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Meghan Cowling

Meghan Cowling

Meghan is a first class honours candidate currently studying a bachelor of Environmental Management at the University of Queensland in Brisbane, Australia. Meghan is majoring in Sustainable Development, with a specific interest in carbon and energy management. After conducting an internship with an energy and carbon consultancy company, Meghan was inspired to conduct research in this field, to assess possible greenhouse gas abatement opportunities for Australia.

Meghan’s honours thesis focuses on consumer preferences for electric vehicles, with the aim of identifying which attributes are most influential on the uptake of this technology. This research has been driven by the relatively small interest in EVs within Australia, despite desirable market factors and high private vehicle ownership. Much of the existing research in this field conducts a comparison between plug-in electric, hybrid and conventional vehicles. This presented a gap in knowledge concerning looking solely at electric vehicles and the attributes that make them less desirable to consumers, which Meghan hopes to explore through her research.

Meghan will be using a Choice-Based Conjoint Analysis and Best-Worst Scaling to replicate the decision-making process consumers would be faced with when considering the purchase of an electric vehicle. Using a stated preference method and choice modelling will assist in gaining a better understanding of the EV market in Australia and which attributes are most important in the decision-making process of consumers. These findings can then be used to better inform policy and decision-making directed at encouraging the uptake of electric vehicles in the future, ultimately aiding greenhouse gas abatement in Australia.

If you want to learn more about the work Meghan is doing, feel free to contact her on This email address is being protected from spambots. You need JavaScript enabled to view it..

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Lighthouse Studio is our flagship software for producing and analyzing online and offline surveys. It contains modules for general interviewing, choice-based conjoint, adaptive choice-based conjoint, adaptive choice analysis, choice-value analysis, and maxdiff exercises.

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