Objective environmental exposures correlate differently with recreational and transportation walking: A cross-sectional national study in the Netherlands

Wang, Zhiyong; Ettema, Dick; Helbich, Marco

doi:10.1016/j.envres.2020.110591

Cited by 23 publications

(31 citation statements)

References 48 publications

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“…Second, despite its significant positive effects on non-commuting walking, population density has no effects on commuting walking whatsoever. This finding diverges from the findings of many studies in the developed context (e.g., Christiansen et al, 2016;Kamruzzaman et al, 2016;Wang et al, 2021); these studies confirmed the significant effects of population (or residential) density on transportation walking. Unlike Western cities, where the city center has much higher density while the outer areas are characterized with low-density sprawling, many cities in China have undergone a high-density expansion (Calthorpe, 2016); a community with a higher population density is not necessarily closer to the city center, where jobs gather, and thus living in such a community does not necessarily lead to shorter commuting distance.…”

Section: Discussioncontrasting

confidence: 94%

“…Third, land-use mix is significantly correlated with the frequency and duration of both commuting and non-commuting walking, echoing the findings of a plethora of prior studies in both developed and developing contexts (e.g., Christiansen et al, 2016;Kang et al, 2017;Neves et al, 2021;Vale & Pereira, 2016;Wang et al, 2021). More mixed-use development patterns put different urban functions (commercial, public services, educational, employment, and residential) closer to each other, thus shortening the distance to commuting and non-commuting destinations and making walking more convenient (Cheng et al, 2020).…”

Section: Discussionsupporting

confidence: 65%

“…For example, an international comparative study found transportation walking of respondents in 14 cities across 10 countries to be positively associated with population density, land-use mix, intersection density, and the number of parks (Christiansen et al, 2016). Wang et al (2021) found that higher land-use mix can significantly increase walking for both transportation and recreation, whereas higher intersection density can increase transportation walking but decrease recreational walking. However, Lu et al (2017) found in Hong Kong that land-use mix and street connectivity were insignificantly associated with walking for either transport or leisure, and that population density was only positively related to walking for transportation but had an inverse-U-Shape association with leisure walking.…”

Section: Built Environment Correlates Of Multipurpose Walkingmentioning

confidence: 96%

“…Specifically, the 5Ds framework tries to delineate the built environment from 5 dimensions, including density, diversity (land-use mix), design (pedestrian-friendly design), destination accessibility, and distance to the transit (Ewing & Cervero, 2001, 2010Ewing et al, 2009). In general, higher density (population and employment) (Bentley et al, 2018), higher land-use mix (Wang et al, 2021), better design (often measured by intersection density) (Kang, 2017;Koohsari et al, 2017), higher destination accessibility (distance to CBD) (Chudyk et al, 2015;Ewing et al, 2015), and a shorter distance to transit (Kang, 2018;Wang & Cao, 2017) are widely found to be associated with a higher frequency or longer duration of walking. However, inconsistencies and discrepancies exist, mainly due to the differences between developed and developing contexts (Boakye-Dankwa, Barnett et al, 2019;Boakye-Dankwa, Nathan, et al, 2019;Lu et al, 2018;Lu et al, 2017), which entails more studies in the understudied developing context.…”

Section: Built Environment Correlates Of Multipurpose Walkingmentioning

confidence: 99%

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Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

Liu

Zhou²,

Xiao

2021

JTLU

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As a sustainable mode of travel, walking for transportation has multiple environmental, social, and health-related benefits. In existing studies, however, such walking has rarely been differentiated between commuting and non-commuting trips. Using multilevel zero-inflated negative binomial regression and multilevel Tobit regression models, this study empirically examines the frequency and duration of commuting and non-commuting walking and their correlates in Xiamen, China. It finds that (1) non-commuting walking, on average, has a higher frequency and longer duration than commuting walking; (2) most socio-demographic variables are significant predictors, and age, occupation, and family size have opposite-direction effects on commuting and non-commuting walking; and (3) different sets of built environment variables are correlated with commuting and non-commuting walking, and the built environment collectively influences the latter more significantly than the former. The findings provide useful references for customized interventions concerning promoting commuting and non-commuting walking.

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Section: Discussioncontrasting

confidence: 94%

Section: Discussionsupporting

confidence: 65%

Section: Built Environment Correlates Of Multipurpose Walkingmentioning

confidence: 96%

Section: Built Environment Correlates Of Multipurpose Walkingmentioning

confidence: 99%

See 2 more Smart Citations

Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

Liu

Zhou²,

Xiao

2021

JTLU

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“…The finding that NDVI was positively associated with transport walking at low densities and with recreational walking at medium densities was consistent with previous studies, but the finding that NDVI was negatively associated with recreational walking at high densities differed from previous studies ( 37 – 39 ). This phenomenon could be explained by the fact that recreation sites (e.g., pocket parks) with poorer environments lacking shelter space in high-density areas are located in areas with higher levels of vegetation, resulting in less recreational walking ( 40 ). We found that street connectivity and road crossing density were negatively correlated with recreational walking in small buffer sizes at high densities but positively correlated with medium density areas at high densities with buffers of 1,000 m, which is not consistent with previous studies ( 18 ).…”

Section: Discussionmentioning

confidence: 99%

Research on the Difference Between Recreational Walking and Transport Walking Among the Elderly in Mega Cities With Different Density Zones: The Case of Guangzhou City

Zang

Qiu

Xian

et al. 2021

Front. Public Health

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Walking is the easiest method of physical activity for older people, and current research has demonstrated that the built environment is differently associated with recreational and transport walking. This study modelled the environmental characteristics of three different building density zones in Guangzhou city at low, medium, and high densities, and examined the differences in walking among older people in the three zones. The International Physical Activity Questionnaire (IPAQ) was used to investigate the recreational and transport walking time of older people aged 65 years and above for the past week, for a total of three density zones (N = 597) and was analysed as a dependent variable. Geographic Information Systems (GIS) was used to identify 300, 500, 800, and 1,000 m buffers and to assess differences between recreational and transport walking in terms of the built environment [e.g., land-use mix, street connectivity, Normalised Difference Vegetation Index (NDVI) data]. The data were processed and validated using the SPSS software to calculate Pearson's correlation models and stepwise regression models between recreation and transit walking and the built environment. The results found that land use mix and NDVI were positively correlated with transport walking in low-density areas and that transport walking was negatively correlated with roadway mediated centrality (BtE) and Point-of-Interest (PoI) density. Moreover, recreational walking in medium density areas was negatively correlated with self-rated health, road intersection density, and PoI density while positively correlated with educational attainment, population density, land use mix, street connectivity, PoIs density, and NDVI. Transport walking was negatively correlated with land-use mix, number of road crossings while positively correlated with commercial PoI density. Street connectivity, road intersection density, DNVI, and recreational walking in high-density areas showed negative correlations. Moreover, the built environment of older people in Guangzhou differed between recreational and transport walking at different densities. The richness of PoIs has different effects on different types of walking.

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Car energy efficiency and emissions in the built environment

ten Dam,

Bahamonde-Birke,

Ettema

et al. 2024

Transportation

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To mitigate climate change and safeguard energy security, it is necessary to limit car dependence, reduce car weights, and shift to alternative car powertrains. This study therefore looked into the real-world specific energy consumption and $$\hbox {CO}_{{2}}$$ CO 2 emissions of cars in the Netherlands. Next, it analyzed how sociodemographic and built environment variables influence energy-relevant car type choices with a multilevel discrete choice modeling framework. At a first stage, this framework considered the number of cars owned. Conditional on that decision, it simultaneously considered choices between different car fuel types and weight categories. The results showed that small, lower-income households with few male or older members in non-green (urban) environments were more likely to own light (efficient) vehicles. Remote households had a preference for light and diesel vehicles. In contrast, households with private parking tended to own heavy and electric vehicles. Finally, owning multiple cars was correlated with both non-urban living and heavy car preferences. The combined effect was a mild preference for energy efficient vehicles in urban areas. Previous studies that omitted vehicle energy efficiency thus slightly underestimated urban planning’s environmental impact. However, our results indicate that the built environment has a greater effect on travel energy use through the number of cars owned than through car specific energy consumption. The bias in the official vehicle energy data was also much larger than the total influence of the built environment on car specific energy consumption.

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Objective environmental exposures correlate differently with recreational and transportation walking: A cross-sectional national study in the Netherlands

Cited by 23 publications

References 48 publications

Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

Built environment correlates of walking for transportation: Differences between commuting and non-commuting trips

Research on the Difference Between Recreational Walking and Transport Walking Among the Elderly in Mega Cities With Different Density Zones: The Case of Guangzhou City

Car energy efficiency and emissions in the built environment

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