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China’s regional environmental efficiency evaluation: a dynamic analysis with biennial Malmquist productivity index based on common weights

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Abstract

China’s economy in the past 10 years has developed rapidly and achieved great success, but at the same time, environmental problems have been deteriorating, seriously hindering the country’s regional sustainable development. This paper proposes a common-weights DEA model based on “the priority of choosing common weights” to assess the environmental performance of 30 provinces from 2006 to 2015 and analyzes regional development in combination with China’s economic division. This paper also introduces the biennial Malmquist Productivity Index (BMPI) to study environmental productivity levels during the 11th and 12th Five Year Plans (FYP) from a time series perspective. The results present a large gap in regional environmental efficiency, mainly manifested by the fact that the eastern and northeastern regions’ environmental condition is obviously better than that of the central and western regions. BMPI analysis indicates that the overall environmental performances during the 11th and 12th FYPs did not improve significantly, with a clear imbalance in the western region, implying that its development potential is huge. On this basis, we offer some suggestions for improving the environmental performance of different regions.

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References

  • An Q, Meng F, Xiong B, Wang Z, Chen X (2018) Assessing the relative efficiency of Chinese high-tech industries: a dynamic network data envelopment analysis approach. Ann Oper Res (1):1–23

  • Caves DW, Christensen LR, Diewert WE (1982) The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica 50(6):1393–1414

    Article  Google Scholar 

  • Chang YT, Zhang N, Danao D, Zhang N (2013) Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy 58(9):277–283

    Article  Google Scholar 

  • Choi Y, Zhang N, Zhou P (2012) Efficiency and abatement costs of energy-related CO 2 emissions in China: a slacks-based efficiency measure. Appl Energy 98(5):198–208

    Article  Google Scholar 

  • Cook WD, Zhu J (2005) Allocation of shared costs among decision making units: a DEA approach. Comput Oper Res 32(8):2171–2178

    Article  Google Scholar 

  • Cook WD, Zhu J (2007) Within-group common weights in DEA: An analysis of power plant efficiency. Eur J Oper Res 178(1):207–216

    Article  Google Scholar 

  • Du J, Chen Y, Huang Y (2017) A Modified Malmquist-Luenberger Productivity Index: assessing environmental productivity performance in China. Eur J Oper Res:269

  • Du J, Cook WD, Liang L, Zhu J (2014) Fixed cost and resource allocation based on DEA cross-efficiency. Eur J Oper Res 235(1):206–214

    Article  Google Scholar 

  • Dyckhoff H, Allen K (2001) Measuring ecological efficiency with data envelopment analysis (DEA). Eur J Oper Res 132(2):312–325

    Article  Google Scholar 

  • Emrouznejad A, Yang GL (2016) A framework for measuring global Malmquist–Luenberger productivity index with CO 2 emissions on Chinese manufacturing industries. Energy 115:840–856

    Article  CAS  Google Scholar 

  • Fare R, Grosskopf S, Lovell CAK, Pasurka C (1989) Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. Rev Econ Stat 71(1):90–98

    Article  Google Scholar 

  • Fare R, Grosskopf S, Lovell CAK, Yaisawarng S (1993) Derivation of shadow prices for undesirable outputs: a distance function approach. Rev Econ Stat 75(2):374–380

    Article  Google Scholar 

  • Ganley JA, Cubbin JS (1992) Public sector efficiency measurement: applications of data envelopment analysis. J Oper Res Soc 34(2):174–175

    Google Scholar 

  • Golany B, Roll Y (1989) An application procedure for DEA. Omega 17(3):237–250

    Article  Google Scholar 

  • Halkos GE, Tzeremes NG (2013) A conditional directional distance function approach for measuring;regional environmental efficiency: Evidence from UK regions. Eur J Oper Res 227(1):182–189

    Article  Google Scholar 

  • Kao C (2010) Malmquist productivity index based on common-weights DEA: the case of Taiwan forests after reorganization. Omega 38(6):484–491

    Article  Google Scholar 

  • Kao C, Hung H-T (2005) Data envelopment analysis with common weights: the compromise solution approach. J Oper Res Soc 56(10):1196–1203. https://doi.org/10.1057/palgrave.jors.2601924

    Article  Google Scholar 

  • Lim S, Zhu J (2018) Primal-dual correspondence and frontier projections in two-stage network DEA models. Omega

  • Pastor JT, Asmild M, Lovell CAK (2011) The biennial Malmquist productivity change index. Socio Econ Plan Sci 45(1):10–15

    Article  Google Scholar 

  • Pastor JT, Lovell CAK (2005) A global Malmquist productivity index. Econ Lett 88(2):266–271

    Article  Google Scholar 

  • Perez K, González-Araya MC, Iriarte A (2017) Energy and GHG emission efficiency in the Chilean manufacturing industry: sectoral and regional analysis by DEA and Malmquist indexes. Energy Econ 66:290–302

    Article  Google Scholar 

  • Roll Y, Golany B (1993) Alternate methods of treating factor weights in DEA. Omega 21(1):99–109

    Article  Google Scholar 

  • Ruiz JL, Sirvent I (2016) Common benchmarking and ranking of units with DEA. Omega 65:1–9

    Article  Google Scholar 

  • Scheel H (2001) Undesirable outputs in efficiency valuations. Eur J Oper Res 132(2):400–410

    Article  Google Scholar 

  • Seiford LM, Zhu J (2002) Modeling undesirable factors in efficiency evaluation. Eur J Oper Res 142(1):16–20

    Article  Google Scholar 

  • Shan, Y., Guan, D., Zheng, H., Ou, J., Li, Y., Meng Shan, Y., Guan, D., Zheng, H., Ou, J., Li, Y., Meng, J., ... Zhang, Q. (2018). Data descriptor: China CO2 emission accounts 1997–2015

    Google Scholar 

  • Sueyoshi T, Goto M (2014) DEA radial measurement for environmental assessment: a comparative study between Japanese chemical and pharmaceutical firms. Appl Energy 115(4):502–513

    Article  CAS  Google Scholar 

  • Sueyoshi T, Goto M (2015) DEA environmental assessment in time horizon: radial approach for Malmquist index measurement on petroleum companies. Energy Econ 51(1):329–345

    Article  Google Scholar 

  • Wang J, Zhao T (2016) Regional energy-environmental performance and investment strategy for China's non-ferrous metals industry: a non-radial DEA based analysis. J Clean Prod 163:187–201

    Article  CAS  Google Scholar 

  • Wang K, Huang W, Wu J, Liu YN (2014) Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega 44(2):5–20

    Article  CAS  Google Scholar 

  • Wang K, Wei YM (2014) China’s regional industrial energy efficiency and carbon emissions abatement costs. Appl Energy 130(C):617–631

    Google Scholar 

  • Wang K, Wei YM, Zhang X (2013) Energy and emissions efficiency patterns of Chinese regions: a multi-directional efficiency analysis. Appl Energy 104(2):105–116

    Article  Google Scholar 

  • Wu J, Zhu Q, Ji X, Chu J, Liang L (2016) Two-stage network processes with shared resources and resources recovered from undesirable outputs. Eur J Oper Res 251(1):182–197

    Article  Google Scholar 

  • Yu C, Shi L, Wang Y, Chang Y, Cheng B (2016a) The eco-efficiency of pulp and paper industry in China: an assessment based on slacks-based measure and Malmquist–Luenberger index. J Clean Prod 127:511–521

  • Yu Y, Zhu W, Shi Q, Qian Z (2016b) Network-like DEA approach for environmental assessment: evidence from U.S. manufacturing sectors. J Clean Prod 139:277–286

  • Zhang T (2009) Frame work of data envelopment analysis-a model to evaluate the environmental efficiency of China’s industrial sectors. Biomed Environ Sci 22(1):8–13

    Article  Google Scholar 

  • Zhang Z, Ye J (2015) Decomposition of environmental total factor productivity growth using hyperbolic distance functions: a panel data analysis for China. Energy Econ 47:87–97

    Article  Google Scholar 

  • Zhou X, Luo R, Yao L, Cao S, Wang S, Lev B (2018) Assessing integrated water use and wastewater treatment systems in China: a mixed network structure two-stage SBM DEA model. J Clean Prod 185:533–546

    Article  Google Scholar 

  • Zhu W, Sun P, Zhang Q (2017) Context-dependent data envelopment analysis with common set of weights. Infor Information Systems & Operational Research 56(3):286–297

    Article  Google Scholar 

Download references

Funding

This research is supported by National Natural Science Funds of China (No.71771126,71801133,71871105,71701059), Jiangsu Social Science Fund(17GLB013) , Project of Jiangsu Qing Lan and Social Science Excellent Young Scholars. This research was also supported by The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0945) and The Excellent Innovation Teams of Philosophy and Social Science in Jiangsu Province (2017ZSTD022), as well as The Major Research Plan of National Social Science Foundation(18ZDA052).

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Authors and Affiliations

  1. Research Center of Information Industry Integrated Innovation and Emergency Management, School of Management, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing, 210023, China

    Weiwei Zhu

  2. School of Economics and Management, Jiangsu University of Science and Technology, 2 Mengxi Road, Zhenjiang, 212003, China

    Weiwei Zhu

  3. School of Management, Nanjing University of Posts and Telecommunications, 9 Wenyuan Road, Nanjing, 210023, China

    Yaqin Zhu

  4. College of Auditing and Evaluation, Nanjing Audit University, 86 Yushan Road west, Nanjing, 211815, China

    Yu Yu

Authors
  1. Weiwei Zhu
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  2. Yaqin Zhu
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  3. Yu Yu
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Correspondence to Yu Yu.

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Highlight

• A common-weights DEA model based on “the priority of choosing common weights” to evaluate the environmental efficiency is proposed.

• A biennial Malmquist Productivity Index (BMPI) to study the environmental productivity levels is introduced.

• The environmental efficiency of China’s 30 provinces from 2006 to 2015 is evaluated.

• The regional development in combination with China’s economic division is analyzed.

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Zhu, W., Zhu, Y. & Yu, Y. China’s regional environmental efficiency evaluation: a dynamic analysis with biennial Malmquist productivity index based on common weights. Environ Sci Pollut Res 27, 39726–39741 (2020). https://doi.org/10.1007/s11356-019-06966-2

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  • DOI: https://doi.org/10.1007/s11356-019-06966-2

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