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Mukherjee, Sanghamitra
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Mukherjee, Sanghamitra
Official Name
Mukherjee, Sanghamitra
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Now showing 1 - 3 of 3
- PublicationA Framework to Measure Regional Disparities in Battery Electric Vehicle Diffusion in IrelandThis work studies the role of socio-economic and geospatial factors in shaping battery electric vehicle adoption for the case study of Ireland. It provides new insights on the level and timing of likely adoption at scale using a Bass diffusion model combined with a spatial model. The Bass model demonstrates that a country like Ireland may experience peak sales between 2025 and 2030 given current trends, reaching overall uptake levels that are not commensurate with current policy goals, whilst also potentially creating gulfs in regional take-up. The key conclusion from the spatial analysis is that location matters for uptake, through various channels that help or hinder adoption such as resources, information, and policy. Additional investment in public charging infrastructure facilities may also be needed as gaps in coverage exist, especially in rural areas to the West and South-West of the country. Although Ireland enjoys good network coverage overall, this study suggests that more charge points may be needed in some counties and Dublin city and suburbia where the number of charge points is currently disproportionate to a minimum network coverage comparable with the land area, population size, number of private vehicle owners, and travel behaviour. As the urgency for climate action intensifies in the coming decade, our spatio-temporal approach to studying uptake will not only help meet Ireland’s socio-ecological vision for the future, but also provide insights and strategies for comparable countries that are similarly placed in terms of electric vehicle adoption.
155 - PublicationBoosting Renewable Energy Technology Uptake in Ireland: A Machine Learning ApproachThis 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.
237 - PublicationAttitudes to Renewable Energy Technologies: Driving Change in Early Adopter Markets(University College Dublin. School of Economics, 2020-09)
; ; ; ; ; ; This paper explores the motivations behind the adoption of key renewable energy technologies in an early adopter market. Notwithstanding their social benefits, uptake of electric vehicles, heat pumps, and solar photovoltaic panels remains low, necessitating targeted measures to address this. We conducted a comprehensive survey of a nationally representative sample of Irish households and analysed this rich dataset using pairwise group comparisons and a factor analysis combined with a logit regression model. We found fundamental differences between adopters and non-adopters. Current adopters tend to be younger, more educated, of higher socio-economic status, and more likely to live in newer buildings of generous size than non-adopters. Environmental attitudes are an insufficient predictor of uptake - whilst non-adopters self-report as being more sustainable, adopters believe that their own decisions impact climate change. Importantly, social processes will be instrumental in future uptake. Word-of-mouth recommendation will matter greatly in communicating the use and benefits of technologies as evident from the significantly larger social networks that current adopters enjoy. Using these insights, policy incentives can be designed according to public preferences.317