The impact of Built Environment Characteristics on Metropolitans Energy Consumption: An Example of Greater Cairo Metropolitan Region

Osman, T. A.; Divigalpitiya, Prasanna; Osman, Mustafa M.

doi:10.3390/buildings6020012

Cited by 10 publications

(6 citation statements)

References 42 publications

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“…Public transport service is normally believed to be an effective way to encourage non-vehicle travel [ 30 ]. However, new findings from Atlanta suggest that a new subway expansion would increase commuting trips, and non-vehicle trips would not be reduced as expected [ 31 ].…”

Section: Literature Reviewmentioning

confidence: 99%

The spatially heterogeneous and double-edged effect of the built environment on commuting distance: Home-based and work-based perspectives

2022

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Rich literature has examined the impact of the built environment on commuting distance. Linear models assume that the influence of the built environment is spatially homogeneous. However, given the spatial heterogeneity of urban space, conclusions might be different or even be contrary. The influence of the built environment might also be different by home and work locations. To explore the spatially heterogeneous effect of the built environment from both home-based and work-based perspectives, this study applied large-scale cellular cellphone data in Guangzhou, China. Commuting was measured by decay parameters of probabilistic distributions of commuting distances. Geographically weighted regression models were applied to examine the spatially heterogeneous effect, differentiated by home-based and work-based perspectives. Results confirmed that the impact of the built environment on commuting distance is spatially heterogeneous. The urban space is classified into clusters of central areas, inner suburbs, and outer suburbs. Results also revealed the double-edged effect of the built environment. Residential population, recreation facilities, and mixed development are residence-attractive factors that increase the home-based commuting distance and decrease the work-based commuting distance. Work population and transport facilities are work-attractive factors that decrease home-based commuting distance and increase work-based commuting distance. The results further provide evidence to support area-based policies in urban planning practice.

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Section: Literature Reviewmentioning

confidence: 99%

The spatially heterogeneous and double-edged effect of the built environment on commuting distance: Home-based and work-based perspectives

2022

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“…Because the tourism industry is closely related to urban development, the light index, which can represent regional prosperity, was selected as a weighting factor. Energy consumption is related to population and land use type [42]; therefore, the corresponding indexes were selected, such as the industrial electricity/water consumption-industrial land area, residential electricity/water consumption-residential land area. According to the key weighting factors (Table 2), socioeconomic data were allocated using Eq 1, and spatial socioeconomic data with a resolution of 2 � 2 km were obtained.…”

Section: Plos Onementioning

confidence: 99%

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

Liu

Wang

et al. 2020

PLoS ONE

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The value of ecosystem services is affected by increasing human activities. However, the anthropogenic driving mechanisms of ecosystem services are poorly understood. Here, we established a deep learning model to approximate the ecosystem service value (ESV) of Nanjing City using 23 socioeconomic factors. A multi-view analysis was then conducted on feasible impact mechanisms using model disassembly. The results indicated that certain factors had their own significant and independent effects on ESV, such as the proportion of water areas in the land-use structure and the output value of the secondary industry. The proportion of ecological water should be increased as much as possible, whereas the output value of the secondary industry should be reasonably controlled in Nanjing. Other intrinsically related factors were likely to be composited together to affect ESV, such as industrial water consumption and industrial electricity consumption. In Nanjing, simultaneously optimizing socioeconomic factors related to city size, resources, and energy use efficiency likely represents an effective management strategy for maintaining and enhancing regional ecological service capabilities. The results of this work suggest that deep learning is an effective method of deepening studies on the prediction of ESV trends and human-driven mechanisms.

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“…They proved that density has a negative effect on the number of vehicle tenure, whereas it has a positive impact on the amount of trip making. T. Osman, Arima, Divigalpitiya and M. Osman (2016) investigated the effect of the built environment and socioeconomic driving factors on the fuel consumption through a case study of the Cairo metropolitan region, Egypt using Structural equation modeling (SEM). The results affirmed that household income and the family size especially number of adults as driving factors increase the energy consumption but in contrast with the other studies population slightly affect the fuel consumption as well as eco-friendly driving factors.…”

Section: Materials Studiedmentioning

confidence: 99%

The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran

Rezaeian¹,

Rahnama²,

Javan³

et al. 2017

JSD

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Concerns over rising fuel consumption have prompted research into the influences of built environments on travel behavior. On the basis of data from origin-destination(OD) travel survey data of Mashhad (74287 trip data in 2011) and using Geographically Weighted Regression, socio-demographic characteristics, are shown to be strongly and positively associated with the fuel consumption per capita (car ownership elasticity=0.347878); we also found a positive association between distance to center and designs that are not pedestrian friendly with fuel consumption (average block size=0.147489, distance to center =0.334953) Although the study demonstrates a moderately strong negative elasticity between population density and the fuel consumption(population density = -0.259335). It suggests that the largest energy consumption reductions would come from creating compact communities which have land-use diversity and more walkable areas with pedestrian cycling infrastructure around all of the stations along transit lines.In order to enhance a sustainable urban plan, the socio-economic driving factors should be considered as one of the main element of energy consumption as well.

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The impact of Built Environment Characteristics on Metropolitans Energy Consumption: An Example of Greater Cairo Metropolitan Region

Cited by 10 publications

References 42 publications

The spatially heterogeneous and double-edged effect of the built environment on commuting distance: Home-based and work-based perspectives

The spatially heterogeneous and double-edged effect of the built environment on commuting distance: Home-based and work-based perspectives

Deep learning: To better understand how human activities affect the value of ecosystem services—A case study of Nanjing

The Impact of Built Environment Characteristics on Energy Consumption Using Geographically Weighted Regression in Mashhad, Iran

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