%0 Figure %A Heinonen, Jukka %A Jalas, Mikko %A K Juntunen, Jouni %A Ala-Mantila, Sanna %A Junnila, Seppo %D 2013 %T The sampling process and the utilized groupings %U https://iop.figshare.com/articles/figure/_The_sampling_process_and_the_utilized_groupings/1011930 %R 10.6084/m9.figshare.1011930.v1 %2 https://iop.figshare.com/ndownloader/files/1479755 %K capita basis %K income levels %K expenditure data %K GHG emissions %K GHG implications %K variables housing type %K part II %K lifestyle %K carbon footprints %K greenhouse gas %K consumption volumes %K consumption activities %K family sizes %K consumption choices %K area types %K budget constraints %K GHG assessment method %K GHG assessment %K housing type %K Environmental Science %X

Figure 1. The sampling process and the utilized groupings.

Abstract

The relationship between urban form and greenhouse gas (GHG) emissions has been studied extensively during the last two decades. The prevailing paradigm arising from these studies is that a dense or compact urban form would best enable low-carbon living. However, the vast majority of these studies have actually concentrated on transportation and/or housing energy, whereas a growing number of studies argue that the GHG implications of other consumption should be taken into account and the relationships evaluated. With this two-part study of four different area types in Finland we illustrate the importance of including all the consumption activities into the GHG assessment. Furthermore, we add to the discussion the idea that consumption choices, or lifestyles, and the resulting GHGs are not just a product of the values of individuals but actually tied to the form of the surrounding urbanization: that is, lifestyles are situated. In part I (Heinonen et al 2013 Environ. Res. Lett. 8 025003) we looked into this situation in Finland, showing how the residents of the most urbanized areas bring about the highest GHG emissions due to their higher consumption volumes and the economies-of-scale advantages in the less urbanized areas. In part II here, we concentrate only on the middle-income segment and look for differences in the lifestyles when the budget constraints are equal. Here we also add the variables housing type and motorization into the assessment. The same time-use and private expenditure data as in part I and the same GHG assessment method are used here to maintain high transparency and comparability between the two parts. The results of the study imply that larger family sizes and economies-of-scale effects in the less dense areas offset the advantages of more dense living when the emissions are assessed on per capita basis. Also, at equal income levels the carbon footprints vary surprisingly little due to complementary effects of the majority of low-carbon lifestyle choices. Motorization was still found to increase the emissions, but a similar pattern regarding housing type was not found.

%I IOP Publishing