T., Harabasz, J. & Caliliski, T. A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974).

    MathSciNet  MATH  Google Scholar 

  75. Davies, D. L. & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-1, 224–227 (1979).

    Google Scholar 

  76. Halkidi, M. On clustering validation techniques. J. Intell. Inf. Syst. 17, 107–145 (2001).

    MATH  Google Scholar 

  77. Efron, B. Computers and the theory of statistics: thinking the unthinkable. SIAM Rev. 21, 460–480 (1979).

  78. Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, 1993).

  79. Buckland, S. T., Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application (Cambridge Univ. Press, 1997);

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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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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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