IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i8p2294-d223456.html
   My bibliography  Save this article

Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach

Author

Listed:
  • Li Xie

    (School of Economics and Trade, Hunan University, Changsha 410079, China)

  • Chunlin Chen

    (School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Yihua Yu

    (School of Applied Economics, Renmin University of China, Beijing 100872, China)

Abstract

While China’s rapid industrialization has brought great pressure on environmental pollution, great variations appear in terms of environmental pollution levels among industries. The effective assessment of the environmental performance of different industries is not only conducive to identifying the major sources of pollution in China but also of great significance to the Chinese government in formulating differentiated industry environmental control policies in a targeted manner. Using data of 36 Chinese industries from 2006 to 2015 and a multiple data envelopment analysis (DEA) with a Gini criterion as well as a systematic clustering approach, this study first calculates the environmental efficiency score of Chinese industries and then identifies those pollution sources based on a ranking and clustering analysis. The main result indicates that the ranking of environmental efficiency of various industries overall varies greatly by time. In addition, using a clustering analysis, this study finds that 13 labor-intensive light industries and heavy chemical industries with high energy use and high emissions are medium- and high-pollution industries. Important policy implications are drawn to achieve green industrial development.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2294-:d:223456
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/8/2294/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/8/2294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Han, Lei & Han, Botang & Shi, Xunpeng & Su, Bin & Lv, Xin & Lei, Xiao, 2018. "Energy efficiency convergence across countries in the context of China’s Belt and Road initiative," Applied Energy, Elsevier, vol. 213(C), pages 112-122.
    4. Xie, Bai-Chen & Shang, Li-Feng & Yang, Si-Bo & Yi, Bo-Wen, 2014. "Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countrie," Energy, Elsevier, vol. 74(C), pages 147-157.
    5. Yanni Yu & Weijie Zhang & Ning Zhang, 2018. "The Potential Gains from Carbon Emissions Trading in China’s Industrial Sectors," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1175-1194, 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. Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), 2016. "Advances in Efficiency and Productivity," International Series in Operations Research and Management Science, Springer, number 978-3-319-48461-7, September.
    8. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
    9. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    10. Song, Malin & Wang, Jianlin, 2018. "Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model," Energy, Elsevier, vol. 161(C), pages 325-336.
    11. M.A. Cole & A.J. Rayner & J.M. Bates, 1998. "Trade Liberalisation and the Environment: The Case of the Uruguay Round," The World Economy, Wiley Blackwell, vol. 21(3), pages 337-347, May.
    12. Liao, Nuo & He, Yong, 2018. "Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model," Energy, Elsevier, vol. 158(C), pages 782-795.
    13. Geng, ZhiQiang & Dong, JunGen & Han, YongMing & Zhu, QunXiong, 2017. "Energy and environment efficiency analysis based on an improved environment DEA cross-model: Case study of complex chemical processes," Applied Energy, Elsevier, vol. 205(C), pages 465-476.
    14. Liao, Xianchun & Shi, Xunpeng (Roc), 2018. "Public appeal, environmental regulation and green investment: Evidence from China," Energy Policy, Elsevier, vol. 119(C), pages 554-562.
    15. Lei Jiang & Henk Folmer & Minhe Ji & Jianjun Tang, 2017. "Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(2), pages 301-319, March.
    16. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    17. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    18. 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.
    19. Luis Orea & Alan Wall, 2016. "Measuring Eco-efficiency Using the Stochastic Frontier Analysis Approach," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 275-297, Springer.
    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. Jun Xu & Yuchen Jiang & Xin Guo & Li Jiang, 2021. "Environmental Efficiency Assessment of Heavy Pollution Industry by Data Envelopment Analysis and Malmquist Index Analysis: Empirical Evidence from China," IJERPH, MDPI, vol. 18(11), pages 1-17, May.
    2. Xue Wan & Xiaoning Yang & Quaner Wen & Jun Gang & Lu Gan, 2020. "Sustainable Development of Industry–Environmental System Based on Resilience Perspective," IJERPH, MDPI, vol. 17(2), pages 1-23, January.
    3. Luning Shao & Jianxin You & Tao Xu & Yilei Shao, 2020. "Non-Parametric Model for Evaluating the Performance of Chinese Commercial Banks’ Product Innovation," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    4. Liangen Zeng & Haiyan Lu & Yenping Liu & Yang Zhou & Haoyu Hu, 2019. "Analysis of Regional Differences and Influencing Factors on China’s Carbon Emission Efficiency in 2005–2015," Energies, MDPI, vol. 12(16), pages 1-21, August.
    5. Maria Molinos-Senante & Alexandros Maziotis, 2021. "The Cost of Reducing Municipal Unsorted Solid Waste: Evidence from Municipalities in Chile," Sustainability, MDPI, vol. 13(12), pages 1-14, June.
    6. Yang Li & Kunlin Zhu & Xianghui Li & Zunirah Mohd Talib & Brian Teo Sheng Xian, 2023. "Dynamic Pattern and Evolution Trend of the New Four Modernizations Synchronous Development in China: An Analysis Based on Panel Data from 31 Provinces," Sustainability, MDPI, vol. 15(8), pages 1-26, April.
    7. Tan Xiuli & Chen Zhongquan & Sun Zhaorong & Chen Zhisong, 2021. "Analysis of Green Technology Upgrading Strategy Based on Collaborative Incentive of Environmental Policy and Green Finance," Journal of Systems Science and Information, De Gruyter, vol. 9(1), pages 61-73, February.
    8. Ramon Sala-Garrido & Manuel Mocholi-Arce & Maria Molinos-Senante & Michail Smyrnakis & Alexandros Maziotis, 2021. "Eco-Efficiency of the English and Welsh Water Companies: A Cross Performance Assessment," IJERPH, MDPI, vol. 18(6), pages 1-17, March.
    9. Tingting Yang & Xuefeng Guan & Yuehui Qian & Weiran Xing & Huayi Wu, 2019. "Efficiency Evaluation of Urban Road Transport and Land Use in Hunan Province of China Based on Hybrid Data Envelopment Analysis (DEA) Models," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    10. Manli Cheng & Zhen Shao & Changhui Yang & Xiaoan Tang, 2019. "Analysis of Coordinated Development of Energy and Environment in China’s Manufacturing Industry under Environmental Regulation: A Comparative Study of Sub-Industries," Sustainability, MDPI, vol. 11(22), pages 1-20, November.

    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. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    2. W. Liu & W. Meng & X. Li & D. Zhang, 2010. "DEA models with undesirable inputs and outputs," Annals of Operations Research, Springer, vol. 173(1), pages 177-194, January.
    3. Jie Wu & Zhixiang Zhou, 2015. "A mixed-objective integer DEA model," Annals of Operations Research, Springer, vol. 228(1), pages 81-95, May.
    4. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    5. Adel Hatami-Marbini & Aliasghar Arabmaldar & John Otu Asu, 2022. "Robust productivity growth and efficiency measurement with undesirable outputs: evidence from the oil industry," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(4), pages 1213-1254, December.
    6. Bretholt, Abraham & Pan, Jeh-Nan, 2013. "Evolving the latent variable model as an environmental DEA technology," Omega, Elsevier, vol. 41(2), pages 315-325.
    7. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    8. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    9. Suzuki, Soushi & Nijkamp, Peter, 2016. "An evaluation of energy-environment-economic efficiency for EU, APEC and ASEAN countries: Design of a Target-Oriented DFM model with fixed factors in Data Envelopment Analysis," Energy Policy, Elsevier, vol. 88(C), pages 100-112.
    10. César Salazar & Roberto Cárdenas-Retamal & Marcela Jaime, 2023. "Environmental efficiency in the salmon industry—an exploratory analysis around the 2007 ISA virus outbreak and subsequent regulations in Chile," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8107-8135, August.
    11. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    12. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 363-391, June.
    13. Yung-ho Chiu & Chin-wei Huang & Chung-te Ting, 2012. "A non-radial measure of different systems for Taiwanese tourist hotels’ efficiency assessment," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 45-63, March.
    14. Tran, Trung Hieu & Mao, Yong & Nathanail, Paul & Siebers, Peer-Olaf & Robinson, Darren, 2019. "Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis," Omega, Elsevier, vol. 85(C), pages 156-165.
    15. Zou, Bo & Kafle, Nabin & Chang, Young-Tae & Park, Kevin, 2015. "US airport financial reform and its implications for airport efficiency: An exploratory investigation," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 66-78.
    16. Chen, Chien-Ming, 2013. "Super efficiencies or super inefficiencies? Insights from a joint computation model for slacks-based measures in DEA," European Journal of Operational Research, Elsevier, vol. 226(2), pages 258-267.
    17. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    18. 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.
    19. Zhiqiang Zhu & Xuechi Zhang & Mengqing Xue & Yaoyao Song, 2023. "Eco-Efficiency and Its Evolutionary Change under Regulatory Constraints: A Case Study of Chinese Transportation Industry," Sustainability, MDPI, vol. 15(9), pages 1-18, April.
    20. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.

    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:8:p:2294-:d:223456. 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.