RT info:eu-repo/semantics/article
T1 Assesing the influence of environmental variables on the performance of water companies: An efficiency analysis tree approach
A1 Molinos Senante, María
A1 Maziotis, Alexandros
A1 Sala Garrido, Ramón
A1 Mocholi Arce, Manuel
K1 Efficiency analysis
K1 Análisis de eficiencia
K1 Water utilities
K1 Servicios de aguas
K1 Environmental variables
K1 Variables ambientales
AB 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.
PB Elsevier
SN 0957-4174
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/55533
UL https://uvadoc.uva.es/handle/10324/55533
LA eng
NO Expert Systems with Applications, Volume 212, 2023, 118844
NO Producción Científica
DS UVaDOC
RD 28-nov-2024