Spatial dynamics of household energy consumption and local drivers in Randstad, Netherlands. (February 2018)

• Record Type: Journal Article
• Authors: Mashhoodi, Bardia
• Is Part Of: Applied geography. Volume 91(2018)
• Page Start: 123
• Page End: 130
• Publication Date: 2018-02
• Subjects: Household energy consumption -- Geographically weighted regression -- Randstad -- Netherlands; Geography -- Periodicals; Human geography -- Periodicals; Human ecology -- Periodicals; 910
• DOI: 10.1016/j.apgeog.2018.01.003
• Languages: English
• ISSNs: 0143-6228

Abstract

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.