The low-carbon transition of the global electricity sector
Galina completed her degree in 2022. She was based at the Sustainable Finance Programme of the University of Oxford’s Smith School of Enterprise and the Environment. She is leading projects focusing on quantifying barriers to the global electricity sector’s transition to renewable energy and away from fossil fuels, and the associated carbon lock-in risks.
In her work, she applies machine-learning-based techniques to uncover novel insights form large complex asset-level datasets, to inform future energy infrastructure investment decisions.
Her research has culminated in impactful publications in top journals, with two of her recent Nature Energy studies covered by over 100 news outlets and radio programmes, including BBC, The Guardian, Bloomberg, The New York Times and TIME, and expert pieces in Joule and Nature Energy.
Previously at the OECD, an international organisation in Paris, Galina led projects for the public and private sector on the decarbonisation of the extractive industry, climate finance and natural capital management. A highlight of Galina’s career also includes her work as a direct economic adviser to the Minister of Industrialisation and Trade in Namibia.
Galina holds an MPhil in spatial economics and real estate finance from the University of Cambridge and MA in economics from the University of Glasgow.
- Alova, G. and Caldecott, B. (2021) A machine learning model to investigate factors contributing to the energy transition of utility and independent power producer sectors internationally. iScience, 24(9). 102929.
- Alova, G., Trotter, P.A. and Money, A. (2021) A machine-learning approach to predicting Africa's electricity mix based on planned power plants and their chances of success. Nature Energy, 6: 158-166.
- Alova, G. (2020) A global analysis of the progress and failure of electric utilities to adapt their portfolios of power-generation assets to the energy transition. Nature Energy, 5: 920-927.