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How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis

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  • Zha, Jianping
  • Tan, Ting
  • Fan, Rong
  • Xu, Han
  • Ma, Siqi

Abstract

China's transport sector has been attracting great attention for its excessive energy consumption and ever-increasing pollution emissions. Thus, reducing energy intensity is one of the top priorities of China's ongoing transport upgrade. In this paper, by establishing a panel data regression model derived from the Cobb–Douglas cost function, we focus on investigating the impacts of energy price and transport productivity on transport energy intensity at the national and regional levels. The study uses the provincial panel data for 2005–2016 to perform regression analysis. The results show that: (1) energy price has a significantly negative effect on transport energy intensity in the whole China and the eastern region, whereas it has no significant impacts in the central and western China. (2) Improvements in transport productivity can effectively decrease transport energy intensity in the whole China and the three major regions. (3) Applying an extended data envelopment analysis (DEA) approach, we decompose transport productivity into four components (i.e., technical change, technology gap change, scale efficiency change, and pure efficiency change) and further differentiate their impacts in different regions. The results indicate that these four components have substantially different impacts in each region. These results provide some valuable insights for policymakers and enterprise entities aiming to adopt measures to reduce energy intensity and achieve sustainable development in China's transport sector.

Suggested Citation

  • Zha, Jianping & Tan, Ting & Fan, Rong & Xu, Han & Ma, Siqi, 2020. "How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
  • Handle: RePEc:eee:soceps:v:71:y:2020:i:c:s0038012119301958
    DOI: 10.1016/j.seps.2019.100772
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    as
    1. Fisher-Vanden, Karen & Jefferson, Gary H. & Liu, Hongmei & Tao, Quan, 2004. "What is driving China's decline in energy intensity?," Resource and Energy Economics, Elsevier, vol. 26(1), pages 77-97, March.
    2. Han, Chirok & Orea, Luis & Schmidt, Peter, 2005. "Estimation of a panel data model with parametric temporal variation in individual effects," Journal of Econometrics, Elsevier, vol. 126(2), pages 241-267, June.
    3. Lin, Boqiang & Zhang, Guoliang, 2013. "Estimates of electricity saving potential in Chinese nonferrous metals industry," Energy Policy, Elsevier, vol. 60(C), pages 558-568.
    4. Wu, Na & Zhao, Shengchuan & Zhang, Qi, 2016. "A study on the determinants of private car ownership in China: Findings from the panel data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 186-195.
    5. Birol, Fatih & Keppler, Jan Horst, 2000. "Prices, technology development and the rebound effect," Energy Policy, Elsevier, vol. 28(6-7), pages 457-469, June.
    6. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    7. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    8. Okajima, Shigeharu & Okajima, Hiroko, 2013. "Analysis of energy intensity in Japan," Energy Policy, Elsevier, vol. 61(C), pages 574-586.
    9. Filipović, Sanja & Verbič, Miroslav & Radovanović, Mirjana, 2015. "Determinants of energy intensity in the European Union: A panel data analysis," Energy, Elsevier, vol. 92(P3), pages 547-555.
    10. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.
    11. Arens, Marlene & Worrell, Ernst & Schleich, Joachim, 2012. "Energy intensity development of the German iron and steel industry between 1991 and 2007," Energy, Elsevier, vol. 45(1), pages 786-797.
    12. Cao, Jing & Karplus, Valerie J., 2014. "Firm-level determinants of energy and carbon intensity in China," Energy Policy, Elsevier, vol. 75(C), pages 167-178.
    13. Wanke, Peter & Barros, C.P. & Figueiredo, Otávio, 2016. "Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach," Utilities Policy, Elsevier, vol. 41(C), pages 31-39.
    14. Chang, Young-Tae & Zhang, Ning & Danao, Denise & Zhang, Nan, 2013. "Environmental efficiency analysis of transportation system in China: A non-radial DEA approach," Energy Policy, Elsevier, vol. 58(C), pages 277-283.
    15. Bogetoft, Peter & Leth Hougaard, Jens, 2004. "Super efficiency evaluations based on potential slack," European Journal of Operational Research, Elsevier, vol. 152(1), pages 14-21, January.
    16. Jeremy Atack & Fred Bateman & Michael Haines & Robert A. Margo, 2009. "Did Railroads Induce or Follow Economic Growth? Urbanization and Population Growth in the American Midwest, 1850-60," NBER Working Papers 14640, National Bureau of Economic Research, Inc.
    17. Song, Feng & Zheng, Xinye, 2012. "What drives the change in China's energy intensity: Combining decomposition analysis and econometric analysis at the provincial level," Energy Policy, Elsevier, vol. 51(C), pages 445-453.
    18. Wang, Qunwei & Zhao, Zengyao & Zhou, Peng & Zhou, Dequn, 2013. "Energy efficiency and production technology heterogeneity in China: A meta-frontier DEA approach," Economic Modelling, Elsevier, vol. 35(C), pages 283-289.
    19. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    20. Chung, William & Zhou, Guanghui & Yeung, Iris M.H., 2013. "A study of energy efficiency of transport sector in China from 2003 to 2009," Applied Energy, Elsevier, vol. 112(C), pages 1066-1077.
    21. He, Yongxiu & Wang, Bing & Wang, Jianhui & Xiong, Wei & Xia, Tian, 2013. "Correlation between Chinese and international energy prices based on a HP filter and time difference analysis," Energy Policy, Elsevier, vol. 62(C), pages 898-909.
    22. Mulder, Peter & de Groot, Henri L.F. & Pfeiffer, Birte, 2014. "Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005," Ecological Economics, Elsevier, vol. 100(C), pages 1-15.
    23. Lining He & Faye Duchin, 2009. "Regional Development In China: Interregional Transportation Infrastructure And Regional Comparative Advantage," Economic Systems Research, Taylor & Francis Journals, vol. 21(1), pages 3-22.
    24. Shahiduzzaman, Md. & Alam, Khorshed, 2013. "Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach," Energy Policy, Elsevier, vol. 56(C), pages 341-351.
    25. Akihiro Otsuka & Mika Goto, 2018. "Regional determinants of energy intensity in Japan: the impact of population density," Asia-Pacific Journal of Regional Science, Springer, vol. 2(2), pages 257-278, August.
    26. Sun, Qi & Xu, Lin & Yin, Hua, 2016. "Energy pricing reform and energy efficiency in China: Evidence from the automobile market," Resource and Energy Economics, Elsevier, vol. 44(C), pages 39-51.
    27. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    28. Wu, Yanrui, 2012. "Energy intensity and its determinants in China's regional economies," Energy Policy, Elsevier, vol. 41(C), pages 703-711.
    29. Andrés, Lidia & Padilla, Emilio, 2015. "Energy intensity in road freight transport of heavy goods vehicles in Spain," Energy Policy, Elsevier, vol. 85(C), pages 309-321.
    30. Su, Bin & Ang, B.W., 2017. "Multiplicative structural decomposition analysis of aggregate embodied energy and emission intensities," Energy Economics, Elsevier, vol. 65(C), pages 137-147.
    31. Du, Kerui & Lu, Huang & Yu, Kun, 2014. "Sources of the potential CO2 emission reduction in China: A nonparametric metafrontier approach," Applied Energy, Elsevier, vol. 115(C), pages 491-501.
    32. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    33. Lin, Boqiang & Du, Zhili, 2015. "How China׳s urbanization impacts transport energy consumption in the face of income disparity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1693-1701.
    34. Lin, Boqiang & Xie, Chunping, 2013. "Estimation on oil demand and oil saving potential of China's road transport sector," Energy Policy, Elsevier, vol. 61(C), pages 472-482.
    35. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    36. Douglas, Paul H, 1976. "The Cobb-Douglas Production Function Once Again: Its History, Its Testing, and Some New Empirical Values," Journal of Political Economy, University of Chicago Press, vol. 84(5), pages 903-915, October.
    37. Xie, Chunping & Bai, Mengqi & Wang, Xiaolei, 2018. "Accessing provincial energy efficiencies in China’s transport sector," Energy Policy, Elsevier, vol. 123(C), pages 525-532.
    38. Hang, Leiming & Tu, Meizeng, 2007. "The impacts of energy prices on energy intensity: Evidence from China," Energy Policy, Elsevier, vol. 35(5), pages 2978-2988, May.
    39. Karimu, Amin & Brännlund, Runar & Lundgren, Tommy & Söderholm, Patrik, 2017. "Energy intensity and convergence in Swedish industry: A combined econometric and decomposition analysis," Energy Economics, Elsevier, vol. 62(C), pages 347-356.
    40. Li, Ke & Lin, Boqiang, 2015. "How does administrative pricing affect energy consumption and CO2 emissions in China?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 952-962.
    41. Hui Hu & Xiang Li & Fuxia Yang & Jesmin Islam, 2016. "Total Factor Productivity and Energy Intensity: An Empirical Study of China’s Cement Industry," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(6), pages 1405-1413, June.
    42. Cornillie, Jan & Fankhauser, Samuel, 2004. "The energy intensity of transition countries," Energy Economics, Elsevier, vol. 26(3), pages 283-295, May.
    43. repec:dau:papers:123456789/10972 is not listed on IDEAS
    44. Rui Xie & Linyuan Huang & Boshi Tian & Jiayu Fang, 2019. "Differences in Changes in Carbon Dioxide Emissions among China’s Transportation Subsectors: A Structural Decomposition Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(6), pages 1294-1311, May.
    45. Li, Yi & Sun, Linyan & Feng, Taiwen & Zhu, Chunyan, 2013. "How to reduce energy intensity in China: A regional comparison perspective," Energy Policy, Elsevier, vol. 61(C), pages 513-522.
    46. Guo, Bin & Geng, Yong & Franke, Bernd & Hao, Han & Liu, Yaxuan & Chiu, Anthony, 2014. "Uncovering China’s transport CO2 emission patterns at the regional level," Energy Policy, Elsevier, vol. 74(C), pages 134-146.
    47. Lin, Boqiang & Moubarak, Mohamed, 2014. "Estimation of energy saving potential in China's paper industry," Energy, Elsevier, vol. 65(C), pages 182-189.
    48. Lin, Boqiang & Liu, Xia, 2013. "Reform of refined oil product pricing mechanism and energy rebound effect for passenger transportation in China," Energy Policy, Elsevier, vol. 57(C), pages 329-337.
    49. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    50. Wang, Feng & Wei, Xianjin & Liu, Juan & He, Lingyun & Gao, Mengnan, 2019. "Impact of high-speed rail on population mobility and urbanisation: A case study on Yangtze River Delta urban agglomeration, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 99-114.
    51. Dowlatabadi, Hadi & Oravetz, Matthew A., 2006. "US long-term energy intensity: Backcast and projection," Energy Policy, Elsevier, vol. 34(17), pages 3245-3256, November.
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