Multiple demographic factors (e.g., income status, age, gender, disability status), and their interactions within their community, are important to help understand the complex needs of people that are currently without modern forms of energy access in Low- and Middle- Income Countries (LMICs).
In this session, Stephanie will look at the role of user-perceived values (UPV) in informing selection of the optimal energy service solution (e.g., irrigation pumps or street lighting). The focus will be on rural communities in East Africa. Specifically, she will look at the impact of different “markers of inequality”. These are derived from the interaction of demographic factors. Stephanie explores how these factors can impact UPV classification using Natural Language Processing. Finally, she will discuss how the “markers of inequality” may be used to better select and target energy solutions from a local to national scale.
This event is organised by the Oxford Martin Programme on Integrating Renewable Energy.
Registration required: Please see the programme's event webpage for full details and to register: https://www.renewableenergy.ox...