Examining the effects of the built environment and residential self-selection on commuting trips and the related CO 2 emissions: An empirical study in Guangzhou, China

Cao, Xiaoshu; Yang, Wenyue

doi:10.1016/j.trd.2017.02.003

Cited by 126 publications

(90 citation statements)

References 68 publications

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“…Therefore, this group consumes the most energy on road. Our results verify the assumption that the effects of age structure are partly decided by household income or wealth levels and are consistent with the findings from Cao and Yang (2017) and Zhu and Peng (2012). For example, Cao and Yang (2017) suggest that age has a positive effect on environment, and Zhu and Peng (2012) suggest that the working group (age 16-64) has more positive effects on the environment than other cohorts.…”

Section: Discussionsupporting

confidence: 89%

“…Our results verify the assumption that the effects of age structure are partly decided by household income or wealth levels and are consistent with the findings from Cao and Yang (2017) and Zhu and Peng (2012). For example, Cao and Yang (2017) suggest that age has a positive effect on environment, and Zhu and Peng (2012) suggest that the working group (age 16-64) has more positive effects on the environment than other cohorts. Furthermore, the elasticity of urban density with respect to per capita road energy use is negative given the urbanization rate and the ratio of low residential housing stock which implies that compact cities contribute to decreases in road energy use per capita.…”

Section: Discussionsupporting

confidence: 89%

“…For example, Liddle (2011) divided the population into five age groups (< 20, 20-34, 35-49, 50-69, and 70 ≥) whereas Zhu and Peng (2012) integrated the working population into one group. Further, Cao and Yang (2017) classified the age of 44 as an important node (16-24, 25-34, 35-44, 45 >). The classifications of Cao and Yang (2017) and Zhu and Peng (2012), combined with the Norwegian factors, resulted in our division into four groups.…”

Section: Data and Modelsmentioning

confidence: 99%

“…Further, Cao and Yang (2017) classified the age of 44 as an important node (16-24, 25-34, 35-44, 45 >). The classifications of Cao and Yang (2017) and Zhu and Peng (2012), combined with the Norwegian factors, resulted in our division into four groups. The study period (2006)(2007)(2008)(2009) Norway by merging the municipalities into the large ones (Parliament, 2017).…”

Section: Data and Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

Liu

Huang

Kaloudis

et al. 2017

Transportation Research Part D: Transport and Environment

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The relationship between urbanization, energy use, and CO2 emissions has been extensively studied in recent years, however little attention paid to the differences in urban forms. Previous studies implicitly assume that the urban form is homogenous across different urban areas. Such an assumption is questionable as urban form can have many different facets. This paper investigates the effects of urbanization on the road transport energy use by considering different urban forms from a dataset of (3) there is a non-linear (quadratic) relationship between road energy use per capita and urban population. This implies that an increase in total municipality population over a specific turning point can result in a decrease in road energy use per capita. However, (4) the ratio of urban residential buildings with private gardens has a negative and significant influence on road transport energy use.This implies that there may be a trade-off between compact and sprawl city development strategies, highlighting that sustainable energy use requires further investigation.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 89%

Section: Data and Modelsmentioning

confidence: 99%

Section: Data and Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

Liu

Huang

Kaloudis

et al. 2017

Transportation Research Part D: Transport and Environment

View full text Add to dashboard Cite

show abstract

“…Some of these factors are believed to be even more important than the built environment [43][44][45][46][47].…”

Section: Other Factors Influencing Car Ownership and Usementioning

confidence: 99%

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

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Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

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Urban Advantage? Sustainability Trade-Offs Across and Within the Intra-Urban Space

Lerpold

Sjöberg

Tang

2021

Sustainable Consumption and Production, Volume I

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Abstract“Sustainable cities” as a singular concept may very well be a utopian vision impossible to realise in a broader sense. In this chapter, we review the literature on urban sustainability highlighting the complexities and trade-offs between and within the 3 Es—ecology, economy and equality. In particular, we focus here on the intra-urban dimensions of density, mobility, the built environment and housing, lifestyle trends and gentrification along with social sustainability issues of crime, homelessness and community. While gains from increased size and density can be had, there are also many outcomes that depend on urban morphology and the consequences of spatial sorting. Positive outcomes generated by density and efficiency may be offset by, for instance, less sustainable construction materials or increased income inequality. In particular, rebound effects are often overlooked. Hence, it often becomes an empirical issue whether the potential for sustainability gains materialise. Furthermore, as assessed from a more holistic 3 Es’ view, where social sustainability is as important as environmental sustainability, the potential of a “sustainable city” may be a victim of trade-offs that are difficult to resolve.

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Examining the effects of the built environment and residential self-selection on commuting trips and the related CO 2 emissions: An empirical study in Guangzhou, China

Cited by 126 publications

References 68 publications

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

Does urbanization lead to less energy use on road transport? Evidence from municipalities in Norway

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

Urban Advantage? Sustainability Trade-Offs Across and Within the Intra-Urban Space

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