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

Hanjie Wang, Lucie Maruejols and Xiaohua Yu

Energy Economics, 2021, vol. 102, issue C

Abstract: 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 socioeconomic survey data in response to these challenges. We found that a machine learning algorithm incorporating 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.

Keywords: Remote sensing data; Machine learning; Energy poverty prediction; Random forest; Precipitation; PM2.5 concentration (search for similar items in EconPapers)
JEL-codes: I32 Q47 Q48 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:102:y:2021:i:c:s0140988321003923

DOI: 10.1016/j.eneco.2021.105510

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