Project title: Predicting empowerment: AI-driven targeting of best-fit energy services for efficient sustainable development
PI: Stephanie Hirmer
While energy access is a critical enabler of health, education, safety, gender equality, and productivity, 775 million people worldwide still lack access to electricity and 2.3 billion lack access to clean cooking. To use limited aid funds efficiently to close this energy gap, this project aims to predictively target energy interventions to match local needs, values, and context. As gathering field data for this purpose is difficult and expensive, the project investigates whether multi-modal transfer learning – combining large-scale geospatial data with local survey data – can be used to predict energy interventions that align with community needs and values at scale.
