Analysis of consumer choice for low-carbon technologies by using neural networks

Abstract

Through a modelling-based contribution, this paper critically reviews and explores the impact of low carbon heat policies to induce technological policy development. The paper interrogates an Irish government funded sustainable energy financed scheme (the ‘Greener Homes Scheme’), launched in 2006 and aimed at the deployment of low carbon technology in the residential sector. This paper analyses 31,560 technology installations supported under this scheme and it utilises artificial neural network modelling as a method of better analysing and understanding the effects, relationships and dependencies that influence consumer decision making and responses to new technological policies. It is responding to a perceived limited understanding of the variables that influence a wider adoption of low carbon technologies and the opportunities and potential that could result in a wider appreciation of the broader impact of market barriers. It builds up on the artificial neural networks modelling work, explores its application in pattern recognition and interprets its influence in predicting customer behaviour. The paper provides an enhanced understanding of the various factors that influence consumer selection of one low carbon technology over another. Evaluation of the results revealed that the developed artificial neural network model (generic 7-6-4 neurons layered architecture) is the most appropriate tool and suitable network in predicting indices, based on certain social conditions, on the choice of certain low carbon technologies.