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Measuring Pollution Control and Environmental Sustainable Development in China Based on Parallel DEA Method

Author

Listed:
  • Ying Feng

    (Business College, Northwest University of Political Science and Law, No. 558 West Chang’an Road, Chang’an District, Xi’an 710122, China)

  • Chih-Yu Yang

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

  • Ching-Cheng Lu

    (Department of Business, National Open University, No. 172, Zhongzheng Road, Luzhou District, New Taipei City 247, Taiwan)

  • Pao-Yu Tang

    (Department of Economics, Soochow University, 56, Kueiyang St., Sec. 1, Taipei 100, Taiwan)

Abstract

The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO 2 and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.

Suggested Citation

  • Ying Feng & Chih-Yu Yang & Ching-Cheng Lu & Pao-Yu Tang, 2022. "Measuring Pollution Control and Environmental Sustainable Development in China Based on Parallel DEA Method," Energies, MDPI, vol. 15(15), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5697-:d:881349
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    References listed on IDEAS

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    1. S C Ray, 2008. "The directional distance function and measurement of super-efficiency: an application to airlines data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 788-797, June.
    2. 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.
    3. 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.
    4. 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.
    5. Zaim, Osman, 2004. "Measuring environmental performance of state manufacturing through changes in pollution intensities: a DEA framework," Ecological Economics, Elsevier, vol. 48(1), pages 37-47, January.
    6. Valadkhani, Abbas & Roshdi, Israfil & Smyth, Russell, 2016. "A multiplicative environmental DEA approach to measure efficiency changes in the world's major polluters," Energy Economics, Elsevier, vol. 54(C), pages 363-375.
    7. Chen, Shuai & Oliva, Paulina & Zhang, Peng, 2022. "The effect of air pollution on migration: Evidence from China," Journal of Development Economics, Elsevier, vol. 156(C).
    8. 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.
    9. 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.
    10. Färe, Rolf & Grosskopf, Shawna, 2010. "Directional distance functions and slacks-based measures of efficiency," European Journal of Operational Research, Elsevier, vol. 200(1), pages 320-322, January.
    11. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    12. 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.
    13. Hui Hu & Qian Jin & Philip Kavan, 2014. "A Study of Heavy Metal Pollution in China: Current Status, Pollution-Control Policies and Countermeasures," Sustainability, MDPI, vol. 6(9), pages 1-19, September.
    14. Kynčlová, Petra & Upadhyaya, Shyam & Nice, Thomas, 2020. "Composite index as a measure on achieving Sustainable Development Goal 9 (SDG-9) industry-related targets: The SDG-9 index," Applied Energy, Elsevier, vol. 265(C).
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    Cited by:

    1. Talat S. Genc & Stephen Kosempel, 2023. "Energy Transition and the Economy: A Review Article," Energies, MDPI, vol. 16(7), pages 1-26, March.

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