DOI: 10.1016/j.scitotenv.2019.136014
论文题名: Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda
作者: Requejo-Castro D. ; Giné-Garriga R. ; Pérez-Foguet A.
刊名: Science of the Total Environment
ISSN: 489697
出版年: 2020
卷: 710 语种: 英语
英文关键词: Bayesian networks
; Data-driven
; Interlinkages
; SDG 6
; Sustainable Development Goals
Scopus关键词: Planning
; Sustainable development
; Added values
; Data driven
; Data-driven approach
; Interlinkages
; Monitoring purpose
; Network modelling
; Sample sizes
; SDG 6
; Bayesian networks
; drinking water
; Bayesian analysis
; data interpretation
; monitoring
; sanitation
; sustainable development
; water management
; algorithm
; Article
; Bayesian network
; environmental change
; environmental decision making
; environmental impact
; environmental management
; environmental monitoring
; environmental planning
; environmental sanitation
; environmental sustainability
; human
; information processing
; priority journal
; program sustainability
; validation study
; water quality
; water stress
; water supply
英文摘要: The Sustainable Development Goals (SDGs) are presented as integrated and indivisible. Therefore, for monitoring purposes, conventional indicator-based frameworks need to be combined with approaches that capture and describe the links and interdependencies between the Goals and their targets. In this study, we propose a data-driven Bayesian network (BN) approach to identify and interpret SDGs interlinkages. We focus our analysis on the interlinkages of SDG 6, related to water and sanitation, across the whole 2030 Agenda, using SDG global available data corresponding to 179 countries, 16 goals, 28 targets and 44 indicators. To analyze and validate the BN results, we first demonstrate the robustness of the BN approach in identifying indicator relationships (i.e. consistent results throughout different country sample sizes). Second, we show the coherency of the results by comparing them with an exhaustive study developed by UN-Water. As an added value, our data-driven approach provides further interlinkages, which are contrasted against the existing literature. We conclude that the approach adopted is useful to accommodate a thorough analysis and interpretation of the complexities and interdependencies of the SDGs. © 2019 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158641
Appears in Collections: 气候变化与战略
There are no files associated with this item.
作者单位: Engineering Sciences and Global Development (EScGD), Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya · BarcelonaTech (UPC), Jordi Girona, 1-3, Barcelona, 08034, Spain; Stockholm International Water Institute (SIWI), Linnégatan 87A, Stockholm, 100 55, Sweden
Recommended Citation:
Requejo-Castro D.,Giné-Garriga R.,Pérez-Foguet A.. Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda[J]. Science of the Total Environment,2020-01-01,710