Reducing household electricity consumption is of central relevance to climate policy given the share of 12.2% of the residential sector in greenhouse gas emissions. Drawing on data originating from the German Residential Energy Survey (GRECS), this paper estimates the contribution of individual appliances to household electricity demand using the conditional demand approach, which relies on readily obtainable information on appliance ownership. Moving beyond the standard focus of mean regression, we employ a quantile regression approach to capture the heterogeneity in the contribution of each appliance according to the conditional distribution of household electricity consumption. This heterogeneity indicates that there are quite large technical potentials for efficiency improvements and electricity conservation in private households. We also find substantial differences in the end-use shares across households originating from the opposite tails of the electricity consumption distribution, highlighting the added value of applying quantile regression methods in estimating consumption rates of electric appliances.

Discussion Paper / SFB 823;39/2015