Thesis icon

Thesis

Quantifying patterns of the power generation sector's low-carbon transition using machine learning and asset-level datasets

Abstract:

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 u...

Expand abstract

Actions


Access Document


Files:

Authors


More by this author
Division:
SSD
Department:
SOGE
Role:
Author

Contributors

Institution:
University of Oxford
Division:
SSD
Department:
SOGE
Sub department:
Smith School
Role:
Supervisor
Institution:
University of Oxford
Role:
Supervisor
More from this funder
Name:
Economic and Social Research Council Grand Union Doctoral Training Partnership
More from this funder
Name:
Scatcherd European Scholarship, the University of Oxford
More from this funder
Name:
73 Scholarship Fund for Geography from Hertford College, Oxford, established through the generosity of the college’s alumni, P. Newman and M. Teversham
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP