Quantifying patterns of the power generation sector's low-carbon transition using machine learning and asset-level datasets
Keywords: decarbonisation
Africa
coal
fossil fuels
gas
energy transition
machine learning
renewable energy
utilities
independent power producers

The past decades have seen a dramatic decline in the costs of clean power generation, driven by support policies and technology improvements, making renewable energy increasingly more cost-competitive. Yet, the share of solar and wind in global generation mix remains low, with fossil fuels continuing to dominate. This points to potential frictions in the electricity sector’s transition to renewables and, even more so, in the sector’s shift away from fossil fuels. Key research gaps lie in understanding and accurately capturing the pace and nature of the on-going low-carbon transition of the power sector, both at the company and country level, and across different geographies. While developing countries merit a particular research focus, given the dual challenges they face to simultaneously industrialise and decarbonise their economies, extant research tends to be often limited to the Western world and single-country studies. In this context, there is a particular need for large international analyses to examine the intricate interrelationships of different factors affecting the decarbonisation patterns of the power sector across companies and countries.
This DPhil thesis aims to contribute to closing these research gaps, by offering insights into the pace and drivers of the decarbonisation process of the global power-generation sector. In doing so, it also seeks to contribute to the methodological advancement of the field, by demonstrating the data and analytical tools that can be used to capture and examine the complexity of the sector’s transition. To this end, the thesis has produced a series of three articles (Chapters 3-5) focusing on the decarbonisation of the power sector internationally, at the company and country level. Methodologically, the thesis is integrated through the application of machine-learning-based techniques, both supervised and unsupervised methods, selected on the basis of their best suitability to analyse asset-level datasets involved in addressing each research problem at hand.
The findings of the thesis point to considerable electricity system inertia. As the global analysis of utility companies suggests, this might be driven among others by the slow transition of electric utilities to renewable energy and their continued investments in fossil fuels, particularly natural gas. This could at least in part be explained by the finding that gas overall shows greater resilience to environmental policy stringency, compared to coal, as concluded by the study of the factors contributing to company technology choices. Furthermore, gas power plants have considerably higher chances of being successfully commissioned than renewable energy projects, as the analysis across African countries has shown. Utilities also tend to be less responsive to environmental policies than non-utility actors such as independent power producers, particularly in terms of renewable energy investments. This could also contribute to explaining the fact that independent power producers own the vast majority of the currently installed global renewable energy capacity. That said, when looking across African economies, state-ownership of planned power plants does not seem to reduce their implementation chances. Overall, this work demonstrates the importance of a careful and nuanced design of electricity markets and environmental policies to promote the uptake of renewables, while simultaneously breaking the dependence on fossil fuels.