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Analysis of China’s Regional Economic Environmental Performance: A Non-radial Multi-objective DEA Approach

  • Tao Ding
  • Zhixiang ZhouEmail author
  • Qianzhi Dai
  • Liang Liang
Article

Abstract

One of the hot topics is how to achieve more accurate results of economic and environmental efficiency evaluation in China. Previous data envelopment analysis (DEA) literature on environmental performance measurement often follow the concept of non-radial efficiency measure for calculating the performance on resources and economic-environmental factors respectively. This paper proposes a non-radial and multi-objective generalized DEA model for economic-environmental efficiency evaluation. The results illustrate that this model can not only analyze the relationship between DEA efficiency and Pareto optimality of the multi-objective programming problem defined on the production possibility set, but also obtain the performance improvement direction by using the projection of decision making units. Finally, a case on measuring the economic-environmental performance of Chinese provincial regions is employed to indicate that the proposed model can be helpful to promote the accuracy of economic-environmental efficiency evaluation.

Keywords

Data envelopment analysis (DEA) Undesirable outputs Non-radial Multi-objective 

Notes

Acknowledgements

The authors are grateful to Professor Joe Zhu for his suggestions and comments on earlier versions of the paper. This research is financially supported by the National Natural Science Foundation of China (71801068, 71701059, 71471053 and 71828101)

References

  1. Aggelopoulos, E., & Georgopoulos, A. (2017). Bank branch efficiency under environmental change: a bootstrap DEA on monthly Profit and Loss accounting statements of Greek retail branches. European Journal of Operational Research, 261(3), 1170–1188.Google Scholar
  2. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale efficiencies in DEA. Management Science, 30(9), 1078–1092.Google Scholar
  3. Beltrán-Esteve, M., & Picazo-Tadeo, A. J. (2017). Assessing environmental performance in the European Union: Eco-innovation versus catching-up. Energy Policy, 104, 240–252.Google Scholar
  4. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.Google Scholar
  5. Chen, Y. (2003). A non-radial Malmquist productivity index with an illustrative application to Chinese major industries. International Journal of Production Economics, 83(1), 27–35.Google Scholar
  6. Chowdhury, H., & Zelenyuk, V. (2016). Performance of hospital services in Ontario: DEA with truncated regression approach. Omega-The International Journal of Management Science, 63, 111–122.Google Scholar
  7. Chu, J., Wu, J., Zhu, Q., An, Q., & Xiong, B. (2016). Analysis of China’s regional eco-efficiency: A DEA two-stage network approach with equitable efficiency decomposition. Computational Economics.  https://doi.org/10.1007/s10614-015-9558-8.Google Scholar
  8. Ding, C., & Li, J. (2014). Analysis over factors of innovation in China’s fast economic growth since its beginning of reform and opening up. AI & SOCIETY, 29(3), 377–386.Google Scholar
  9. Färe, R., & Grosskopf, S. (1985). A nonparametric cost approach to scale efficiency. The Scandinavian Journal of Economics, 87, 594–604.Google Scholar
  10. Färe, R., Grosskopf, S., & Hernandez-Sancho, F. (2004). Environmental performance: an index number approach. Resource and Energy Economics, 26(4), 343–352.Google Scholar
  11. Färe, R., Grosskopf, S., Lovell, C. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach. The review of Economics and Statistics, 71, 90–98.Google Scholar
  12. Fei, Y., Bi, G., Song, W., & Luo, Y. (2016). Measuring the efficiency of two-stage production process in the presence of undesirable outputs. Computational Economics.  https://doi.org/10.1007/s10614-016-9621-0.Google Scholar
  13. Huang, C. W., Chiu, Y. H., Fang, W. T., & Shen, N. (2014). Assessing the performance of Taiwan’s environmental protection system with a non-radial network DEA approach. Energy Policy, 74, 547–556.Google Scholar
  14. Krivonozhko, V. E., Førsund, F. R., & Lychev, A. V. (2014). Measurement of returns to scale using non-radial DEA models. European Journal of Operational Research, 232(3), 664–670.Google Scholar
  15. Mardani, A., Zavadskas, E. K., Streimikiene, D., Jusoh, A., & Khoshnoudi, M. (2016). A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency. Renewable and Sustainable Energy Reviews, 70, 1298–1322.Google Scholar
  16. Masuda, K. (2016). Measuring eco-efficiency of wheat production in Japan: a combined application of life cycle assessment and data envelopment analysis. Journal of Cleaner Production, 126, 373–381.Google Scholar
  17. Meng, F. Y., Fan, L. W., Zhou, P., & Zhou, D. Q. (2013). Measuring environmental performance in China’s industrial sectors with non-radial DEA. Mathematical and Computer Modelling, 58(5), 1047–1056.Google Scholar
  18. Sagarra, M., Marmolinero, C., & Agasisti, T. (2014). Exploring the efficiency of Mexican universities: Integrating Data Envelopment Analysis and Multidimensional Scaling. Omega-The International Journal of Management Science, 55(4), 1324–1325.Google Scholar
  19. Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis. Journal of econometrics, 46(1), 7–38.Google Scholar
  20. Song, M., Peng, J., Wang, J., & Dong, L. (2018). Better resource management: An improved resource and environmental efficiency evaluation approach that considers undesirable outputs. Resources, Conservation and Recycling, 128, 197–205.Google Scholar
  21. Song, M., Peng, J., Wang, J., & Zhao, J. (2017). Environmental efficiency and economic growth of China: A ray slack-based model analysis. European Journal of Operational Research, 269(1), 51–63.Google Scholar
  22. Sueyoshi, T., & Wang, D. (2014). Radial and non-radial approaches for environmental assessment by data envelopment analysis: Corporate sustainability and effective investment for technology innovation. Energy Economics, 45, 537–551.Google Scholar
  23. Sueyoshi, T., Yuan, Y., Li, A., & Wang, D. (2017). Social sustainability of Provinces in China: A Data Envelopment Analysis (DEA) window analysis under the concepts of natural and managerial disposability. Sustainability, 9(11), 1–18.Google Scholar
  24. Thanassoulis, E., Dey, P. K., Petridis, K., Goniadis, I., & Georgiou, A. C. (2017). Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis. Journal of the Operational Research Society, 68(4), 431–445.Google Scholar
  25. Toloo, M., & Jalili, R. (2016). LU decomposition in DEA with an application to hospitals. Computational Economics, 47(3), 1–16.Google Scholar
  26. Tsai, H., Wu, J., & Zhou, Z. (2011). Managing efficiency in international tourist hotels in Taipei using a DEA model with non-discretionary inputs. Asia Pacific Journal of Tourism Research, 16(4), 417–432.Google Scholar
  27. Wei, Q., Yan, H., & Xiong, L. (2008). A bi-objective generalized data envelopment analysis model and point-to-set mapping projection. European Journal of Operational Research, 190(3), 855–876.Google Scholar
  28. Yang, M., An, Q. X., Ding, T., Yin, P. Z., & Liang, L. (2017). Carbon emission allocation in China based on gradually efficiency improvement and emission reduction planning principle. Annals of Operations Research.  https://doi.org/10.1007/s10479-017-2682-1.Google Scholar
  29. Yu, G., Wei, Q., Brockett, P., & Zhou, L. (1996). Construction of all DEA efficient surfaces of the production possibility set under the generalized data envelopment analysis model. European Journal of Operational Research, 95(3), 491–510.Google Scholar
  30. Zhang, C., Wang, Q. W., Shi, D., Li, P. F., & Cai, W. H. (2016). Scenario-based potential effects of carbon trading in China: An integrated approach. Applied Energy, 182, 177–190.Google Scholar
  31. Zhang, N., & Chen, Z. (2017). Sustainability characteristics of China’s Poyang Lake Eco-Economics Zone in the big data environment. Journal of Cleaner Production, 142, 642–653.Google Scholar
  32. Zhang, N., & Choi, Y. (2013). Total-factor carbon emission performance of fossil fuel power plants in China: A metafrontier non-radial Malmquist index analysis. Energy Economics, 40, 549–559.Google Scholar
  33. Zhou, P., Ang, B. W., & Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30(1), 1–14.Google Scholar
  34. Zhou, Z., Amowine, N., & Huang, D. (2018). Quantitative efficiency assessment based on the dynamic slack-based network data envelopment analysis for commercial banks in Ghana. South African Journal of Economic and Management Sciences, 21(1), 1–11.Google Scholar
  35. Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (Vol. 213). Berlin: Springer.Google Scholar
  36. Zhu, Q., Wu, J., Li, X., & Xiong, B. (2016). China’s regional natural resource allocation and utilization: aDEA-based approach in a big data environment. Journal of Cleaner Production, 142(2), 809–818.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tao Ding
    • 1
  • Zhixiang Zhou
    • 1
    Email author
  • Qianzhi Dai
    • 1
  • Liang Liang
    • 2
  1. 1.School of EconomicsHefei University of TechnologyHefei CityPeople’s Republic of China
  2. 2.School of ManagementHefei University of TechnologyHefei CityPeople’s Republic of China

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