  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 clean cooking interventions must enact a range of
measures to address the barriers faced by households on different energy
transition pathways.
