Áhrif byggðagerða á neyslutengd kolefnisspor á Norðurlöndum

A substantial body of research has examined the relationship between urbanization and carbon emissions. While higher-density urban areas are often found to be more energy efficient—particularly in terms of transportation and housing energy use—this efficiency can be overridden by higher levels of consumption, typically associated with urban lifestyles. At the same time, less is known about emission patterns across other settlement types and density gradients. This study aims to explore the relationship between the consumption-based carbon footprints and the settlement type in the Nordic countries, including cities, dense towns, semi-dense towns, suburban/peri-urban areas, villages, dispersed rural areas and mostly uninhabited areas. The consumption-based carbon footprints were calculated using data from an online survey of ~7,400 respondents and assessed through a hybrid lifecycle assessment method (LCA), capturing both direct and indirect emissions. Bivariate analysis is used to compare the footprints across the different settlement types, while regression analysis is employed to assess the unique influence of settlement type and other sociodemographic factors on household emissions. The results indicate that dense (6.9 tCO₂e/CU) and semi-dense towns (7.0 tCO₂e/CU) have the lowest footprints, followed by cities (7.1 tCO₂e/CU), while the highest footprints are observed in thinly populated areas like villages (8.2 tCO₂e/CU) and mostly uninhabited areas (8.3 tCO₂e/CU). Settlement type was found to be a significant predictor of the total CBCFs. After controlling for socioeconomic factors cities had the lowest total CBCF, with suburban and rural areas exhibiting 12-18% higher emissions. Even though settlement type was a statistically significant predictor, other sociodemographic characteristics were found to exert stronger influence on total CBCFs, as well as on the CBCFs of certain consumption domains. Housing emissions were more strongly influenced by country-level characteristics, and emissions from goods and services were driven mainly by income. These findings suggest that effective mitigation strategies must combine spatial planning with demand-side measures that address affluence and lifestyle-driven consumption.