Predicting energy poverty with combinations of remote-sensing and socioeconomic survey data in India: Evidence from machine learning

Identifying energy poverty and targeting interventions require up-to-date and comprehensive survey data, which are expensive, time-consuming, and difficult to conduct, especially in rural areas of developing countries. This paper examined the potential of satellite remote sensing data in energy poverty prediction combined with so-cioeconomic survey data in response to these challenges. We found that a machine learning algorithm incor-porating geographical and environmental remotely collected indicators could identify 90.91% of the districts with high energy poverty and performs better than those using socioeconomic indicators only. Specifically, precipitation and fine particulate matter (PM2.5) offer the most significant contribution. Moreover, the algorithm, which was trained using a dataset from 2015, could also perform well to predict energy poverty using two environment indicators: precipitation and PM2.5 concentration.

Automated summary

The study demonstrates that a machine learning algorithm incorporating satellite remote sensing data and socioeconomic survey data can accurately predict energy poverty, with precipitation and PM2.5 being the most important environmental indicators.