Program > Papers by speaker > C. Mukherjee Sanghamitra

Factors influencing early electric vehicle adoption in Ireland
Sanghamitra C. Mukherjee  1@  , Lisa Ryan  1, *@  
1 : University College Dublin  (UCD)
* : Corresponding author

The objective of this work is to analyse the key determinants of electric vehicle uptake amongst early adopters. Transport accounts for about a quarter of Europe's total greenhouse gas emissions and has not achieved similar reductions in emissions as other sectors. However, there is an opportunity to achieve lower emissions through the widespread use of electric vehicles. Due to the rising awareness of the link between emissions and global warming, the European Union has set serious targets for renewable energy and greenhouse gas emissions that member states must achieve by 2020 and 2030. Although considerable progress has been made in reaching overall targets, efforts in the transport sector have been lagging in many countries, with a significant boost required in electric vehicle roll-out if transport-specific targets are to be met. One reason for this lack of progress is possibly an incomplete understanding of the motivations behind consumer uptake, which in turn, hampers policy design to encourage adoption. Here, for the first time, the case study of Ireland is used to analyse socio-demographic and neighbourhood characteristics such as charging infrastructure, dealers and other EV adopters, to identify the key determinants of electric vehicle adoption in the early phase of technology diffusion. From our exploratory data analysis, social class which represents whether the population consists of skilled, semi-skilled or unskilled workers, appears to be the principal factor affecting EV uptake in Ireland. This variable may proxy for income effects, implying that the average wealth of a neighbourhood matters for EV ownership. There also appears to be clustering in EV adopters, possibly due to unobserved peer effects. The OLS model performs poorly for our dataset. Our future work will help determine the significant predictors of adoption based on a spatial econometric approach that explicitly models relationships between agents in the model such that the restrictive assumptions of OLS models can be relaxed to allow for interdependence between individual actors.


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