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Productivity evaluation of urban water supply industry in China: a metafrontier-biennial cost Malmquist productivity index approach

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Abstract

Insufficient water sources, large regional differences, and serious waste caused by leakage of water make the performance of urban water supply industry in China worth exploring. This paper proposes a metafrontier-biennial cost Malmquist productivity index approach combining metafrontier-biennial data envelopment analysis and cost function based on cost minimization. The leakage of water in the urban water supply industry is considered as an undesirable output. We purpose to evaluate total factor productivity and the decomposition of its growth, including technical efficiency, technological change, allocative efficiency, factor prices effect, catch-up effect of pure technology and relative change of potential technology through the data set of water supply industry in 226 cities of China. Findings show that the U-shaped trend in cost technology gap ratio appeared during the period from 2001 to 2016, and the production technology of cities with moderate water shortage is closest to the optimal production frontier. The growth rate of total factor productivity of urban water supply industry gradually slowed down, and even slightly decreased in 2007–2008. The growth rate of productivity among the cities with extreme water shortage, severe water shortage, moderate water shortage, mild water shortage and sufficient water resources shows an increasing trend. Furthermore, the growth of productivity is mainly due to the increase of allocative efficiency and technological progress, while the decline of factor prices effect inhibits the growth of productivity.

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Notes

  1. According to the United Nations water scarcity level of classification criteria, sufficient water sources means that the per capita water resources are greater than 3000 cubic meters. Mild water shortage means that the per capita water resources are between 2000 cubic meters and 3000 cubic meters. Moderate water shortage means that the per capita water resources are between 1000 cubic meters and 2000 cubic meters. Severe water shortage means that per capita water resources are between 500 cubic meters and 1000 cubic meters, and extreme water shortage means that per capita water resources are less than 500 cubic meters.

References

  • Abbott, M., & Cohen, B. (2009). Productivity and efficiency in the water industry. Utilities Policy, 17(3–4), 233–244.

    Article  Google Scholar 

  • Ananda, J. (2018). Productivity implications of the water-energy-emissions nexus: An empirical analysis of the drinking water and wastewater sector. Journal of Cleaner Production, 196, 1097–1105.

    Article  Google Scholar 

  • Ananda, J., & Hampf, B. (2015). Measuring environmentally sensitive productivity growth: An application to the urban water sector. Ecological Economics, 116, 211–219.

    Article  Google Scholar 

  • Ananda, J., & Pawsey, N. (2019). Benchmarking service quality in the urban water industry. Journal of Productivity Analysis, 51(1), 55–72.

    Article  Google Scholar 

  • Anwandter, L. (2002). Can public sector reforms improve the efficiency of public water utilities? Environment and Development Economics, 7(4), 687–700.

    Article  Google Scholar 

  • Azad, M. A., & Ancev, T. (2014). Measuring environmental efficiency of agricultural water use: A Luenberger environmental indicator. Journal of Environmental Management, 145, 314–320.

    Article  Google Scholar 

  • Ball, E., Färe, R., Grosskopf, S., & Zaim, O. (2005). Accounting for externalities in the measurement of productivity growth: The Malmquist cost productivity measure. Structural Shange and Economic Dynamics, 16(3), 374–394.

    Article  Google Scholar 

  • Bian, Y., Yan, S., & Xu, H. (2014). Efficiency evaluation for regional urban water use and wastewater decontamination systems in China: A DEA approach. Resources, Conservation and Recycling, 83, 15–23.

    Article  Google Scholar 

  • Brockett, P. L., & Golany, B. (1996). Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Management Science, 42(3), 466–472.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1981). Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through. Management Science, 27(6), 668–697.

    Article  Google Scholar 

  • Chen, J., Du, M., & Huang, C. (2022). Efficiency and its influencing factors of urban water sector in China and major OECD countries. Journal of Cleaner Production, 373, 133885.

    Article  Google Scholar 

  • Chiu, C. R., Chiu, Y. H., Chen, Y. C., & Fang, C. L. (2016). Exploring the source of metafrontier inefficiency for various bank types in the two-stage network system with undesirable output. Pacific-Basin Finance Journal, 36, 1–13.

    Article  Google Scholar 

  • Chiu, C. R., Lu, K. H., Tsang, S. S., & Chen, Y. F. (2013). Decomposition of meta-frontier inefficiency in the two-stage network directional distance function with quasi-fixed inputs. International Transactions in Operational Research, 20(4), 595–611.

    Article  Google Scholar 

  • Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823–4839.

    Article  Google Scholar 

  • da Cruz, N. F., Carvalho, P., & Marques, R. C. (2013). Disentangling the cost efficiency of jointly provided water and wastewater services. Utilities Policy, 24, 70–77.

    Article  Google Scholar 

  • De Clercq, D., Smith, K., Chou, B., Gonzalez, A., Kothapalle, R., Li, C., Dong, X., Liu, S., & Wen, Z. (2018). Identification of urban drinking water supply patterns across 627 cities in China based on supervised and unsupervised statistical learning. Journal of Environmental Management, 223, 658–667.

    Article  Google Scholar 

  • De Witte, K., & Marques, R. C. (2010). Influential observations in frontier models, a robust non-oriented approach to the water sector. Annals of Operations Research, 181(1), 377–392.

    Article  Google Scholar 

  • Du, M., Feng, R., & Chen, Z. (2022a). Blue sky defense in low-carbon pilot cities: A spatial spillover perspective of carbon emission efficiency. Science of the Total Environment, 846, 157509.

    Article  Google Scholar 

  • Du, M., Huang, C., & Chen, Z. (2022b). Evaluating the water-saving and wastewater-reducing effects of water rights trading pilots: Evidence from a quasi-natural experiment. Journal of Environmental Management, 319, 115706.

    Article  Google Scholar 

  • Du, M., Liao, L., Wang, B., & Chen, Z. (2021). Evaluating the effectiveness of the water-saving society construction in China: A quasi-natural experiment. Journal of Environmental Management, 277, 111394.

    Article  Google Scholar 

  • Färe, R., Fèare, R., Grosskopf, S., & Lovell, C. K. (1994). Production frontiers. Cambridge University Press.

    Google Scholar 

  • Ferro, G., & Mercadier, A. C. (2016). Technical efficiency in Chile’s water and sanitation providers. Utilities Policy, 43, 97–106.

    Article  Google Scholar 

  • Fisher, I. (1922). The making of index numbers: A study of their varieties, tests, and reliability (no. 1). Houghton Mifflin.

  • Garcia-Valiñas, M. A., & Muñiz, M. A. (2007). Is DEA useful in the regulation of water utilities? A dynamic efficiency evaluation (a dynamic efficiency evaluation of water utilities). Applied Economics, 39(2), 245–252.

    Article  Google Scholar 

  • Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? The Quarterly Journal of Economics, 114(1), 83–116.

    Article  Google Scholar 

  • Higuerey, A., Trujillo, L., & González, M. M. (2017). Has efficiency improved after the decentralization in the water industry in Venezuela? Utilities Policy, 49, 127–136.

    Article  Google Scholar 

  • Hu, J. L., Wang, S. C., & Yeh, F. Y. (2006). Total-factor water efficiency of regions in China. Resources Policy, 31(4), 217–230.

    Article  Google Scholar 

  • Huang, M. Y., Juo, J. C., & Fu, T. T. (2015). Metafrontier cost Malmquist productivity index: An application to Taiwanese and Chinese commercial banks. Journal of Productivity Analysis, 44(3), 321–335.

    Article  Google Scholar 

  • Li, Y., Chiu, Y. H., Li, Y., Cen, H., & Lin, T. Y. (2021). Dynamic analysis of residential and enterprise water supply and leakage efficiencies. Environmental Science and Pollution Research, 28(29), 39471–39492.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Lu, Y. H., Chiu, Y. H., Chiu, C. R., & Hung, Y. C. (2014). Two-stage efficiency measurement and technological heterogeneity: Evidence from the biotechnological industry in Taiwan. Asia-Pacific Journal of Operational Research, 31(01), 1450007.

    Article  Google Scholar 

  • Maniadakis, N., & Thanassoulis, E. (2004). A cost Malmquist productivity index. European Journal of Operational Research, 154(2), 396–409.

    Article  Google Scholar 

  • Maziotis, A., Molinos-Senante, M., & Sala-Garrido, R. (2017). Assesing the impact of quality of service on the productivity of water industry: A Malmquist–Luenberger approach for England and Wales. Water Resources Management, 31(8), 2407–2427.

    Article  Google Scholar 

  • Mbuvi, D., De Witte, K., & Perelman, S. (2012). Urban water sector performance in Africa: A step-wise bias-corrected efficiency and effectiveness analysis. Utilities Policy, 22, 31–40.

    Article  Google Scholar 

  • Molinos-Senante, M., Gémar, G., Gómez, T., Caballero, R., & Sala-Garrido, R. (2016). Eco-efficiency assessment of wastewater treatment plants using a weighted Russell directional distance model. Journal of Cleaner Production, 137, 1066–1075.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Molinos-Senante, M., Maziotis, A., & Sala-Garrido, R. (2017). Assessing the productivity change of water companies in England and Wales: A dynamic metafrontier approach. Journal of Environmental Management, 197, 1–9.

    Article  Google Scholar 

  • Molinos-Senante, M., & Sala-Garrido, R. (2017). Decomposition of productivity growth of water and sewerage companies: An empirical approach for Chile. Water Resources Management, 31(13), 4309–4321.

    Article  Google Scholar 

  • Norton, J. W., Jr., & Weber, W. J., Jr. (2009). Water utility efficiency assessment using a data envelopment analysis procedure. Journal of Infrastructure Systems, 15(2), 80–87.

    Article  Google Scholar 

  • O’Donnell, C. J., Rao, D. S., & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical Economics, 34(2), 231–255.

    Article  Google Scholar 

  • Pastor, J. T., Asmild, M., & Lovell, C. K. (2011). The biennial Malmquist productivity change index. Socio-Economic Planning Sciences, 45(1), 10–15.

    Article  Google Scholar 

  • Picazo-Tadeo, A. J., Sáez-Fernández, F. J., & González-Gómez, F. (2009). The role of environmental factors in water utilities’ technical efficiency. Empirical evidence from Spanish companies. Applied Economics, 41(5), 615–628.

    Article  Google Scholar 

  • Pointon, C., & Matthews, K. (2016). Dynamic efficiency in the English and Welsh water and sewerage industry. Omega, 58, 86–96.

    Article  Google Scholar 

  • Portela, M. C. A. S., Thanassoulis, E., Horncastle, A., & Maugg, T. (2011). Productivity change in the water industry in England and Wales: Application of the meta-Malmquist index. Journal of the Operational Research Society, 62(12), 2173–2188.

    Article  Google Scholar 

  • Romano, G., & Guerrini, A. (2011). Measuring and comparing the efficiency of water utility companies: A data envelopment analysis approach. Utilities Policy, 19(3), 202–209.

    Article  Google Scholar 

  • Romano, G., Molinos-Senante, M., & Guerrini, A. (2017). Water utility efficiency assessment in Italy by accounting for service quality: An empirical investigation. Utilities Policy, 45, 97–108.

    Article  Google Scholar 

  • Saal, D. S., Parker, D., & Weyman-Jones, T. (2007). Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry: 1985–2000. Journal of Productivity Analysis, 28(1–2), 127–139.

    Article  Google Scholar 

  • Sala-Garrido, R., Molinos-Senante, M., & Mocholí-Arce, M. (2018). Assessing productivity changes in water companies: A comparison of the Luenberger and Luenberger–Hicks–Moorsteen productivity indicators. Urban Water Journal, 15(7), 626–635.

    Article  Google Scholar 

  • See, K. F., & Ma, Z. (2018). Does non-revenue water affect Malaysia’s water services industry productivity? Utilities Policy, 54, 125–131.

    Article  Google Scholar 

  • Song, M., Wang, R., & Zeng, X. (2018). Water resources utilization efficiency and influence factors under environmental restrictions. Journal of Cleaner Production, 184, 611–621.

    Article  Google Scholar 

  • Sun, Y., Ding, W., Yang, Z., Yang, G., & Du, J. (2020). Measuring China’s regional inclusive green growth. Science of the Total Environment, 713, 136367.

    Article  Google Scholar 

  • Ye, Q., Li, Y., Zhuo, L., Zhang, W., Xiong, W., Wang, C., & Wang, P. (2018). Optimal allocation of physical water resources integrated with virtual water trade in water scarce regions: A case study for Beijing, China. Water Research, 129, 264–276.

    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. Journal of Cleaner Production, 185, 533–546.

    Article  Google Scholar 

  • Zhou, Z., Wu, H., & Song, P. (2019). Measuring the resource and environmental efficiency of industrial water consumption in China: A non-radial directional distance function. Journal of Cleaner Production, 240, 118169.

    Article  Google Scholar 

  • Zschille, M. (2015). Consolidating the water industry: An analysis of the potential gains from horizontal integration in a conditional efficiency framework. Journal of Productivity Analysis, 44(1), 97–114.

    Article  Google Scholar 

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Acknowledgements

This paper is supported by the Guangzhou Philosophy and Social Science Planning 2020 Annual Project (2020GZGJ57).

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Authors and Affiliations

  1. School of Economics and Management, South China Normal University, Guangzhou, 510006, Guangdong, China

    Minzhe Du

  2. School of Economics, Jinan University, Guangzhou, 510632, Guangdong, China

    Bing Wang & Zhongfei Chen

  3. School of Public Finance and Taxation, Guangdong University of Finance and Economics, Guangzhou, 510320, Guangdong, China

    Liping Liao

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Du, M., Wang, B., Chen, Z. et al. Productivity evaluation of urban water supply industry in China: a metafrontier-biennial cost Malmquist productivity index approach. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05294-6

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