Skip to main content
Log in

Benchmarking the Performance of Productive Units Using Cross-Efficiency Techniques: An Empirical Approach for Water Companies

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Water companies provide essential services to society, such as drinking water and sanitation. Improving the management of these companies is fundamental for evaluating their performance. This study employs cross-efficiency data envelopment analysis techniques to assess a sample of water companies during the years 2010–2018. The assessment focuses on three main topics: i) the impact of service quality on water companies' performance; ii) the influence of environmental variables on efficiency and eco-efficiency in water companies and; iii) the effect of ownership on water company performance. The results reveal that service quality significantly influences water companies' performance. Additionally, it demonstrates that customer density and ownership of water companies impact economic and environmental efficiency, with public water companies showing the best performance. However, the quality of service of public water companies had deteriorated over time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Data Availability

Data will be available upon a reasonable request.

References

  • Aparicio J, Ortiz L, Pastor JT, Zabala-Iturriagagoitia JM (2020) Introducing cross-productivity: A new approach for ranking productive units over time in Data Envelopment Analysis. Comput Ind Eng 144:106456

    Article  Google Scholar 

  • Bayaraa B, Tarnoczi T, Fenyves V (2019) Measuring performance by integrating k-medoids with DEA: Mongolian case. J Bus Econ Manag 20(6):1238–1257

    Article  Google Scholar 

  • Ben Amor T, Mellah T (2023) Cost efficiency of Tunisian water utility districts: Does heterogeneity matter? Utilities Policy 84:101616

    Article  Google Scholar 

  • Bevilacqua M, Ciarapica FE, Mazzuto G, Paciarotti C (2015) Efficiency assessment of blanching and deep-freezing systems through data envelopment analysis. Eng Agric Environ Food 1–6

  • Brea-Solis H, Perelman S, Saal DS (2017) Regulatory incentives to water losses reduction: the case of England and Wales. J Prod Anal 47(3):259–276

    Article  Google Scholar 

  • Carvalho P, Marques RC, Berg S (2012) A meta-regression analysis of benchmarking studies on water utilities market structure. Utilities Policy 21:40–49

    Article  Google Scholar 

  • Cetrulo TB, Marques RC, Malheiros TF (2019) An analytical review of the efficiency of water and sanitation utilities in developing countries. Water Res 161:372–380

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Article  Google Scholar 

  • Cinaroglou S (2020) Integrated k-means clustering with data envelopment analysis of public hospital efficiency. Health Care Manag Sci 23:325–338

    Article  Google Scholar 

  • Cui Q, Li Y (2020) A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency. Transp Policy 99:31–43

    Article  Google Scholar 

  • Ding ZY, Jo GS, Wang Y, Yeo GT (2015) The relative efficiency of container terminals in small and medium-sized ports in China. Asian J Ship Log 31(2):231–251

    Article  Google Scholar 

  • Doyle J, Green RH (1994) Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. J Oper Res Soc 45(5):567–578

    Article  Google Scholar 

  • Emrouznejad A, Yang G (2018) A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016. Socioecon Plann Sci 61(1):1–5

    Google Scholar 

  • Falagario M, Sciancalepore F, Costantino N, Pietroforte R (2012) Using a DEA-cross efficiency approach in public procurement tenders. Eur J Oper Res 218:523–529

    Article  Google Scholar 

  • Ferro G, Mercadier AC (2016) Technical efficiency in Chile’s water and sanitation provides. Utilities Policy 43:97–106

    Article  Google Scholar 

  • Goh KH, See KF (2021) Twenty years of water utility benchmarking: A bibliometric analysis of emerging interest in water research and collaboration. J Clean Prod 284:124711

    Article  Google Scholar 

  • Jiang Z, Ding Z, Zhang H, Caic W, Liu Y (2019) Data-driven ecological performance evaluation for remanufacturing process. Energy Convers Manag 198:111844

    Article  Google Scholar 

  • Liang L, Wu J, Cook WD, Zhu J (2008) Alternative secondary goals in DEA cross-efficiency evaluation. J Clean Prod 113(2):1025–1030

    Google Scholar 

  • Liu X, Chu J, Yin P, Sun J (2017) DEA cross-efficiency evaluation considering undesirable output and ranking priority: a case study of eco-efficiency analysis of coal-fired power plants. J Clean Prod 142:877–885

    Article  Google Scholar 

  • López-Ruiz S, Ibáñez-Rueda N, Guardiola J, González-Gómez F (2023) Does the ownership of water utilities influence water-saving advice provided to service users? An Analysis of the Spanish water sector. Water Resour Manag 37(8):3299–3318

    Article  Google Scholar 

  • Marques RC, Simões P (2020) Revisiting the comparison of public and private water service provision: An empirical study in Portugal. Water (Switzerland) 12(5):1477

    Google Scholar 

  • Maziotis A, Villegas A, Molinos-Senante M, Sala-Garrido R (2020) Impact of external costs of unplanned interruptions on water company efficiency: Evidence from Chile. Util Policy 66:101087

    Article  Google Scholar 

  • Mellah T, Ben Amor T (2016) Performance of the Tunisian Water Utility: An input distance function approach. Util Policy 38:18–32

    Article  Google Scholar 

  • Moeini M, Karimi B, Khorram E (2015) A cross-efficiency approach for evaluating decision making units in presence of undesirable outputs. In H.A. Le Thi et al. (eds.), Model Comput Optim Inf Syst Manag Sci Adv Intell Syst Comput 360

  • Molinos-Senante M, Donoso G, Sala-Garrido R, Villegas A (2018a) Benchmarking the efficiency of the Chilean water and sewerage companies: a double-bootstrap approach. Environ Sci Pollut Res 25:8432–8440

    Article  Google Scholar 

  • Molinos-Senante M, Porcher S, Maziotis A (2018b) Productivity change and its drivers for the Chilean water companies: A comparison of full private and concessionary companies. J Clean Prod 183:908–916

    Article  Google Scholar 

  • Molinos-Senante M, Sala-Garrido R (2015) The impact of privatization approaches on the productivity growth of the water industry: A case study of Chile. Environ Sci Policy 50:166–179

    Article  Google Scholar 

  • Molinos-Senante M, Villegas A, Maziotis A (2019) Are water tariffs sufficient incentives to reduce water leakages? An empirical approach for Chile. Util Policy 61:100971

    Article  Google Scholar 

  • Moosavian SF, Borzuei D, Ahmadi A (2022) Cost analysis of water quality assessment using multi-criteria decision-making approach. Water Resour Manag 36(12):4843–4862

    Article  Google Scholar 

  • Nuru H, Raji P, Manivasagam VS, Sudha A, Raj C (2023) Global sustainable water management: a systematic qualitative review. Water Resour Manag, In Press

  • Omrani H, Shafaat K, Emrouznejad A (2018) An integrated fuzzy clustering cooperative game data envelopment analysis model with application in hospital efficiency. Expert Syst Appl 114:615–628

    Article  Google Scholar 

  • Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36(2):3336–3341

    Article  Google Scholar 

  • Pinto FS, Simoes P, Marques RC (2017) Water services performance: do operational environmental and quality factors account? Urban Water J 14(8):773–781

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Sala-Garrido R, Maziotis A, Mocholi-Arce M, Molinos-Senante M (2023) Assessing eco-efficiency of wastewater treatment plants: A cross-evaluation strategy. Sci Total Environ 900:165839

    Article  Google Scholar 

  • Sala-Garrido R, Molinos-Senante M, Mocholi-Arche M (2019) Comparing changes in productivity among private water companies integrating quality of service: A metafrontier approach. J Clean Prod 216:597–606

    Article  Google Scholar 

  • Samoilenko S, Osei-Bryson K-M (2010) Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks. Eur J Oper Res Soc 206:479–487

    Article  Google Scholar 

  • Sexton TR, Silkman RH, Hogan AJ (1986) Data envelopment analysis: Critique and extensions. New Dir Prog Eval 32:73–105

    Google Scholar 

  • Sikka V, Luke RD, Ozcan YA (2009) The efficiency of hospital based clusters: evaluating system performance using data envelopment analysis. Health Care Manag Rev 34(3):251–261

    Article  Google Scholar 

  • SISS (Superintendencia de Servicios Sanitarios) (2021) Management reports about Chilean water companies. Available at: https://www.siss.gob.cl/586/w3-propertyvalue-6415.html

  • Suárez-Varela M, de los Ángeles García-Valiñas M, González-Gómez F, Picazo-Tadeo AJ (2017) Ownership and performance in water services revisited: does private management really outperform public? Water Resour Manag 31(8):2355–2373

    Article  Google Scholar 

  • Swenson ER, Bastian ND, Nembhard HB (2016) Data analytics in health promotion: health market segmentation and classification of total joint replacement surgery patients. Expert Syst Appl 60:118–129

    Article  Google Scholar 

  • Tang T, Chen S, Zhao M, Huang W, Luo J (2018) Very large-scale data classification based on K-means clustering and multi-kernel SVM. Soft Comput 23:3793–3801

    Article  Google Scholar 

  • Tavana M, Toloo M, Aghayi N, Arabmaldar A (2021) A robust cross-efficiency data envelopment analysis model with undesirable outputs. Expert Syst Appl 167:114117

    Article  Google Scholar 

  • Wang X, Lu Y, Chen C, Yi X, Cui H (2024) Total-factor energy efficiency of ten major global energy-consuming countries. J Environ Sci (china) 137:41–52

    Article  Google Scholar 

  • Wu J, Chu J, Sun J, Zhu Q, Liang L (2016) Extended secondary goal models for weights selection in DEA cross-efficiency evaluation. Comput Ind Eng 93:143–151

    Article  Google Scholar 

  • Wu J, Liang L, Song M (2010) Performance based clustering for benchmarking of container ports: An application of dea and cluster analysis technique. Int J Comput Intell Syst 3(6):709–722

    Google Scholar 

  • Wu J, Liang L, Yang F (2009) Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method. Eur J Oper Res 197:722–730

    Article  Google Scholar 

  • Xie L, Chen C, Yu Y (2019) Dynamic assessment of environmental efficiency in Chinese industry: A multiple DEA model with a gini criterion approach. Sustainability 11:2294

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ramón Sal-Garrido, Manuel Mocholi-Arce and Alexandros Maziotis. The first draft of the manuscript was written by Alexandros Maziotis and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maria Molinos-Senante.

Ethics declarations

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 5 Correlations and difference comparison between efficiency measurements

Figures 2, 3, 4 and 5

Fig. 2
figure 2

Cluster analysis – Model DO

Fig. 3
figure 3

Silhouette score – Model DO

Fig. 4
figure 4

Cluster analysis – Model DUO

Fig. 5
figure 5

Silhouette score – Model DUO

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sala-Garrido, R., Mocholi-Arce, M., Molinos-Senante, M. et al. Benchmarking the Performance of Productive Units Using Cross-Efficiency Techniques: An Empirical Approach for Water Companies. Water Resour Manage 37, 5459–5476 (2023). https://doi.org/10.1007/s11269-023-03614-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-023-03614-w

Keywords

Navigation