IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v78y2019icp468-480.html
   My bibliography  Save this article

Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs

Author

Listed:
  • Wu, Jie
  • Li, Mingjun
  • Zhu, Qingyuan
  • Zhou, Zhixiang
  • Liang, Liang

Abstract

Environmental problems brought by industry are attracting extensive attention so a comprehensive analysis of industrial environmental performance is increasingly important. However, the comparison of industrial sector efficiencies is complicated by the fact that the natural resources consumed and/or the pollutants discharged by each sector may differ. In this paper, we extend the DEA model to consider two-sided non-homogeneous problems, handling DMU sets that have non-homogeneity in both inputs and outputs. This is different from the previous researches which generally focus on regional data to avoid non-homogeneity. Today environmental reform and energy conservation in various industrial sectors are both parts of the basic state policy of China. The empirical results show that: (1) Sectors' efficiencies are still low and unbalanced. The Recycling and Disposal of Waste department achieves the best energy saving and emission reduction efficiency. (2) 38 sectors can be clustered into four groups and set new benchmark in each group. (3) The overall efficiency of 38 industrial sectors in China maintained a rising trend in five years. With this more realistic analysis of environmental efficiency, the Chinese government can make more informed decisions to realize sustainable industrial development.

Suggested Citation

  • Wu, Jie & Li, Mingjun & Zhu, Qingyuan & Zhou, Zhixiang & Liang, Liang, 2019. "Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs," Energy Economics, Elsevier, vol. 78(C), pages 468-480.
    • Handle: RePEc:eee:eneeco:v:78:y:2019:i:c:p:468-480
    DOI: 10.1016/j.eneco.2018.11.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988318304821
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2018.11.036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    2. Gongbing Bi & Yan Luo & Jingjing Ding & Liang Liang, 2015. "Environmental performance analysis of Chinese industry from a slacks-based perspective," Annals of Operations Research, Springer, vol. 228(1), pages 65-80, May.
    3. Yang, Dennis Tao, 2002. "What has caused regional inequality in China?," China Economic Review, Elsevier, vol. 13(4), pages 331-334, December.
    4. Ouyang, Xiaoling & Sun, Chuanwang, 2015. "Energy savings potential in China's industrial sector: From the perspectives of factor price distortion and allocative inefficiency," Energy Economics, Elsevier, vol. 48(C), pages 117-126.
    5. 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.
    6. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    7. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    8. Zhu, Joe, 2003. "Imprecise data envelopment analysis (IDEA): A review and improvement with an application," European Journal of Operational Research, Elsevier, vol. 144(3), pages 513-529, February.
    9. Liang, Liang & Cook, Wade D. & Zhu, Joe, 2016. "DEA models for non-homogeneous DMUs with different input configurationsAuthor-Name: Li, WangHong," European Journal of Operational Research, Elsevier, vol. 254(3), pages 946-956.
    10. Johnes, Jill, 2006. "Data envelopment analysis and its application to the measurement of efficiency in higher education," Economics of Education Review, Elsevier, vol. 25(3), pages 273-288, June.
    11. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    12. Cook, Wade D. & Harrison, Julie & Rouse, Paul & Zhu, Joe, 2012. "Relative efficiency measurement: The problem of a missing output in a subset of decision making units," European Journal of Operational Research, Elsevier, vol. 220(1), pages 79-84.
    13. Zhou, Nan & Levine, Mark D. & Price, Lynn, 2010. "Overview of current energy-efficiency policies in China," Energy Policy, Elsevier, vol. 38(11), pages 6439-6452, November.
    14. Du, Juan & Chen, Yao & Huo, Jiazhen, 2015. "DEA for non-homogenous parallel networks," Omega, Elsevier, vol. 56(C), pages 122-132.
    15. Raha Imanirad & Wade D. Cook & Joe Zhu, 2013. "Partial input to output impacts in DEA: Production considerations and resource sharing among business subunits," Naval Research Logistics (NRL), John Wiley & Sons, vol. 60(3), pages 190-207, April.
    16. 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.
    17. Barros, Carlos Pestana & Dieke, Peter U.C., 2008. "Measuring the economic efficiency of airports: A Simar-Wilson methodology analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 1039-1051, November.
    18. Wang, Qiang, 2010. "Effective policies for renewable energy--the example of China's wind power--lessons for China's photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 702-712, February.
    19. Wade D. Cook & Julie Harrison & Raha Imanirad & Paul Rouse & Joe Zhu, 2013. "Data Envelopment Analysis with Nonhomogeneous DMUs," Operations Research, INFORMS, vol. 61(3), pages 666-676, June.
    20. 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.
    21. Johnes, Jill, 2006. "Measuring teaching efficiency in higher education: An application of data envelopment analysis to economics graduates from UK Universities 1993," European Journal of Operational Research, Elsevier, vol. 174(1), pages 443-456, October.
    22. Daniel Tyteca, 1997. "Linear Programming Models for the Measurement of Environmental Performance of Firms—Concepts and Empirical Results," Journal of Productivity Analysis, Springer, vol. 8(2), pages 183-197, May.
    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. Yu-Chuan Chen & Yung-Ho Chiu & Tzu-Han Chang & Tai-Yu Lin, 2023. "Sustainable Development, Government Efficiency, and People’s Happiness," Journal of Happiness Studies, Springer, vol. 24(4), pages 1549-1578, April.
    2. Fan, Qiufang & Liu, Jintao & Zhang, Tao & Liu, Haomin, 2022. "An Evaluation of the Efficiency of China’s green investment in the “Belt and Road” countries," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 496-511.
    3. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    4. Wang, Zhaohua & Liu, Qiang & Zhang, Bin, 2022. "What kinds of building energy-saving retrofit projects should be preferred? Efficiency evaluation with three-stage data envelopment analysis (DEA)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. zhang, Ling & Niu, Guangli, 2023. "Role of financial performance and natural resources development on economic recovery: Empirical evidence from an Asian perspective," Resources Policy, Elsevier, vol. 85(PA).
    6. An, Qingxian & Tao, Xiangyang & Chen, Xiaohong, 2023. "Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework," European Journal of Operational Research, Elsevier, vol. 306(1), pages 269-285.
    7. Fenfen Li & Bo Dai & Qifan Wu, 2021. "Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    8. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    9. Guo, Wen & Liu, Xiaorui, 2022. "Market fragmentation of energy resource prices and green total factor energy efficiency in China," Resources Policy, Elsevier, vol. 76(C).
    10. Tao Xu & Jianxin You & Hui Li & Luning Shao, 2020. "Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review," Energies, MDPI, vol. 13(14), pages 1-20, July.
    11. Jiang, Lei & Zhou, Haifeng & He, Shixiong, 2021. "Does energy efficiency increase at the expense of output performance: Evidence from manufacturing firms in Jiangsu province, China," Energy, Elsevier, vol. 220(C).
    12. Linan Gao & Xiaofei Liu & Xinyi Mei & Guangwei Rui & Jingcheng Li, 2022. "Research on the Spatial-Temporal Distribution Characteristics and Influencing Factors of Carbon Emission Efficiency in China’s Metal Smelting Industry—Based on the Three-Stage DEA Method," Sustainability, MDPI, vol. 14(24), pages 1-19, December.
    13. Hao, Xiaoli & Wen, Shufang & Xue, Yan & Wu, Haitao & Hao, Yu, 2023. "How to improve environment, resources and economic efficiency in the digital era?," Resources Policy, Elsevier, vol. 80(C).
    14. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    15. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).
    16. Liu, Zhao & Zhang, Huan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2020. "How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China," Applied Energy, Elsevier, vol. 272(C).
    17. Yongzhong Jiang & Xueli Chen & Vivian Valdmanis & Tomas Baležentis, 2019. "Evaluating Economic and Environmental Performance of the Chinese Industry Sector," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    18. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    19. Li Xie & Chunlin Chen & Yihua Yu, 2019. "Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach," Sustainability, MDPI, vol. 11(8), pages 1-22, April.
    20. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    21. Cui, Qiang, 2021. "A data-based comparison of the five undesirable output disposability approaches in airline environmental efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).

    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. Josef Jablonský, 2019. "Data Envelopment Analysis Models in Non-Homogeneous Environment," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 67(6), pages 1535-1540.
    2. Sanjeet Singh & Prabhat Ranjan, 2018. "Efficiency analysis of non-homogeneous parallel sub-unit systems for the performance measurement of higher education," Annals of Operations Research, Springer, vol. 269(1), pages 641-666, October.
    3. Xiang Ji & Jie Wu & Qingyuan Zhu & Jiasen Sun, 2019. "Using a hybrid heterogeneous DEA method to benchmark China’s sustainable urbanization: an empirical study," Annals of Operations Research, Springer, vol. 278(1), pages 281-335, July.
    4. Wei, Chu & Löschel, Andreas & Liu, Bing, 2015. "Energy-saving and emission-abatement potential of Chinese coal-fired power enterprise: A non-parametric analysis," Energy Economics, Elsevier, vol. 49(C), pages 33-43.
    5. Cao, Ting & Cook, Wade D. & Kristal, M. Murat, 2022. "Has the technological investment been worth it? Assessing the aggregate efficiency of non-homogeneous bank holding companies in the digital age," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    6. Wu, Jie & Lv, Lin & Sun, Jiasen & Ji, Xiang, 2015. "A comprehensive analysis of China's regional energy saving and emission reduction efficiency: From production and treatment perspectives," Energy Policy, Elsevier, vol. 84(C), pages 166-176.
    7. Yang, Hongliang & Pollitt, Michael, 2010. "The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants," Energy Policy, Elsevier, vol. 38(8), pages 4440-4444, August.
    8. Zhu, Weiwei & Yu, Yu & Sun, Panpan, 2018. "Data envelopment analysis cross-like efficiency model for non-homogeneous decision-making units: The case of United States companies’ low-carbon investment to attain corporate sustainability," European Journal of Operational Research, Elsevier, vol. 269(1), pages 99-110.
    9. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    10. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    11. Corrado Lo Storto, 2016. "Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon’s Entropy Method," Sustainability, MDPI, vol. 8(2), pages 1-29, January.
    12. Ang, Sheng & Chen, Chien-Ming, 2016. "Pitfalls of decomposition weights in the additive multi-stage DEA model," Omega, Elsevier, vol. 58(C), pages 139-153.
    13. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    14. Zhou, Guanghui & Chung, William & Zhang, Xiliang, 2013. "A study of carbon dioxide emissions performance of China's transport sector," Energy, Elsevier, vol. 50(C), pages 302-314.
    15. Du, Huibin & Matisoff, Daniel C. & Wang, Yangyang & Liu, Xi, 2016. "Understanding drivers of energy efficiency changes in China," Applied Energy, Elsevier, vol. 184(C), pages 1196-1206.
    16. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Afzalinejad, Mohammad, 2020. "Reverse efficiency measures for environmental assessment in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    18. Tajbakhsh, Alireza & Hassini, Elkafi, 2018. "Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis," Energy Economics, Elsevier, vol. 74(C), pages 154-178.
    19. Yiwen Bian & Kangjuan Lv & Anyu Yu, 2017. "China’s regional energy and carbon dioxide emissions efficiency evaluation with the presence of recovery energy: an interval slacks-based measure approach," Annals of Operations Research, Springer, vol. 255(1), pages 301-321, August.
    20. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.

    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:eee:eneeco:v:78:y:2019:i:c:p:468-480. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.