Slipping through the net: Can data science approaches help target clean cooking policy interventions?

André Paul Neto-Bradley, Ruchi Choudhary and Amir Bazaz

Energy Policy, 2020, vol. 144, issue C

Abstract: Reliance on solid biomass cooking fuels in India has negative health and socio-economic consequences for households, yet policies aimed at promoting uptake of LPG for cooking have not always been effective at promoting sustained transition to cleaner cooking amongst intended beneficiaries. This paper uses a two step approach combining predictive and descriptive analyses of the IHDS panel dataset to identify different groups of households that switched stove between 2004/5 and 2011/12. A tree-based ensemble machine learning predictive analysis identifies key determinants of a switch from biomass to non-biomass stoves. A descriptive clustering analysis is used to identify groups of stove-switching households that follow different transition pathways. There are three key findings of this study: firstly non-income determinants of stove switching do not have a linear effect on stove switching, in particular variables on time of use and appliance ownership which offer a proxy for household energy practices; secondly location specific factors including region, infrastructure availability, and dwelling quality are found to be key determinants and as a result policies must be tailored to take into account local variations; thirdly some groups of households that adopt non-biomass stoves continue using biomass and interventions should be targeted to reduce their biomass use.

Keywords: Energy access; Cooking fuel; Energy poverty; India; Urban analytics (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:144:y:2020:i:c:s0301421520303815

DOI: 10.1016/j.enpol.2020.111650

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