Abstract
Facing global climate change, reducing carbon emissions from transportation has become an important part of building sustainable cities. Study shows that road is the main source of transport carbon emissions, accounting for 71.7% of the total transport carbon dioxide emissions. Therefore, from the perspective of road structure, the study of transport carbon emission efficiency is beneficial to understand the transport carbon emission. Taking Shenzhen as the research region, this paper establishes a high-resolution road traffic carbon emission inventory by modifying the COPERT model with the energy consumption factor parameters and explores the road structure using space syntax. We analyze the interaction between the road structure and road carbon emission. The results show that: the road carbon emission intensity of Shenzhen has the distribution characteristics of “the core is strong and the edge is weak”. Trunk roads connecting the city groups are the carbon emission intensity core; there was a significant negative correlation between road integration and road carbon emission intensity. However, there was a significant positive correlation between road accessibility and road transport carbon emission intensity. Enhancing the local integration and dispersing the demand pressure on the trunk roads are effective ways to reduce traffic carbon emissions in the future.
This is a preview of subscription content, access via your institution.




REFERENCES
Bin Xu and Boqiang Lin, Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model, J. Cleaner Production, 2018, vol. 175, pp. 109–122.
Solaymani, S., CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector, Energy, 2019, vol. 168, pp. 989–1001.
Dou, S., Yao, Y., Zheng, H., et al., Delineating China’s urban traffic layout by integrating complex graph theory and road network data., J. Geo-information Sci., 2021, vol. 23, pp. 812–824.
Zhao, P., Lv, D., Hu, H., et al., Population-development oriented comprehensive modern transport system in China, Acta Geographica Sinica, 2020, vol. 75, pp. 2699–2715.
Shao, H. and Wang, Z., Spatial network structure of transportation carbon emissions efficiency in China and its influencing factors, China Population Res. Envir., 2021, vol. 31, pp.32–41.
Timilsina, G.R. and Shrestha, A., Transport sector CO2 emissions growth in Asia: Underlying factors and policy options, Energy Policy, 2009, vol. 37, pp. 4523–4539.
Marrero, Á.S., Marrero, G.A., González, R.M., et al., Convergence in road transport CO2 emissions in Europe, Energy Econ., 2021, vol. 99, p. 105322.
Awaworyi Churchill, S., Inekwe, J., Ivanovski, K., et al., Transport infrastructure and CO2 emissions in the OECD over the long run, Transportation Res. Pt. D: Transport Envir., 2021, vol. 95, p. 102857.
Chen, S., Zhang, S., and Yuan, C., China’s Transportation economy development environmental efficient evaluation, China J. Highway and Transport, 2019, vol. 32, pp. 154–161.
Yuan, C., Zhang, S., Jiao, P., et al., Temporal and spatial variation and influencing factors research on total efficiency for transportation carbon emission in China, Res. Sci., 2017, vol. 39, pp. 687–697.
Abe, R., Kato, H., and Hayashi, Y., What led to the establishment of a rail-oriented city? Determinants of urban rail supply in Tokyo, Japan, 1950–2010, Transport Policy, 2017, vol. 58 (C), pp. 72–79.
Tan, X., Tu, T., Gu, B., et al., Scenario simulation of CO2 emissions from light-duty passenger vehicles under land use-transport planning: A case of Shenzhen International Low Carbon City, Sustainable Cities and Society, 2021, vol. 75, p. 103266.
Song, Y., Zhang, M., and Shan, C., Research on the decoupling trend and mitigation potential of CO2 emissions from China’s transport sector, Energy, 2019, vol. 183, pp. 837–843.
Hillier, B., Spatial sustainability in cities: organic patterns and sustainable forms. royal institute of technology, Proceedings of the 7th International Space Syntax Symposium, Stockholm: Royal Institute of Technology, 2009.
Hillier, B., Research solves real problems, Engineering, 1987, vol. 227, pp. 274–274.
Lu, Y., Fu, X., Lu, C., et al., Effects of route familiarity on drivers’ psychological conditions: Based on driving behaviour and driving environment, Transport. Res. Pt. F: Traffic Psychology and Behaviour, 2020, vol. 75, pp. 37–54.
Osorio, C. and Nanduri, K., Urban transportation emissions mitigation: Coupling high-resolution vehicular emissions and traffic models for traffic signal optimization, Transport. Res. Pt. B, 2015, vol. 81, pp. 520–538.
Al-Wreikat, Ya., Serrano, C., and Ricardo Sodré, J., Driving behavior and trip condition effects on the energy consumption of an electric vehicle under real-world driving, Appl. Energy, 2021, vol. 297, p. 117096.
Xie, S., Song, X., and Shen, X., Calculating vehicular emission factors with COPERTIII Mode in China, Envir. Sci., 2006, pp. 3415–3419.
Hong, I. and Jung, W-S., Application of gravity model on the Korean urban bus network, Physica A, 2016, vol. 462, pp. 48–55.
Funding
Class A strategic leading science and technology project of Chinese Academy of Sciences (Grant number XDA20030203), National Natural Science Foundation of China (Grant no. 42071282).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no conflicts of interest.
Rights and permissions
About this article
Cite this article
Guo, K., Li, F. & Cheng, H. Interaction of Urban Traffic Network Structure and Carbon Emission Intensity: A Case Study in Shenzhen. Geogr. Nat. Resour. 43 (Suppl 1), S97–S102 (2022). https://doi.org/10.1134/S1875372822050109
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1875372822050109