Assesing the influence of environmental variables on the performance of water companies: An efficiency analysis tree approach. (February 2023)

• Record Type: Journal Article
• Authors: Molinos-Senante, María; Maziotis, Alexandros; Sala-Garrido, Ramon; Mocholi-Arce, Manuel
• Is Part Of: Expert systems with applications. Volume 212(2023)
• Publication Date: 2023-02
• Subjects: Regression trees -- Efficiency analysis -- Performance -- Water utilities -- Environmental variables -- Water services; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118844
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Overfitting issues were overcome in efficiency assessment by using efficiency analysis trees. Average efficiency of water companies over 1991–2000 was 0.489. Environmental variables influencing performance of water companies were identified. Abstract: Efficiency assessment is a valuable tool for industries that are regulated, such as the provision of drinking water. Hence, past research on this topic is wide. However, current, widely used approaches such as parametric, non-parametric and partial frontier methods present several limitations and pitfalls. Thus, here, the Efficiency Analysis Tree (EAT) method was trialled on a sample of water companies. This method overcomes overfitting issues, because it employs a combination of classification, regression tree methods, and non-parametric analyses. For comparative purposes, efficiency was also estimated using Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH) non-parametric methods. The approach was applied empirically using a sample of English and Welsh water companies during 1991–2020. Average efficiency was estimated at 0.489, showing that water companies could save 51.1% of their costs if efficient. Except for the 2011–2015 period, efficiency increased over time, indicating that price reviews by the English and Welsh water regulator contributed to improving water company performance. The application of bootstrap regression analysis techniques showed that the main source of raw water, percentage of metered properties, population density, and percentage of water leakage represented environmental variables that significantly influenced the efficiency scores of water companies. The approach introduced here could be of use to water regulators, as it overcomes the existing limitations of traditional approaches employed to assess the performance of water companies, facilitating sound decision-making.