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Author:

Tian, D. (Tian, D..) | Zhang, J. (Zhang, J..) | Li, B. (Li, B..) | Xia, C. (Xia, C..) | Zhu, Y. (Zhu, Y..) | Zhou, C. (Zhou, C..) | Wang, Y. (Wang, Y..) | Liu, X. (Liu, X..) | Yang, M. (Yang, M..)

Indexed by:

EI Scopus SCIE

Abstract:

Commuting carbon emissions (CCE) constitute a significant portion of urban carbon emissions and exert a notable influence on urban land use policies and spatial planning strategies. Traditional methods for estimating CCEs have many disadvantages and are less accurate in terms of space and time. In this study, a typical weekday in the main urban area of Beijing is selected as the research object. First, taxi CCEs are estimated by an improved VSP model based on GPS trajectory data. Second, taxi CCEs are obtained by filtering the commuting trajectories based on POI data and taxi trajectories. Finally, the spatial distribution characteristics of taxi CCEs are analysed by spatial autocorrelation. The results show that (1) the morning peak commuting carbon emission in Beijing reaches 5338.04 kg/h, accounting for 26.13 % of the total carbon emission of cab trips; the evening peak commuting carbon emission reaches 3858.73 kg/h, accounting for 18.89 % of the total carbon emission of cab trips; (2) the spatial distribution of CCE in Beijing shows that the western part of the core area is higher, while the northeast and southwest of the central city are higher; (3) High-value commuting carbon emission areas are mainly concentrated in regions with a more developed financial industry; the carbon emissions from the city ring road and airport highway significantly surpass those of other areas. This research introduces an innovative approach to examine CCE and measure it within various functional zones of urban areas. The findings of this study can serve as valuable guidance for informing future urban spatial configurations and land use policies, leading to more effective and sustainable urban development. © 2024

Keyword:

Commuting carbon emissions VSP model Beijing Urban land use GPS trajectory data

Author Community:

  • [ 1 ] [Tian D.]School of Architecture and Urban Planning, Beijing University of Civil, Engineering and Architecture, Beijing, 100044, China
  • [ 2 ] [Tian D.]Research Center for Urban Big Data Applications, Beijing University of Civil, Engineering and Architecture, Beijing, 100044, China
  • [ 3 ] [Zhang J.]Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Li B.]Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 5 ] [Xia C.]Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 6 ] [Zhu Y.]School of Architecture and Urban Planning, Beijing University of Civil, Engineering and Architecture, Beijing, 100044, China
  • [ 7 ] [Zhou C.]Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 8 ] [Wang Y.]Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 9 ] [Liu X.]Research Center for Urban Big Data Applications, Beijing University of Civil, Engineering and Architecture, Beijing, 100044, China
  • [ 10 ] [Yang M.]School of Architecture and Urban Planning, Beijing University of Civil, Engineering and Architecture, Beijing, 100044, China

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Source :

Ecological Indicators

ISSN: 1470-160X

Year: 2024

Volume: 159

6 . 9 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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