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Measuring Environmental Efficiency through the Lens of Technology Heterogeneity: A Comparative Study between China and the G20

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  • Xiaoling Wang

    (School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China)

  • Manyin Zhang

    (School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
    School of Marxism Studies, University of Science and Technology Beijing, 100083, Beijing, China)

  • Jatin Nathwani

    (Waterloo Institute of Sustainable Energy, University ofWaterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada)

  • Fangming Yang

    (School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Drawing on a perspective of technology heterogeneity, this study advances the analytical framework for evaluation of environmental efficiency (EE) across diverse economies. To improve the continuity and robustness of efficiency estimation, we construct a Hybrid Malmquist–Luenberger index under the meta-frontier (MHML) technique to allow a dynamic evaluation of environmental efficiency and to probe the underlying sources of inefficiency. Decomposition of the MHML index into component factors of efficiency change (EC), Best Practice Change (BPC) and Technological Gap Change (TPC) allows an improved understanding of causality and enhanced guidance for decision-making units (DMUs). Empirical tests based on panel data of the Group 20 countries spanning 2000–2014 reveal an upward improving trend in environmental efficiency but is also characterized by notable evidence of technological heterogeneity. Whereas technical progress was the main cause of environmental efficiency improvements in the G20 countries, for the BRICS (i.e., Brazil, Russia, India, China, South Africa), economic growth rates played a more significant in contrast to the role of technical change and allocation efficiency. The lagging growth rates of environmental efficiency for the G20 countries compared to the BRICS is a reflection of the fact that room for optimization in G20 countries was not as high as it was for BRICS and, China, in particular. China has been catching up with frontier technology whereas developing countries were shifting away from benchmark technology frontier. The developed economies remain the best performers and leaders in environmental technology. However, the BRICS countries, represented by China, remain on an upward trajectory of improvements’ in EE with gains from managerial sufficiency and technological advancement. The MHML index developed here provides a robust quantitative measure for policy interventions to support overall national environmental performance. Context-specific suggestions are proposed to foster efficiency gains and green transition for Chinese development scenarios against best performing economies.

Suggested Citation

  • Xiaoling Wang & Manyin Zhang & Jatin Nathwani & Fangming Yang, 2019. "Measuring Environmental Efficiency through the Lens of Technology Heterogeneity: A Comparative Study between China and the G20," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:461-:d:198346
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    References listed on IDEAS

    as
    1. Easterly, William & Kremer, Michael & Pritchett, Lant & Summers, Lawrence H., 1993. "Good policy or good luck?: Country growth performance and temporary shocks," Journal of Monetary Economics, Elsevier, vol. 32(3), pages 459-483, December.
    2. Vlontzos, George & Niavis, Spyros & Manos, Basil, 2014. "A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 91-96.
    3. Ramli, Noor Asiah & Munisamy, Susila, 2015. "Eco-efficiency in greenhouse emissions among manufacturing industries: A range adjusted measure," Economic Modelling, Elsevier, vol. 47(C), pages 219-227.
    4. Kounetas, Konstantinos, 2015. "Heterogeneous technologies, strategic groups and environmental efficiency technology gaps for European countries," Energy Policy, Elsevier, vol. 83(C), pages 277-287.
    5. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    6. 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.
    7. Chiu, Ching-Ren & Liou, Je-Liang & Wu, Pei-Ing & Fang, Chen-Ling, 2012. "Decomposition of the environmental inefficiency of the meta-frontier with undesirable output," Energy Economics, Elsevier, vol. 34(5), pages 1392-1399.
    8. Xu Wang & Liyan Han & Libo Yin, 2016. "Environmental Efficiency and Its Determinants for Manufacturing in China," Sustainability, MDPI, vol. 9(1), pages 1-18, December.
    9. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    10. King, Robert G. & Levine, Ross, 1994. "Capital fundamentalism, economic development, and economic growth," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 40(1), pages 259-292, June.
    11. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    12. Song, Malin & An, Qingxian & Zhang, Wei & Wang, Zeya & Wu, Jie, 2012. "Environmental efficiency evaluation based on data envelopment analysis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4465-4469.
    13. Lee, Taehwee & Yeo, Gi-Tae & Thai, Vinh V., 2014. "Environmental efficiency analysis of port cities: Slacks-based measure data envelopment analysis approach," Transport Policy, Elsevier, vol. 33(C), pages 82-88.
    14. Ramanathan, Ramakrishnan & Ramanathan, Usha & Zhang, Yubo, 2016. "Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach," International Journal of Production Economics, Elsevier, vol. 176(C), pages 111-122.
    15. R G Dyson & E A Shale, 2010. "Data envelopment analysis, operational research and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 25-34, January.
    16. Lee, Jung Wan, 2013. "The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth," Energy Policy, Elsevier, vol. 55(C), pages 483-489.
    17. Chen, Nengcheng & Xu, Lei & Chen, Zeqiang, 2017. "Environmental efficiency analysis of the Yangtze River Economic Zone using super efficiency data envelopment analysis (SEDEA) and tobit models," Energy, Elsevier, vol. 134(C), pages 659-671.
    18. Fang Zhang & Hong Fang & Junjie Wu & Damian Ward, 2016. "Environmental Efficiency Analysis of Listed Cement Enterprises in China," Sustainability, MDPI, vol. 8(5), pages 1-19, May.
    19. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. Nikos Chatzistamoulou & Phoebe Koundouri, 2020. "Environmental Efficiency, Productive Performance and Spillover Effects under heterogeneous Environmental Awareness Regimes," DEOS Working Papers 2013, Athens University of Economics and Business.

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