Measuring the wastewater treatment plants productivity change: Comparison of the Luenberger and Luenberger-Hicks-Moorsteen Productivity Indicators

Abstract It is essential to assess the productivity of wastewater treatment plants (WWTPs) to improve their economic and technical performance over time. In doing so, reliable indexes should be used to avoid biased conclusions leading to unsuccessful policy and managerial measures. Ratio-based indexes are typically employed, but are infeasible when any of the variables are equal or close to zero. To overcome this limitation, this paper presents the innovative approach of applying and comparing two difference-based productivity indicators, Luenberger (LPI) and Luenberger-Hicks-Moorsteen (LHMPI), to evaluate how productivity changes in a sample of WWTPs. Because the LHMPI is an additively complete indicator, the contribution of operational costs (inputs) and pollutant removal efficiency (outputs) to changes in productivity was quantified. The results showed that, on average, LPI and LHMPI estimations were not statistically different, except for at WWTP level. Moreover, for most facilities, there was a trade-off between operational costs and pollutants removed from the wastewater. The results demonstrate that WWTP managers and regulators should focus on the indexes or indicators used to evaluate the performance of facilities to avoid making biased conclusions.

[1]  Siyu Zeng,et al.  Measuring and explaining eco-efficiencies of wastewater treatment plants in China: An uncertainty analysis perspective. , 2017, Water research.

[2]  M. Molinos-Senante,et al.  Assessing changes in eco-productivity of wastewater treatment plants: The role of costs, pollutant removal efficiency, and greenhouse gas emissions , 2018 .

[3]  K. Kerstens,et al.  Comparing Luenberger and Luenberger-Hicks-Moorsteen productivity indicators: How well is total factor productivity approximated? , 2018 .

[4]  Pieter Jan Kerstens,et al.  Decomposing the Luenberger-Hicks-Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture , 2017, Eur. J. Oper. Res..

[5]  G Romano,et al.  Measuring the efficiency of wastewater services through Data Envelopment Analysis. , 2015, Water science and technology : a journal of the International Association on Water Pollution Research.

[6]  María Molinos-Senante,et al.  Development and application of the Hicks-Moorsteen productivity index for the total factor productivity assessment of wastewater treatment plants , 2016 .

[7]  Laura Carosi,et al.  Water pollution in wastewater treatment plants: An efficiency analysis with undesirable output , 2017, Eur. J. Oper. Res..

[8]  María Molinos-Senante,et al.  Comparing the dynamic performance of wastewater treatment systems: A metafrontier Malmquist productivity index approach. , 2015, Journal of environmental management.

[9]  Biresh K. Sahoo,et al.  Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries , 2011 .

[10]  Andrés J Picazo-Tadeo,et al.  Ownership and Performance in Water Services Revisited: Does Private Management Really Outperform Public? , 2017, Water Resources Management.

[11]  H. Awad,et al.  Environmental and cost life cycle assessment of different alternatives for improvement of wastewater treatment plants in developing countries. , 2019, The Science of the total environment.

[12]  Pedro Simões,et al.  Influence of regulation on the productivity of waste utilities. What can we learn with the Portuguese experience? , 2012, Waste management.

[13]  Brian P. Bledsoe,et al.  Benchmarking sustainability of urban water infrastructure systems in China , 2018 .

[14]  Kristiaan Kerstens,et al.  EXACT RELATIONS BETWEEN FOUR DEFINITIONS OF PRODUCTIVITY INDICES AND INDICATORS , 2012 .

[15]  C. O'Donnell An aggregate quantity framework for measuring and decomposing productivity change , 2012 .

[16]  Antonio Dell'Anno,et al.  Bioremediation of contaminated marine sediments can enhance metal mobility due to changes of bacterial diversity. , 2015, Water research.

[17]  Kristiaan Kerstens,et al.  A Luenberger-Hicks-Moorsteen productivity indicator: its relation to the Hicks-Moorsteen productivity index and the Luenberger productivity indicator , 2004 .

[18]  Ying-Ming Wang,et al.  Measuring Malmquist productivity index: A new approach based on double frontiers data envelopment analysis , 2011, Math. Comput. Model..

[19]  W. Diewert Decompositions of productivity growth into sectoral effects , 2015 .

[20]  B. Dollery,et al.  Are there scale economies in urban waste and wastewater municipal services? A non-radial input-oriented model applied to the Portuguese local government , 2019, Journal of Cleaner Production.

[21]  Benjamin Lev,et al.  Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model , 2018, Journal of Cleaner Production.

[22]  W. Cooper,et al.  Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software , 1999 .

[23]  Andrew C. Gross,et al.  Water and wastewater treatment worldwide: the industry and the market for equipment and chemicals , 2018 .

[24]  R. Fuentes,et al.  Productivity of wastewater treatment plants in the Valencia Region of Spain , 2017 .

[25]  Gumersindo Feijoo,et al.  Dynamic environmental efficiency assessment for wastewater treatment plants , 2018, The International Journal of Life Cycle Assessment.

[26]  Corrado lo Storto,et al.  Efficiency, Conflicting Goals and Trade-Offs: A Nonparametric Analysis of the Water and Wastewater Service Industry in Italy , 2018 .

[27]  Rolf Färe,et al.  Exact relations between Luenberger productivity indicators and Malmquist productivity indexes , 2008 .

[28]  María Molinos-Senante,et al.  Assessing the efficiency of wastewater treatment plants: A double-bootstrap approach , 2017 .

[29]  Gumersindo Feijoo,et al.  Eco-efficiency analysis of Spanish WWTPs using the LCA + DEA method. , 2015, Water research.

[30]  Giulia Romano,et al.  Energy Efficiency Drivers in Wastewater Treatment Plants: A Double Bootstrap DEA Analysis , 2017 .

[31]  M Molinos-Senante,et al.  Techno-economical efficiency and productivity change of wastewater treatment plants: the role of internal and external factors. , 2011, Journal of environmental monitoring : JEM.

[32]  Kristiaan Kerstens,et al.  Infeasibility and Directional Distance Functions with Application to the Determinateness of the Luenberger Productivity Indicator , 2009 .

[33]  R. Färe,et al.  Benefit and Distance Functions , 1996 .

[34]  D. Luenberger Benefit functions and duality , 1992 .

[35]  Gustavo Ferro,et al.  Regulation and performance: A production frontier estimate for the Latin American water and sanitation sector , 2011 .

[36]  F. Førsund,et al.  Malmquist Productivity Indexes: An Empirical Comparison , 1998 .

[37]  R. Chambers Exact nonradial input, output, and productivity measurement , 2002 .

[38]  C. Barros,et al.  Productivity Change of Nigerian Insurance Companies: 1994–2005 , 2008 .

[39]  R. Färe,et al.  Theory and Application of Directional Distance Functions , 2000 .

[40]  Jayanath Ananda,et al.  Productivity implications of the water-energy-emissions nexus: An empirical analysis of the drinking water and wastewater sector , 2018, Journal of Cleaner Production.

[41]  Yiwen Bian,et al.  Efficiency evaluation for regional urban water use and wastewater decontamination systems in China: A DEA approach , 2014 .