Socioeconomic and Cultural Patterns in Heat Consumption

Within the last decade, an increasing number of studies around the world have made use of regression-based models based on specific geographic regions for the purpose of finding correlations between socioeconomic and cultural factors and heat consumption in order to turn the heat consumption practices of households in a more energy-reducing direction. By exploring such correlations, a new focus has recently been given attention; the tendency for socioeconomic and cultural factors to have indirect effects on heat consumption through dwelling choices and to have direct effect on heat consumption through practices, the sum of which is called total effects.
To reflect upon the knowledge that this indirect and direct effect perspective can provide for understanding heat consumption differences in a specific geographic region, this study explores spatial correlations between the heat consumption and the socioeconomic and cultural characteristics of households in the City of Copenhagen. The purpose is to analyse how the social, cultural, and economic structures affect heat consumption differences in the city and to find out which household types need political targeting in order to reach the goal of a 20 % decrease in the Copenhagen heat consumption in 2025 compared to 2010.
Using a combination of choropleth maps, Pearson’s R, and regression analyses, the total effects as well as direct effects of socioeconomic and cultural variables on heat consumption per capita are analysed using aggregated and averaged heat consumption data from HOFOR. A life-cycle pattern is found to describe heat consumption per capita where heat consumption, residential space, and income increase with the age of children as well as with the age of adults. However, this life-cycle pattern is challenged by the existence of an income divide between a new generation of wealthy families with small children living in newer, semi-detached houses with better energy labels and non-western ethnic people tending to have low income. The income divide affects the economic motivations and options for decreasing heat consumption per capita.
A limitation to the study is that the heat consumption data, despite being based on standard values that have been calculated from actual measurements, is not first and foremost spatially aggregated. With spatially aggregated, actual heat consumption data, it would be possible to explore the extent to which heat consumption differences arrive from energy saving behaviour and the extent to which they arrive from dwelling insulation qualities. However, the study exemplifies how direct effects can show the direct influence of variables on heat consumption and the size of the influence, and how indirect effects can illuminate patterns of connected variables.