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Regional energy efficiency and its determinants in China during 2001–2010: a slacks-based measure and spatial econometric analysis

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  • Kangjuan Lv

    (Shanghai University)

  • Anyu Yu

    (Tongji University)

  • Yiwen Bian

    () (Shanghai University)

Abstract

Abstract Carbon dioxide (CO2) emissions are largely driven by fossil fuels. To reduce CO2 emissions in China, it is important to determine influential factors of energy efficiency. This paper introduces a slacks-based measure window analysis approach to evaluate regional dynamic energy efficiency during 2001–2010, and then explores energy efficiency determinants by considering spatial effects, which is conducted based on spatial econometric models. The empirical results show that there exist evident spatial correlations between regional energy efficiencies in China. We find that, there exist evident disparities in cumulative effects of energy efficiency among the eastern, central and western areas. Interestingly, significant energy efficiency spatial spillovers can be clearly found between regions within the western area and across the eastern and western areas. It is found that, energy structure, energy price, railway transportation development and R&D stock are significant at national level. However, energy structure and railway transportation development are insignificant in the central and western areas, while energy price and R&D stock are insignificant in the eastern and central areas, respectively. Industrial structure and urbanization level are found to be insignificant at national level, but industrial structure is significant in the eastern and western areas, and urbanization level is significant in the central and western areas. Surprisingly, industrial structure and urbanization level are found to have positive impacts on energy efficiency in the western area. In addition to regional disparities and local conditions, policies making should take efficiency spatial spillovers into consideration. Several interesting policy implications are achieved.

Suggested Citation

  • Kangjuan Lv & Anyu Yu & Yiwen Bian, 2017. "Regional energy efficiency and its determinants in China during 2001–2010: a slacks-based measure and spatial econometric analysis," Journal of Productivity Analysis, Springer, vol. 47(1), pages 65-81, February.
  • Handle: RePEc:kap:jproda:v:47:y:2017:i:1:d:10.1007_s11123-016-0490-2
    DOI: 10.1007/s11123-016-0490-2
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    References listed on IDEAS

    as
    1. Li, Yong & Oberheitmann, Andreas, 2009. "Challenges of rapid economic growth in China: Reconciling sustainable energy use, environmental stewardship and social development," Energy Policy, Elsevier, vol. 37(4), pages 1412-1422, April.
    2. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    3. Halkos, George Emm. & Tzeremes, Nickolaos G., 2009. "Exploring the existence of Kuznets curve in countries' environmental efficiency using DEA window analysis," Ecological Economics, Elsevier, vol. 68(7), pages 2168-2176, May.
    4. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    5. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    6. Zhou, P. & Ang, B.W., 2008. "Linear programming models for measuring economy-wide energy efficiency performance," Energy Policy, Elsevier, vol. 36(8), pages 2901-2906, August.
    7. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    8. Boyd, Gale A. & Pang, Joseph X., 2000. "Estimating the linkage between energy efficiency and productivity," Energy Policy, Elsevier, vol. 28(5), pages 289-296, May.
    9. Li, Huanan & Mu, Hailin & Zhang, Ming & Gui, Shusen, 2012. "Analysis of regional difference on impact factors of China’s energy – Related CO2 emissions," Energy, Elsevier, vol. 39(1), pages 319-326.
    10. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    11. Zhang, Chuanguo & Lin, Yan, 2012. "Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China," Energy Policy, Elsevier, vol. 49(C), pages 488-498.
    12. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    13. Blázquez Gomez, Leticia M. & Filippini, Massimo & Heimsch, Fabian, 2013. "Regional impact of changes in disposable income on Spanish electricity demand: A spatial econometric analysis," Energy Economics, Elsevier, vol. 40(S1), pages 58-66.
    14. Fare, Rolf & Grosskopf, Shawna, 2004. "Modeling undesirable factors in efficiency evaluation: Comment," European Journal of Operational Research, Elsevier, vol. 157(1), pages 242-245, August.
    15. Lee, Myunghun & Zhang, Ning, 2012. "Technical efficiency, shadow price of carbon dioxide emissions, and substitutability for energy in the Chinese manufacturing industries," Energy Economics, Elsevier, vol. 34(5), pages 1492-1497.
    16. Wang, Can & Chen, Jining & Zou, Ji, 2005. "Decomposition of energy-related CO2 emission in China: 1957–2000," Energy, Elsevier, vol. 30(1), pages 73-83.
    17. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Slacks-based efficiency measures for modeling environmental performance," Ecological Economics, Elsevier, vol. 60(1), pages 111-118, November.
    18. Bian, Yiwen & He, Ping & Xu, Hao, 2013. "Estimation of potential energy saving and carbon dioxide emission reduction in China based on an extended non-radial DEA approach," Energy Policy, Elsevier, vol. 63(C), pages 962-971.
    19. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    20. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    21. Chu Wei & Jinlan Ni & Manhong Shen, 2009. "Empirical Analysis of Provincial Energy Efficiency in China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 17(5), pages 88-103.
    22. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    23. Biresselioglu, Mehmet Efe & Yelkenci, Tezer, 2016. "Scrutinizing the causality relationships between prices, production and consumption of fossil fuels: A panel data approach," Energy, Elsevier, vol. 102(C), pages 44-53.
    24. Svetlana Ledyaeva, 2009. "Spatial Econometric Analysis of Foreign Direct Investment Determinants in Russian Regions," The World Economy, Wiley Blackwell, vol. 32(4), pages 643-666, April.
    25. Mette Asmild & Joseph Paradi & Vanita Aggarwall & Claire Schaffnit, 2004. "Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry," Journal of Productivity Analysis, Springer, vol. 21(1), pages 67-89, January.
    26. Burnett, J. Wesley & Bergstrom, John C. & Dorfman, Jeffrey H., 2013. "A spatial panel data approach to estimating U.S. state-level energy emissions," Energy Economics, Elsevier, vol. 40(C), pages 396-404.
    27. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    28. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    29. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    30. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    31. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    32. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2012. "A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?," Energy Policy, Elsevier, vol. 46(C), pages 574-584.
    33. Yu, Huayi, 2012. "The influential factors of China's regional energy intensity and its spatial linkages: 1988–2007," Energy Policy, Elsevier, vol. 45(C), pages 583-593.
    34. Yuxiang, Karl & Chen, Zhongchang, 2010. "Government expenditure and energy intensity in China," Energy Policy, Elsevier, vol. 38(2), pages 691-694, February.
    35. Zhou, Xiaoyan & Zhang, Jie & Li, Junpeng, 2013. "Industrial structural transformation and carbon dioxide emissions in China," Energy Policy, Elsevier, vol. 57(C), pages 43-51.
    36. Liu, Yaobin & Xie, Yichun, 2013. "Asymmetric adjustment of the dynamic relationship between energy intensity and urbanization in China," Energy Economics, Elsevier, vol. 36(C), pages 43-54.
    37. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    38. Wei, Yi-Ming & Liao, Hua & Fan, Ying, 2007. "An empirical analysis of energy efficiency in China's iron and steel sector," Energy, Elsevier, vol. 32(12), pages 2262-2270.
    39. Voltes-Dorta, Augusto & Perdiguero, Jordi & Jiménez, Juan Luis, 2013. "Are car manufacturers on the way to reduce CO2 emissions?: A DEA approach," Energy Economics, Elsevier, vol. 38(C), pages 77-86.
    40. Sueyoshi, Toshiyuki & Aoki, Shingo, 2001. "A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test," Omega, Elsevier, vol. 29(1), pages 1-18, February.
    41. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, I," Energy, Elsevier, vol. 36(1), pages 685-693.
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    Cited by:

    1. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.

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