The regional green growth and sustainable development of China in the presence of sustainable resources recovered from pollutions

  • Jie Wu
  • Dacheng Huang
  • Zhixiang ZhouEmail author
  • Qingyuan Zhu


The rapid economic development of China has intensified the country’s many problems. Among them, energy shortage and environmental pollution are two main problems, which highly affects the economic growth and sustainable development. To achieve more rapid green growth, the innovative technology by reusing the environmental wastes has been widely used since doing so not only decreases the environment pollution, but also further brings more natural resource. The present paper establishes a two-stage structure for evaluating the regional green growth and sustainable development in China by calculating the efficiency of “energy saving” and “pollution treatment” separately. Specifically, a set of models based on slack-based measure approach are constructed in which non-discretionary inputs can be calculated in both resource utilization stage and pollution treatment stage. Comparing with the traditional models, the new proposed models can measure the performance of resources saving and pollution treatment with considering the influence of non-discretionary inputs. An empirical application on Chinese 30 regions during 2011–2015 have been done to illustrate the use of our framework and the performance of regional green growth and sustainable development. Based on the efficiency results, we find that the efficiency scores of the provinces in central and northeast area are lower, which is mostly caused by their poor performance on “pollution treatment”. Both the environmental efficiency scores and target values for performance improvement are obtained in this paper to enlighten the corresponding decision-makers.


Green growth Environmental efficiency Non-discretionary input Technology innovation Slack based measure 



The research is supported by the National Natural Science Foundation of China under Grants (Nos. 71701059 and 71571173).


  1. Afonso, A., & Aubyn, M. S. (2006). Cross-country efficiency of secondary education provision: A semi-parametric analysis with non-discretionary inputs. Economic Modelling, 23(3), 476–491.CrossRefGoogle Scholar
  2. Ali, A. I., & Seiford, L. M. (1990). Translation invariance in data envelopment analysis. Operations Research Letters, 9(6), 403–405.CrossRefGoogle Scholar
  3. Azizi, H., & Ajirlu, H. G. (2011). Measurement of the worst practice of decision-making units in the presence of non-discretionary factors and imprecise data. Applied Mathematical Modelling, 35(9), 4149–4156.CrossRefGoogle Scholar
  4. Bian, Y., Liang, N., & Xu, H. (2015). Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach. Journal of Cleaner Production, 87, 348–356.CrossRefGoogle Scholar
  5. Camanho, A. S., Portela, M. C., & Vaz, C. B. (2009). Efficiency analysis accounting for internal and external non-discretionary factors. Computers & Operations Research, 36(5), 1591–1601.CrossRefGoogle Scholar
  6. 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, 30(1–2), 91–107.CrossRefGoogle Scholar
  7. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.CrossRefGoogle Scholar
  8. Cordero-Ferrera, J. M., Pedraja-Chaparro, F., & Santín-González, D. (2010). Enhancing the inclusion of non-discretionary inputs in DEA. Journal of the Operational Research Society, 61(4), 574–584.CrossRefGoogle Scholar
  9. Fried, H. O., Schmidt, S. S., & Yaisawarng, S. (1999). Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis, 12(3), 249–267.CrossRefGoogle Scholar
  10. Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17(3), 237–250.CrossRefGoogle Scholar
  11. Golany, B., & Roll, Y. (1993). Some extensions of techniques to handle non-discretionary factors in data envelopment analysis. Journal of Productivity Analysis, 4(4), 419–432.CrossRefGoogle Scholar
  12. Haelermans, C., & Ruggiero, J. (2013). Estimating technical and allocative efficiency in the public sector: A nonparametric analysis of Dutch schools. European Journal of Operational Research, 227(1), 174–181.CrossRefGoogle Scholar
  13. Jakob, M., & Edenhofer, O. (2014). Green growth, degrowth, and the commons. Oxford Review of Economic Policy, 30(3), 447–468.CrossRefGoogle Scholar
  14. Li, Y., Shi, X., Emrouznejad, A., & Liang, L. (2018). Environmental performance evaluation of Chinese industrial systems: A network SBM approach. Journal of the Operational Research Society, 69(6), 825–839.CrossRefGoogle Scholar
  15. Liang, L., Li, Y., & Li, S. (2009). Increasing the discriminatory power of DEA in the presence of the undesirable outputs and large dimensionality of data sets with PCA. Expert Systems with Applications, 36(3), 5895–5899.CrossRefGoogle Scholar
  16. Lin, E. Y. Y., Chen, P. Y., & Chen, C. C. (2013). Measuring the environmental efficiency of countries: A directional distance function metafrontier approach. Journal of Environmental Management, 119, 134–142.Google Scholar
  17. Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173(1), 177–194.CrossRefGoogle Scholar
  18. Molinos-Senante, M., Hanley, N., & Sala-Garrido, R. (2015). Measuring the CO2 shadow price for wastewater treatment: A directional distance function approach. Applied Energy, 144, 241–249.CrossRefGoogle Scholar
  19. Mousavi-Avval, S. H., Mohammadi, A., Rafiee, S., & Tabatabaeefar, A. (2012). Assessing the technical efficiency of energy use in different barberry production systems. Journal of Cleaner Production, 27, 126–132.CrossRefGoogle Scholar
  20. Pearce, D. W., & Turner, R. K. (1990). Economics of natural resources and the environment. Baltimore: JHU Press.Google Scholar
  21. Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400–410.CrossRefGoogle Scholar
  22. Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20.CrossRefGoogle Scholar
  23. Song, W., Bi, G. B., Wu, J., & Yang, F. (2017). What are the effects of different tax policies on China’s coal-fired power generation industry? An empirical research from a network slacks-based measure perspective. Journal of Cleaner Production, 142, 2816–2827.CrossRefGoogle Scholar
  24. Song, M. L., Fisher, R., Wang, J. L., & Cui, L. B. (2018). Environmental performance evaluation with big data: Theories and methods. Annals of Operations Research, 270(1–2), 459–472.CrossRefGoogle Scholar
  25. Song, M., & Wang, S. (2018). Market competition, green technology progress and comparative advantages in China. Management Decision, 56(1), 188–203.CrossRefGoogle Scholar
  26. Song, M., Wang, S., & Liu, W. (2014). A two-stage DEA approach for environmental efficiency measurement. Environmental Monitoring and Assessment, 186(5), 3041–3051.CrossRefGoogle Scholar
  27. Sufian, F. (2007). The efficiency of Islamic banking industry: A non-parametric analysis with non-discretionary input variable. Islamic Economic Studies, 12(1, 2), 53–87.Google Scholar
  28. Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European journal of operational research, 197, 243–252.CrossRefGoogle Scholar
  29. Tone, K., & Tsutsui, M. (2010). Dynamic DEA: A slacks-based measure approach. Omega, 38(3), 145–156.CrossRefGoogle Scholar
  30. 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.CrossRefGoogle Scholar
  31. Wu, J., Zhu, Q., Chu, J., & Liang, L. (2015). Two-stage network structures with undesirable intermediate outputs reused: A DEA based approach. Computational Economics, 46, 455–477.CrossRefGoogle Scholar
  32. Wu, J., Yin, P., Sun, J., Chu, J., & Liang, L. (2016a). Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective. European Journal of Operational Research, 254(3), 1047–1062.CrossRefGoogle Scholar
  33. Wu, J., Zhu, Q., Ji, X., Chu, J., & Liang, L. (2016b). Two-stage network processes with shared resources and resources recovered from undesirable outputs. European Journal of Operational Research, 251(1), 182–197.CrossRefGoogle Scholar
  34. Zhang, T., Chiu, Y. H., Li, Y., & Lin, T. Y. (2018). Air pollutant and health-efficiency evaluation based on a dynamic network data envelopment analysis. International Journal of Environmental Research and Public Health, 15(9), 2046.CrossRefGoogle Scholar
  35. Zhou, Z., Guo, X., Wu, H., & Yu, J. (2018a). Evaluating air quality in China based on daily data: Application of integer data envelopment analysis. Journal of Cleaner Production, 198, 304–311.CrossRefGoogle Scholar
  36. Zhou, X., Luo, R., Yao, L., Cao, S., Wang, S., & Lev, B. (2018b). Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model. Journal of Cleaner Production, 185, 533–546.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Jie Wu
    • 1
  • Dacheng Huang
    • 1
  • Zhixiang Zhou
    • 2
    Email author
  • Qingyuan Zhu
    • 3
    • 4
  1. 1.School of ManagementUniversity of Science and Technology of ChinaHefeiPeople’s Republic of China
  2. 2.School of EconomicsHefei University of TechnologyHefeiPeople’s Republic of China
  3. 3.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  4. 4.Research Center for Soft Energy ScienceNanjing University of Aeronautics and AstronauticsNanjingChina

Personalised recommendations