E-Waste Quantification and Machine Learning Forecasting in a Data-Scarce Context

Quantifying e-waste in Sub-Saharan Africa remains constrained by scarce data, weak

institutional reporting, and the dominance of informal sector activity. We present the

first nationwide assessment of e-waste generation and Random Forest-based national

forecasting in Sierra Leone. A mixed-methods survey administered 6000 questionnaires

across all 16 districts, targeting households, institutions, enterprises, and informal actors.