Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China

Guanwei Zhao, Zeyu Pan and Muzhuang Yang ()
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Guanwei Zhao: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Zeyu Pan: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Muzhuang Yang: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China

IJERPH, 2022, vol. 19, issue 24, 1-19

Abstract: Understanding the impact of the urban built environment on taxis’ emissions is crucial for sustainable transportation. However, the marginal effects and spatial heterogeneity of this impact is worth noting. To this end, we calculated the taxis’ emissions on weekdays and weekends in Chengdu, China, and investigated the impact of the built environment on taxis’ emissions by applying multi-source data and several spatial regression models. The results showed that the taxis’ daily emissions on weekdays were higher than the emissions on weekends. The time heterogeneity of hourly taxis’ emissions was not significant, while the spatial heterogeneity of taxis’ emissions was significant. Except the HHI, the selected built environment variables both had a significant positive effect on taxis’ emissions on the global scale. There was a marginal effect of some built environment variables on taxis’ emissions, such as the density of bus stops and population density. The former exhibited an inhibitory effect on taxis’ emissions only when it was greater than 9 stops/km 2 , while the latter showed an inhibitory effect only in the range between 16,000 people/km 2 and 22,000 people/km 2 . There were some spatial variations in the effects of built environment factors on taxis’ emissions, with HHI, road density, and accommodation service facilities density showing the most significant variation. The marginal effect and spatial variation of the impact needs to be considered when developing strategies to reduce taxis’ pollutant emissions.

Keywords: taxis’ emissions; built environment; marginal effect; spatial nonstationary; big data; spatiotemporal (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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