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Assessing the Green Total Factor Productivity of Water Use in Mainland China

  • Ning, Meng (Institutes of Science and Development, Chinese Academy of Sciences) ;
  • Wu, Zheru (International Economic & Technical Cooperation and Exchange Center, Ministry of Water Resources of the People's Republic of China) ;
  • Zhou, Zhitian (Institutes of Science and Development, Chinese Academy of Sciences) ;
  • Yang, Duogui (Institutes of Science and Development, Chinese Academy of Sciences)
  • Received : 2021.01.05
  • Published : 2021.01.30

Abstract

The significance of high-quality development and green total factor productivity has attracted widespread attention and research, while few studies on green total factor productivity that considers the use of water resources have been conducted in the context of water shortages and water stress. In this study, the green total factor productivity of water use from 2005 to 2015 in mainland China is evaluated based on the global Malmquist-Luenberger productivity index. Results show that: (1) China's green total factor productivity of water use has been improving since 2005 with an annual global Malmquist-Luenberger productivity index of 1.0104. (2) At the regional level, the eastern zone in mainland China owns the highest green total factor productivity of water use, while that in the intermediate zone ranks last. (3) The green total factor productivity of water use in the southern region (1.0113) significantly higher than that in the northern region (1.0095), and also higher than the national average level in the same period. BPC index has been the most important incluencing factor of green total factor productivity of water use at both national level and regional level since 2011.

Keywords

References

  1. Abbott, M., Cohen, B., Wang, W.C., 2012. The performance of the urban water and wastewater sectors in Australia. Utilities Policy, 2012, 52-63. https://doi.org/10.1016/j.jup.2011.11.003
  2. Banker, R.D., Charnes, A., Cooper, W., 1984. Some models for estimating technical and scale inefficiency in Data Envelopment Analysis. Management Science, 30, 1078-1092. https://doi.org/10.1287/mnsc.30.9.1078
  3. Cetrulo, T.B., Ferreira, D.F.C, Marques, R.C., Malheiros, T.F., 2020. Water utilities performance analysis in developing countries: On an adequate model for universal access. Journal of Environmental Management, 268: 110662. https://doi.org/10.1016/j.jenvman.2020.110662
  4. Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
  5. Chung, Y.H., Fare, R., Grosskopf, S. 1997. Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51(3), 229-240. https://doi.org/10.1006/jema.1997.0146
  6. Deng, G., Li, L., Song, Y., 2016. Provincial water use efficiency measurement and factor analysis in China: Based on SBM-DEA model. Ecological Indicators, 69, 12-18. https://doi.org/10.1016/j.ecolind.2016.03.052
  7. Fuentes, R., Torregrosa-Marti, T., Hernandez-Sancho, F., 2017. Productivity of wastewater treatment plants in the Valencia Region of Spain. Utilities Policy, 46, 58-70. https://doi.org/10.1016/j.jup.2017.04.004
  8. Gadanakis, Y., Bennett, R., Park, J., Areal, F.J., 2015. Improving productivity and water use efficiency: A case study of farms in England. Agricultural Water Management, 160, 22-32. https://doi.org/10.1016/j.agwat.2015.06.020
  9. Gautam, T.K., Paudel K.P., Guidry, K.M., 2020. An evaluation of irrigation water use efficiency in crop production using a Data Envelopment Analysis approach: A case of Louisana, USA. Water, 12, 3193. https://doi.org/10.3390/w12113193
  10. Huong, L.T.T., Takahashi, Y., Nomura, H., Duy, L.V., Son, C.T., Yabe, M., 2020. Water-use efficiency of alternative pig farming systems in Vietnam. Resources, Conservation & Recycling, 161, 104926. https://doi.org/10.1016/j.resconrec.2020.104926
  11. Liu, K.D., Yang, G.L., Yang, D.G., 2020. Investigating industrial water-use efficiency in mainland China: An improved SBM-DEA model. Journal of Environmental Management, 270, 110859. https://doi.org/10.1016/j.jenvman.2020.110859
  12. Liu, K.D., Yang, G.L., Yang, D.G., 2020. Industrial water-use efficiency in China: Regional heterogeneity and incentives identification. Journal of Cleaner Production, 258, 120828. https://doi.org/10.1016/j.jclepro.2020.120828
  13. Lu, X., Xu, C., 2019. The difference and convergence of total factor productivity of inter-provincial water resources in China based on three-stage DEA-Malmquist index model. Sustainable Computing: Informatics and Systems, 22, 75-83. https://doi.org/10.1016/j.suscom.2019.01.019
  14. Molinos-Senante, M., Maziotis, A., Sala-Garrido, R., 2014. The Luenberger productivity indicator in the water industry: An empirical analysis for England and Wales. Utilities Policy, 30, 18-28. https://doi.org/10.1016/j.jup.2014.07.001
  15. Pan, Z., Wang, Y., Zhou, Y., Wang, Y., 2020. Analysis of the water use efficiency using super-efficiency data envelopment analysis. Applied Water Science, 10, 139. https://doi.org/10.1007/s13201-020-01223-1
  16. Wang, G., Chen, J., Wu, F., Li, Z., 2015. An integrated analysis of agricultural water-use efficiency: A case study in the Heihe River Basin in Northwest China. Physics and Chemistry of the Earth, 80-90, 3-9. https://doi.org/10.1016/j.pce.2015.10.009
  17. Wang, S., Zhou, L., Wang, H., Li, X., 2018. Water use efficiency and its influencing factors in China: Based on the Data Envelopment Analysis (DEA) - Tobit model. Water, 10, 832. https://doi.org/10.3390/w10070832
  18. Wang, Y., Bian, Y., Xu, H., 2015. Water use efficiency and related pollutants' abatement costs of regional industrial systems in China: a slacks-based measure approach. Journal of Cleaner Production, 101, 301-310. https://doi.org/10.1016/j.jclepro.2015.03.092
  19. Yan, J. Spatiotemporal analysis for investment efficiency of China's rural water conservancy based on DEA model and Malmquist productivity index model. Sustainable Computing: Informatics and Systems, 21, 56-71. https://doi.org/10.1016/j.suscom.2018.11.004