The Scale-Dependent Behaviour of Cities: A Cross-Cities Multiscale Driver Analysis of Urban Energy Use

Abstract

Hosting more than half of the world population, cities are currently responsible for two thirds of the global energy use and three quarters of the global CO2 emissions related to energy use. As humanity becomes more urbanized, urban systems are becoming a major nexus of global sustainability. Various studies have tried to pinpoint urban energy use drivers in order to find actionable levers to mitigate consumption and its associated environmental effects. Some of the approaches, mainly coming from complexity science and industrial ecology disciplines, use city-scale data to find power-laws relating to different types of energy use metrics with urban features at a city-scale. By doing so, cities’ internal complexity and heterogeneity are not explicitly addressed. Moreover, to our knowledge, no studies have yet explicitly addressed the potential scale dependency of such drivers. Drivers might not be transferable to other scales and yield undesired effects. In the present study, power-law relations are examined for 10 cities worldwide at city scale and infra-city scale, and the results are compared across scales. Relations are made across three urban features for three energy use intensity metrics. The results show that energy use drivers are in fact scale-dependent and are city-dependent for intra-urban territories.

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

“…Our results confirm earlier research showing that explanatory variables for resource consumption are sensitive to the spatial units used. So far this was shown only in studies comparing two different levels of spatial resolution, in the case of water for census area versus household or census block level (Chang et al, 2017;Ghavidelfar et al, 2017) and in the case of energy for city versus census area level (Bettignies et al, 2019;Kennedy et al, 2015). By considering four levels of spatial aggregation, we show that it matters at which level of detail within the city spatial variation of urban and resource characteristics is studied.…”

“…Hitherto, various factors have been described to underlie variability in residential energy and water demand. Studies have substantiated correlations between consumption and climate and weather conditions (Meng et al, 2020;Rasifaghihi et al, 2020), demographic and economic factors (Bettignies et al, 2019) and urban and architectural morphological characteristics (Chen, Han, & Vries, 2020;Fox, McIntosh, & Jeffrey, 2009;You & Kim, 2018). Others revealed a combination of climate, socio-economic and morphological characteristics as explanatory variables for energy (Chen et al, 2020a;Kennedy et al, 2015;Wiedenhofer, Lenzen, & Steinberger, 2013) and water consumption (Chang, Bonnette, Stoker, Crow-Miller, & Wentz, 2017;Jayarathna et al, 2017;Stoker & Rothfeder, 2014;Villarín, 2019).…”

“…To formulate sustainability strategies and affiliated interventions on consumption reduction, urban planners and designers need detailed insight into urban resource flows (Kennedy, Pincetl, & Bunje, 2011;Pulido Barrera et al, 2018;Voskamp et al, 2018). To inform such strategies and interventions, it is particularly essential to comprehend not only where resource consumption takes place, but also how the specific conditions and urban characteristics at hand affect this consumption (Bettignies et al, 2019;Broto, Allen, & Rapoport, 2012;Cui, 2018;Jayarathna et al, 2017). After all, cities are typically characterised by the heterogeneity of their demographic, socio-economic, environmental and cultural characteristics that are forming complex mechanisms which underlie differences within and amongst cities (Li & Kwan, 2018;Pickett et al, 2017).…”

“…Chang et al, 2017;Ghavidelfar, Shamseldin, & Melville, 2017;Kennedy et al, 2015). Studies that look into spatial variability of consumption using more detailed data aggregation levels than city level and that rely upon metered data are still uncommon (Bettignies et al, 2019;Bich-Ngoc & Teller, 2018;Voskamp et al, 2020). Yet, multi-level, high resolution case studies can provide insight in the scale-and city-dependent behaviour of explanatory variables for consumption, about which still little is known (Bettignies et al, 2019;Chang et al, 2017).…”

Accessibility and Connectivity Criteria for Assessing Walkability: An Application in Qazvin, Iran

“…Thus, an in-depth analysis is necessary to evaluate the quality of walking environments [1,2]. In recent years, several authors have analyzed walkability and focused on diverse topics such as (i) physical activity and health [3][4][5][6]; (ii) criteria affecting walking [7][8][9][10][11][12]; (iii) creating pedestrian indices or walkability scores [13][14][15][16][17][18]; and (iv) behavioral research [19,20].…”