Micro-macro multilevel analysis of day-to-day lifestyle and carbon emissions in UK multiple occupancy households

温室气体 消费(社会学) 多级模型 家庭收入 能源消耗 占用率 城市化 可持续消费 经济 自然资源经济学 业务 地理 工程类 经济 生态学 生产(经济) 建筑工程 计算机科学 生物 社会科学 宏观经济学 考古 机器学习 社会学 电气工程
作者
Ting Liu,Nick Shryane,Mark Elliot
出处
期刊:Sustainable Production and Consumption [Elsevier BV]
卷期号:39: 13-29 被引量:3
标识
DOI:10.1016/j.spc.2023.04.023
摘要

Far-reaching changes in daily life present a pressing need to balance energy consumption with environmental impact. Previous research on household carbon emissions generally described its contributors in disposable income, consumption pattern, and household-related lifestyle, whereas they have not fully explored how carbon emissions relate to residents' day-to-day lifestyles. Given that individual lifestyles within a household may be correlated, there is a need to disentangle the clustering effect of household members' lifestyles and their association with household carbon emissions. This study used micro-macro multilevel modelling to examine the structure of individual lifestyles and their impact on household carbon emissions for 8618 multiple occupancy households of 19,816 respondents in the UK Household Longitudinal Study dataset. The results showed that a factor capturing energy-saving lifestyle behaviours significantly reduced housing fuel use emissions and a second capturing transportation and consumption choices cut motor emissions. Interestingly, the contribution of energy-saving lifestyle in cutting down housing-fuel-using emissions becomes more pronounced when household income and household characteristics (e.g., household size, dwelling, house ownership, number of cars, urbanity, employment) were controlled for. Contrarily, the strength of green transportation and consumption lifestyle contributing to lower motor emissions was weakened after controlling for household characteristics. Findings indicated that day-to-day lifestyle not only reflects individual variability in sustainable living but also systematic household variation in carbon emissions. Knowledge of which living patterns are responsible for disproportionately high levels of carbon emissions can enhance effective targeted policy aimed at stimulating sustainable lifestyles and carbon reduction.

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