{"title":"Enhancing the explanation of household water consumption through the water-energy nexus concept","authors":"Zonghan Li, Chunyan Wang, Yi Liu, Jiangshan Wang","doi":"10.1038/s41545-024-00298-6","DOIUrl":null,"url":null,"abstract":"Estimating household water consumption can facilitate infrastructure management and municipal planning. The relatively low explanatory power of household water consumption, although it has been extensively explored based on various techniques and assumptions regarding influencing features, has the potential to be enhanced based on the water-energy nexus concept. This study attempts to explain household water consumption by establishing estimation models, incorporating energy-related features as inputs and providing strong evidence of the need to consider the water-energy nexus to explain water consumption. Traditional statistical (OLS) and machine learning techniques (random forest and XGBoost) are employed using a sample of 1320 households in Beijing, China. The results demonstrate that the inclusion of energy-related features increases the coefficient of determination (R2) by 34.0% on average. XGBoost performs the best among the three techniques. Energy-related features exhibit higher explanatory power and importance than water-related features. These findings provide a feasible modelling basis and can help better understand the household water-energy nexus.","PeriodicalId":19375,"journal":{"name":"npj Clean Water","volume":null,"pages":null},"PeriodicalIF":10.4000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41545-024-00298-6.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Clean Water","FirstCategoryId":"5","ListUrlMain":"https://www.nature.com/articles/s41545-024-00298-6","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Estimating household water consumption can facilitate infrastructure management and municipal planning. The relatively low explanatory power of household water consumption, although it has been extensively explored based on various techniques and assumptions regarding influencing features, has the potential to be enhanced based on the water-energy nexus concept. This study attempts to explain household water consumption by establishing estimation models, incorporating energy-related features as inputs and providing strong evidence of the need to consider the water-energy nexus to explain water consumption. Traditional statistical (OLS) and machine learning techniques (random forest and XGBoost) are employed using a sample of 1320 households in Beijing, China. The results demonstrate that the inclusion of energy-related features increases the coefficient of determination (R2) by 34.0% on average. XGBoost performs the best among the three techniques. Energy-related features exhibit higher explanatory power and importance than water-related features. These findings provide a feasible modelling basis and can help better understand the household water-energy nexus.
npj Clean WaterEnvironmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍:
npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.