Assessing energy efficiency of water services and its drivers: A case study from water companies in England and Wales

被引:3
作者
Molinos-Senante, Maria [1 ]
Maziotis, Alexandros [2 ,3 ]
机构
[1] Univ Valladolid, Inst Sustainable Proc, C Mergelina S-N, Valladolid, Spain
[2] Pontificia Univ Catolica Chile, Dept Ingn Hidraul & Ambiental, Ave Vicuna Mackenna, Santiago 4860, Chile
[3] New York Coll, Dept Business, Leof Vasilisis Amalias 38, Athina 10558, Greece
关键词
Energy efficiency; Artificial neural networks; Data envelopment analysis; Operating characteristics; Water services; Water-energy nexus; ARTIFICIAL NEURAL-NETWORKS; DATA ENVELOPMENT ANALYSIS; TECHNICAL EFFICIENCY; OPERATIONAL ENVIRONMENT; SANITATION UTILITIES; EMPIRICAL-ANALYSIS; TREATMENT PLANTS; DRINKING-WATER; PERFORMANCE; DEA;
D O I
10.1016/j.jwpe.2024.105596
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Understanding how energy efficient the water services are and what drives inefficiency can greatly assist water utilities in delivering sustainable services. This study employs a neural network (NN) approach to measure the energy efficiency of water services in relation to the volume of drinking water supplied and the number of connected properties. Unlike other non-parametric approaches, NN allows capturing the complex relationships and dependencies between various factors influencing energy efficiency of water companies. An empirical application for English and Welsh water utilities embracing water only companies (WoCs) and water and sewerage companies (WaSCs) over 2008-2020 was conducted. The average energy efficiency score was found to be 0.411, indicating that water utilities could potentially save 0.54 kWh per cubic meter of drinking water supplied. Notably, WaSCs exhibited better energy performance compared to WoCs, with energy efficiency scores of 0.559 and 0.239, respectively. Nevertheless, based on the volume of water delivered, WaSCs could save 0.65 kWh/m3 whereas WoCs potential energy savings are 0.24 kWh/m3. Energy efficiency remained relatively stable across the years, with average values of 0.440, 0.388 and 0.454 for the periods 2008-2010, 2011-2015, and 2016-2020, respectively. The analysis conducted using decision tree methods highlighted the relevance of water treatment quality and the source of raw water as key variables influencing the energy efficiency of water utilities. These findings can be valuable for policymakers, enabling them to gain a deeper understanding of the driving factors behind energy efficiency in water service provision.
引用
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页数:10
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