Assessing low-carbon lifestyles of consumer segments: An integrated analysis of consumer expenditure survey and time-use survey microdata

Event: 10th International Conference on Industrial Ecology
Date: July 9, 2019

In this presentation, the characteristics of different consumer segments and their carbon footprints will be examined based on the analysis of consumer expenditure and time-use survey data. It utilises anonymised survey microdata and carbon intensity from existing EEIOA databases and focuses on in-depth analysis of the relationships between consumer lifestyles, observed from socio-economic and time-use characteristics, and their impacts on climate change. In addition to multivariate regression analysis, the application of statistical matching and clustering analysis will be examined. Case countries are Japan and the United States, which are relatively less studied countries from a consumer footprint perspective as compared to their European counterparts. Preliminary results demonstrate that various socio-economic characteristics of households observe in studies of other countries, such as economic affluence, family composition, residential location, housing type, vehicle ownership, are relevant determinants of carbon footprints in the case countries. The application of clustering analysis also uncovers that the distribution of carbon footprints is large unequal across consumer segments, by approximately 4-5 factors. It also implies the necessity to evaluate consumer lifestyles as a whole package and that individual predictor identified in regression analysis cannot be solely interpreted because individual factors are interrelated. The time-use characteristics of households, such as work hours, leisure, and time spent outside the home, are also identified as relevant factors to carbon footprints. The results suggest the need to more closely investigate the characteristics of low-carbon lifestyles based on consumer survey microdata including from time-use perspective and integrated analysis of consumer groups. They also demonstrate the potential use of statistical matching and cluster analysis methods in analyzing consumer carbon footprints. The analysis also implies the importance of the equitability perspective in addressing climate change mitigation.