Spatial dynamics of household energy consumption and local drivers in Randstad, Netherlands

Abstract

This study is an attempt to bridge an eminent knowledge gap in the empirical studies on Household Energy Consumption (HEC): the previous studies implicitly presumed that the relationships between HEC and the geographic drivers is uniform in different locations of a given study-area, and thus have tried to disclose such everywhere-true relationships. However, the possible spatially varying relationships between the two remain unexplored. By studying the performance of a conventional OLS model and a GWR model-adjusted R 2 , randomness of distribution of residual (tested by Moran's I), AIC and spatial stationary index of the geographic drivers, ANOVA test of residuals-this study demonstrates that the GWR model substantially provides a better understanding of HEC in the Randstad. In this respect, the core conclusion of this study is: the relationships between HEC and geographic drivers are spatially varying and therefore needed to be studied by means of geographically weighted models. Additionally, this study shows that considering spatially varying relationships between HEC and geographic drivers, by application of hierarchical clustering, the areas of the Randstad can be classified in four clusters: building age and income impact areas, building density impact areas, population density and built-up impact areas, household size and income impact areas. Highlights  The geographic drivers of household energy consumption are spatially varying  Household energy consumption has to be studied by geographically weighted models  Policies regarding household energy consumption need to be location-specific

Spatial homogeneity and heterogeneity of energy poverty: a neglected dimension

“…By application of aspatial methods, most of the previous studies have effectively ignored spatial heterogeneity of the determinants of energy poverty. A variety of previous studies, meanwhile, have shown that the best understanding of a wide range of phenomenaamong them academic achievement (Figueroa, Lim, and Lee 2016), effects of ozone pollution (Lin and Lu 2009), vulnerability to terrorism (Eisman, Gebelein, and Breslin 2017), household energy consumption (Mashhoodi 2018;Mashhoodi and van Timmeren 2018) and social vulnerability in slums (Jankowska, Weeks, and Engstrom 2011) is achieved only when spatial heterogeneity of the effects is taken into consideration. The result of this study is beneficial for future studies on energy poverty; there is a central question to start with: what are the spatially homogeneous and heterogeneous determinants of energy poverty?…”

Local determinants of household gas and electricity consumption in Randstad region, Netherlands: application of geographically weighted regression

“…A regional study on household's final energy use in the Netherlands show that quality of buildings and income has a greater impact on HEC of rural areas than urbanized areas. The study conclude that in the suburban areas population density is a significant determinant of HEC, whereas in highly urbanized areas household size or building density are the prominent determinants [29].…”

Features, Driving Forces and Transition of the Household Energy Consumption in China: A Review

“…Curbing levels of energy consumption has been of interest to policymakers since the 1973 and 1979 geopolitical turmoil [27]. Currently, the growing global energy consumption puts increasing pressure on local environmental pollution and global climate change, while the share of households' energy consumption in total energy utilization is substantial [9,10], because households play an increasingly important role in energy consumption and mitigating climate change not only by pro-environmental behaviors but also by acting as a social-economic unit [28].…”