Advertisement

Analysis of the Low-Carbon Efficiency of Chinese Transport Sectors from 2007 to 2015

  • Fei Ma
  • Xiao-dan Li
  • Qi-peng Sun
  • Fei Liu
  • Wen-lin Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

To evaluate the carbon emission efficiency of transport sectors in China, the concept of low-carbon transport efficiency (LCTE) is proposed. On this basis, the LCTE in China’s provinces and autonomous regions is calculated using the evaluation method of cross-data envelopment analysis (DEA) with the data during the period of 2007–2015. The result shows that the overall level of China’s LCTE is mostly in the middle and lower level, meanwhile the difference of provincial and autonomous regions is great. This means there is a lot of room for China’s low-carbon transport efficiency to improve. The average value of LCTE is 0.54, which shows a steady trend of development from 2007 to 2013. While there are large fluctuations from 2013 to 2015 in many provinces and autonomous regions, of which Ningxia and Liaoning are the largest, whose change are the decrease of 40.7% and the increase of 79.1%, respectively. This indicates that current situation of transport carbon emission in China is not optimistic, and the main reason is that the western region is relatively backward.

Keywords

Low-carbon transport efficiency Transport sectors Cross-data envelopment analysis 

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (grant number 41301130), the Social Science Fund of Shaanxi Province (grant number 2016R026), the Social Science Fund of Xi’an City (grant number 17J176), and the Fundamental Research Funds for the Central Universities (grant numbers 310823170214, 0009-2014G6231003, 310823160101, 310823170109).

References

  1. 1.
    Dimoula V, Kehagia F, Tsakalidis A (2016) A holistic approach for estimating carbon emissions of road and rail transport systems. Aerosol Air Qual Res 16:61–68CrossRefGoogle Scholar
  2. 2.
    Waygood EOD, Avineri E (2016) Communicating transportation carbon dioxide emissions information: does gender impact behavioral response? Transp Res Part D 48:187–202CrossRefGoogle Scholar
  3. 3.
    Ferreira M, D’Orey PM (2012) On the impact of virtual traffic lights on carbon emissions mitigation. IEEE T Intelli Transp Syst 13(1):284–295CrossRefGoogle Scholar
  4. 4.
    Mascia M, Hu S, Han K et al (2016) Impact of traffic management on black carbon emissions: a microsimulation study. Netw Spat Econ 17(1):269–291MathSciNetCrossRefGoogle Scholar
  5. 5.
    Rezaee MJ, Izadbakhsh H, Yousefi S (2016) An improvement approach based on DEA-game theory for comparison of operational and spatial efficiencies in urban transportation systems. Transp Eng 20(4):1526–1531CrossRefGoogle Scholar
  6. 6.
    Zhao Y, Triantis K, Murray-Tuite P, Edara P (2011) Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach. Transp Res Part E 47(6):1140–1159CrossRefGoogle Scholar
  7. 7.
    Chang Y, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energ Policy 58(9):277–283CrossRefGoogle Scholar
  8. 8.
    Charnes A, Cooper WW, Cooper (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 30(9):1078–1092Google Scholar
  9. 9.
    Green RH, Doyle JR, Cook WD (1996) Preference voting and project ranking using DEA and cross-evaluation. Eur J Oper Res 90:461–472CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Fei Ma
    • 1
  • Xiao-dan Li
    • 1
  • Qi-peng Sun
    • 1
  • Fei Liu
    • 1
  • Wen-lin Wang
    • 1
  1. 1.School of Economics and ManagementChang’an UniversityXi’anChina

Personalised recommendations