Spatial, infrastructural and consumer characteristics underlying spatial variability in residential energy and water consumption in Amsterdam

Abstract

To design effective strategies for sustainable urban resource management, it is essential to understand which urban characteristics underlie consumption patterns. We used multiple linear regression analyses to examine sixteen factors on their explanatory power for spatial variation in residential electricity, gas and water consumption in Amsterdam. Four models per resource were used, based on distinct spatial units aggregating high-resolution data: neighbourhoods, districts, 100 m squares and 500 m squares. We found twelve explanatory variables for spatial variability in consumption in total and nine or ten per resource. The number and relative importance of explanatory variables varies with the spatial units used. Overall, neighbourhood models explain variance in consumption data best (adjusted R 2 = 0.88, 0.86, 0.74). Income level and building type stand out for having high relative importance (top 4) in all four models for two of the three resources; migration history shows an important correlation with water consumption, which was not described hitherto. We conclude that explanatory variables for resource consumption are sensitive to size and shape of spatial units used. We recommend to use future high resolution studies for different resources of interest to determine which spatial and temporal resolutions of analysis can support urban planners and designers in formulating context-specific interventions.

Urban Water Consumption: A Systematic Literature Review

“…Such management should analyse the different patterns of urban water consumption in different locations. Voskamp et al [47] state that in order to formulate effective strategies for the sustainable management of urban resources, it is extremely important to know the urban characteristics that drive consumption patterns.…”

The Impact of Urban Form and Density on Residential Energy Use: A Systematic Review

“…Housing type is a well-known variable that is the subject of extensive research. The majority of studies have concluded that the degree of detachment of the dwellings positively affects the amount of REC [8,9,30,33,35,[41][42][43][44]50,52,54,56,76,78]. In other words, families residing in detached houses consume more energy than semi-detached houses and apartments [12], while a few studies reached a contradictory conclusion [34,90].…”

“…Some researchers conclude that newer buildings use more energy than older ones [31,37,44,54,57,75,78]. They ascribe the increase in energy demand to the penetration of air-conditioning and high-consumption appliances [12].…”

“…Put differently, population density, when accounting for housing size and household size, emerges as the most comprehensive indicator of urban density, encompassing other density variables such as FAR or BCR. Many scholars conclude that increasing population density could result in improving energy efficiency per household [8,31,36,44,57,76,77]. Counter-intuitively, Ko & Radke [60] considered only cooling energy use as the dependent variable; they found a negative correlation between population density and REC in Sacramento.…”

“…On the one hand, larger buildings consume more energy than smaller ones [9,30,34,35,37,40,44,49,50,53,54,56,57,59,60,76]. Based on Reid Ewing's findings, a household residing in an 186 m 2 house consumes 16% more for space heating and 13% more for space cooling than a household living in a 93 m 2 one [9].…”