IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i2p461-d198346.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/2/461/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/2/461/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Kounetas, Konstantinos, 2015. "Heterogeneous technologies, strategic groups and environmental efficiency technology gaps for European countries," Energy Policy, Elsevier, vol. 83(C), pages 277-287.
    7. 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.
    8. Dong-hyun Oh, 2010. "A global Malmquist-Luenberger productivity index," Journal of Productivity Analysis, Springer, vol. 34(3), pages 183-197, December.
    9. 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.
    10. 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.
    11. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Xiaohong & Yang, Jiangjiang & Xu, Chengzhen & Li, Xingchen & Zhu, Qingyuan, 2023. "Environmental regulation efficiency analysis by considering regional heterogeneity," Resources Policy, Elsevier, vol. 83(C).
    2. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    3. 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.
    4. 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.
    5. Zhong, Shen & Li, Junwei & Chen, Xi & Wen, Hongmei, 2022. "A multi-hierarchy meta-frontier approach for measuring green total factor productivity: An application of pig breeding in China," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    6. Yigang Wei & Yan Li & Meiyu Wu & Yingbo Li, 2020. "Progressing sustainable development of “the Belt and Road countries”: Estimating environmental efficiency based on the Super‐slack‐based measure model," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 521-539, July.
    7. 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.
    8. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    9. Xionghe Qin & Yanming Sun, 2019. "Cross-Regional Comparative Study on Environmental–Economic Efficiency and Driving Forces behind Efficiency Improvement in China: A Multistage Perspective," IJERPH, MDPI, vol. 16(7), pages 1-21, March.
    10. Qingxian An & Xiangyang Tao & Bo Dai & Jinlin Li, 2020. "Modified Distance Friction Minimization Model with Undesirable Output: An Application to the Environmental Efficiency of China’s Regional Industry," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1047-1071, April.
    11. Fei, Rilong & Lin, Boqiang, 2016. "Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 25-34.
    12. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    13. Yu, Shasha & Yuan, Xuanyu & Yao, Xinyan & Lei, Ming, 2022. "Carbon leakage and low-carbon performance: Heterogeneity of responsibility perspectives," Energy Policy, Elsevier, vol. 165(C).
    14. Wen-Chi Yang & Wen-Min Lu & Alagu Perumal Ramasamy, 2021. "International Environmental Efficiency Trends and the Impact of the Paris Agreement," Energies, MDPI, vol. 14(15), pages 1-16, July.
    15. Xiao-Ning Li & Ying Feng & Pei-Ying Wu & Yung-Ho Chiu, 2021. "An Analysis of Environmental Efficiency and Environmental Pollution Treatment Efficiency in China’s Industrial Sector," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    16. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    17. Stergiou, Eirini, 2022. "Environmental Efficiency of European Industries across Sectors and Countries," MPRA Paper 114635, University Library of Munich, Germany.
    18. Feng Li & Qingyuan Zhu & Jun Zhuang, 2018. "Analysis of fire protection efficiency in the United States: a two-stage DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 23-68, January.
    19. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    20. Zuoren Sun & Chao An & Huachen Sun, 2018. "Regional Differences in Energy and Environmental Performance: An Empirical Study of 283 Cities in China," Sustainability, MDPI, vol. 10(7), pages 1-28, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:461-:d:198346. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.