Estimation of Greenhouse Gas Emissions of Taxis and the Nonlinear Influence of Built Environment Considering Spatiotemporal Heterogeneity

Changwei Yuan, Ningyuan Ma (), Xinhua Mao, Yaxin Duan, Jiannan Zhao, Shengxuan Ding and Lu Sun
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Changwei Yuan: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Ningyuan Ma: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Xinhua Mao: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Yaxin Duan: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Jiannan Zhao: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Shengxuan Ding: Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr #211, Orlando, FL 32816, USA
Lu Sun: Xi’an Transportation Development Research Center, Xi’an 710082, China

Sustainability, 2024, vol. 16, issue 16, 1-29

Abstract: The fuel consumption and greenhouse gas (GHG) emission patterns of taxis are in accordance with the urban structure and daily travel footprints of residents. With taxi trajectory data from the intelligent transportation system in Xi’an, China, this study excludes trajectories from electric taxis to accurately estimate GHG emissions of taxis. A gradient boosting decision tree (GBDT) model is employed to examine the nonlinear influence of the built environment (BE) on the GHG emissions of taxis on weekdays and weekends in various urban areas. The research findings indicate that the GHG emissions of taxis within the research area exhibit peak levels during the time intervals of 7:00–9:00, 12:00–14:00, and 23:00–0:00, with notably higher emission factors on weekends than on weekdays. Moreover, a clear nonlinear association exists between BE elements and GHG emissions, with a distinct impact threshold. In the different urban areas, the factors that influence emissions exhibit spatial and temporal heterogeneity. Metro/bus/taxi stops density, residential density, and road network density are the most influential BE elements impacting GHG emissions. Road network density has both positive and negative influences on the GHG emissions in various urban areas. Increasing the road network density in subcentral urban areas and increasing the mixed degree of urban functions in newly developed urban centers to 1.85 or higher can help reduce GHG emissions. These findings provide valuable insights for reducing emissions in urban transportation and promoting sustainable urban development by adjusting urban functional areas.

Keywords: sustainability; built environment; greenhouse gas emissions; machine learning; nonlinear influence; spatiotemporal heterogeneity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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