This study explores the impact of socio-demographic, behavioural, and built-environment characteristics on residential renewable energy technology adoption. It provides new insights on factors influencing uptake using nearest neighbour and random forest machine learning models at a granular spatial scale. Being computationally inexpensive and having good classification performance, these models serve as useful baseline prediction tools. Data is sourced from an Irish survey of consumer perceptions of three key technologies - electric vehicles, solar photovoltaic panels, and heat pumps - and general attitudes towards sustainability, innovation, risk, and time. We demonstrate that utility bills, residence period, attitudes to sustainability, satisfaction with household heating, and perceptions of hassle have the biggest influence on current uptake. Urban areas, typically having better access to information and resources, are likely to see the biggest uptake first. Additionally, compatibility of household infrastructure, technical interest, and social approval are the most important predictors of potential uptake. These results may inform policy in other early adopter markets as well. Overall, policy makers must be cognisant of the stage of adoption their country is currently at. Accordingly, a holistic approach to tackling low adoption must include measures that not only enhance adoption capabilities via rebates and financial measures, but also support the opportunity and intent to purchase such technologies.
Renewable energy technology adoption consumer behaviour machine learning heat pumps solar PVs electric vehicles