Situated lifestyles: I. How lifestyles change along with the level of urbanization and what the greenhouse gas implications are—a study of Finland

Abstract

An extensive body of literature demonstrates how higher density leads to more efficient energy use and lower greenhouse gas (GHG) emissions from transport and housing. However, our current understanding seems to be limited on the relationships between the urban form and the GHG emissions, namely how the urban form affects the lifestyles and thus the GHGs on a much wider scale than traditionally assumed. The urban form affects housing types, commuting distances, availability of different goods and services, social contacts and emulation, and the alternatives for pastimes, meaning that lifestyles are actually situated instead of personal projects. As almost all consumption, be it services or products, involves GHG emissions, looking at the emissions from transport and housing may not be sufficient to define whether one form would be more desirable than another. In the paper we analyze the urban form-lifestyle relationships in Finland together with the resulting GHG implications, employing both monetary expenditure and time use data to portray lifestyles in different basic urban forms: metropolitan, urban, semi-urban and rural. The GHG implications are assessed with a life cycle assessment (LCA) method that takes into account the GHG emissions embedded in different goods and services. The paper depicts that, while the direct emissions from transportation and housing energy slightly decrease with higher density, the reductions can be easily overridden by sources of indirect emissions. We also highlight that the indirect emissions actually seem to have strong structural determinants, often undermined in studies concerning sustainable urban forms. Further, we introduce a concept of 'parallel consumption' to explain how the lifestyles especially in more urbanized areas lead to multiplication of consumption outside of the limits of time budget and the living environment. This is also part I of a two-stage study. In part II we will depict how various other contextual and socioeconomic variables are actually also very important to take into account, and how diverse GHG mitigation strategies would be needed for different types of area in different locations towards a low-carbon future.

City Carbon Footprint Networks

“…The decomposition of "origin" and "destination" of embodied emissions by sector in the carbon map is just an additional step introduced by but consumption-based emissions (or CFs) of cities have been calculated with (multi-region) input-output analysis before [42][43][44][45][46]. The MRIO table for this study was derived from the Australian Industrial Ecology Virtual Laboratory [47] (see the heat map in the Figure S1), which can distinguish regions with a population of 10,000 residents (denoted SA2 region) [48].…”

How Does the Environmental Load of Household Consumption Depend on Residential Location?

“…Food, transport-related services, and recreation are the top single contributors to indirect energy use and carbon emissions in various countries [23,64,66]. Several studies have shown that despite the benefits of urban density, metropolitan and inner-city living is linked to intensive consumption patterns in relation to non-energy products, which increase the indirect carbon emissions of households, thus diminishing the resource-use 'win' from lower levels of driving [15,16,[67][68][69][70]. Other studies have shown either opposite results due to the different distribution of affluence [71,72] or marginal impact of urbanity [66].…”

Culture, Carbon, and Climate Change: A Class Analysis of Climate Change Belief, Lifestyle Lock-in, and Personal Carbon Footprint

“…Concerning the signifi cant negative correlation between census tract population density and both vehicle and total GHGEs (i.e. the more dense a census tract, the less car usage), there is evidence in the literature for this relationship: those in more urban areas "share" more resources, have greater access to public transportation, and drive less (Dargay, 2002;Heinonen et al, 2013;Jones and Kammen, 2014). 39 Th ere is also a signifi cant negative correlation between census tract percentage of bachelor degrees (or above) and vehicle footprint; apparently census tracts with higher levels of education are producing relatively lower driving impacts.…”