The transportation industry is the main CO2 emission industry in China. How to formulate and implement the policies of transportation energy saving and emission reduction policies while meeting the people's growing transportation needs has become an important issue to face Chinese society. The central region, as a transportation hub connecting north and south and connecting east and west, has developed rapidly with the support of policies in recent years, and the transportation sector has also developed accordingly. This paper firstly adopts the Super-SBM model that considers undesired output to calculate and evaluate the CO2 emission efficiency of transportation sector in 6 provinces in central region of China from 2005 to 2016; then, it considers the impact of per capita GDP, urbanization level, number of buses per 10,000, energy intensity and transportation intensity on the CO2 emission efficiency of transportation sector. Based on the analysis results, reasonable policy recommendations are provided for further improving the CO2 emission efficiency of transportation department and developing low-carbon transportation.

Xinyi Li. Examining the CO2 Emission Efficiency and its Influencing Factors for the Transport Sector in Central China. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 7: 169-178. https://doi.org/10.25236/AJETS.2020.030717.

[1] L. Chapman (2007). Transport and climate change: a review. Journal of Transport Geography, vol.15, no.5, p354-367.
[2] F. Creutzig and D. He (2009). Climate change mitigation and co-benefits of feasible transport demandpolicies in Beijing. Transportation Research Part D, vol.14, p.120-131.
[3] T.V. Ramachandra and Shwetmala (2009). Emissions from India's transport sector: Statewise synthesis. Atmospheric Environment, vol.43, no.34, p.5510-5517.
[4] I. Meyer, M. Leimbach and C.C. Jaeger (2007). International passenger transport and climate change: a sector analysis in car demand and associated CO2 emissions from 2000 to 2050. Energy Policy.
[5] M.O.D. Eicker, R. Zah and R. Triviño (2008). Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities. Atmospheric Environment, vol.42, no.7, p.1491-1502.
[6] M. Lesiv and R. Lesiv (2010), Geoinformation Technologies and Spatial Analysis of Carbon Dioxide Transport through Border Line. vol.4, p.322-329.
[7] Z. Peng, Q. Wu, D. Wang and M. Li (2019). Temporal-spatial pattern and influencing factors of china's province-level transport sector CO2 emissions efficiency. Polish Journal of Environmental Studies, 29(1),233-247.
[8] A. Das, and S.K. Paul (2014). CO2 emissions from household consumption in india between 1993–94 and 2006–07: a decomposition analysis. Energy Economics, vol.41, no.1, p.90-105.
[9] C. Zhang and J. Nian (2013). Panel estimation for transport sector CO2 emissions and its affecting factors: a regional analysis in china. Energy Policy, vol.63, no.12, p.918-926.
[10] Y. Kaya and K. Yokobori (1993). Global environment, energy, and economic development. Tokyo: United Nations University.
[11] B.W. Ang (1999). Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?. Energy Policy, vol.27.
[12] J.E. Aldy (2005). Per capita carbon dioxide emissions: convergence or divergence?. Ssrn Electronic Journal.
[13] Y. Fan, L.C. Liu, G. Wu, H.T. Tsai and Y.M. Wei (2007). Changes in carbon intensity in china: empirical findings from 1980-2003. Ecological Economics, vol.62, no.3-4, p.683-691.
[14] T. Jobert, F. Karanfil and A. Tykhonenko (2010). Convergence of per capita carbon dioxide emissions in the eu: legend or reality?. Energy Economics, vol.32, no.6, p.1364-1373.
[15] R. Ramanathan, (2005). An analysis of energy consumption and carbon dioxide emissions in countries of the Middle East and North Africa. Energy, vol.30, no.15, p.2831-2842.
[16] X. Yao, H. Zhou, A. Zhang and A. Li (2015). Regional energy efficiency, carbon emission performance and technology gaps in china: a meta-frontier non-radial directional distance function analysis. Energy Policy, vol.84, no.9, p.142-154.
[17] X.D. Guo, L. Zhu and Y. Fan (2011). Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA. Energy Policy, vol.39, no.5, p.2352-2360.
[18] K. Tone (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, vol.130, no.3, p.498-509.
[19] Y. Choi, N. Zhang and P. Zhou (2012). Efficiency and abatement costs of energy-related CO2 emissions in china: a slacks-based efficiency measure. Applied Energy, vol.98, p.198-208.
[20] R. Gomez-Calvet, D. Conesa, A. R. Gomez-Calvet and E. Tortosa-Ausina (2014). Energy efficiency in the european union: what can be learned from the joint application of directional distance functions and slacks-based measures?. Applied Energy, vol.132, no.11, p,137-154.
[21] C. Wang, J. Chen and J. Zou (2005). Decomposition of energy-related CO2 emission in china: 1957–2000. Energy,vol.30, no.1, p.73-83.
[22] A. Jalil and S.F. Mahmud (2009). Environment kuznets curve for CO2 emissions: a cointegration analysis for china. Energy Policy, vol.37, no.12, p.5167-5172.
[23] A. Charnes, W.W. Cooper and E. Rhodes (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, vol.2, no.6, p.429-444.
[24] R.D. Banker, A. Charnes and W.W. Cooper (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, vol.30, no.9, p.1078-1092.
[25] Y. Bian and F. Yang (2010). Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon’s entropy. Energy Policy, vol.38, no.4, p.1909.
[26] G.M. Shi, J. Bi and J.N. Wang (2010). Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy Policy, vol.38, no. 10, p.6172.
[27] W. Zhang, K. Li, D. Zhou, W. Zhang and H. Gao (2016). Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method. Energy Policy, vol.92, no.3, p.369-381.
[28] S. Lu, H. Jiang, Y. Liu and S. Huang (2017). Regional disparities and influencing factors of average CO2 emissions from transportation industry in yangtze river economic belt. Transportation Research Part D Transport and Environment, vol.57, no.12, p.112-123.