Critical drivers and barriers of digital twin adoption in water infrastructure: An environmental, social, governance, and financial perspective

Abstract Digital twin (DT) is increasingly recognized as a valuable instrument for improving water infrastructure efficiency. However, current research has yet to identify drivers and barriers impacting the use of DT in water infrastructure practices. Preliminary work has been undertaken regarding drivers and barriers of DT in the manufacturing, processing, and construction industries, but not in water. Therefore, this study engages two questions. First, what are the drivers and barriers to DT adoption in water infrastructure projects? Second, which of these drivers and barriers are critical? A literature review targeting an Environmental, Social, Governance, and Financial (ESG‐Financial) framework was undertaken to identify candidate drivers and barriers. Text mining not only supplements but also validates and refines the list of driving and barrier factors obtained from literature reviews. Next, a fuzzy decision‐making trial and evaluation laboratory (DEMATEL) method was utilized to find those most critical. Subsequently, a questionnaire survey provided cross‐validation of the fuzzy DEMATEL results. Finally, 11 critical drivers and 8 critical barriers were identified. Based on these findings, some policy implications are proposed, including developing the potential of DT in water environment monitoring, strengthening collaboration, improving DT model reuse, promoting DT value capture, enhancing publicity, and increasing DT education investment.

Authors

Institutions

Publication Details

Journal
Sustainable Development
Published
2024-09-23
DOI
https://doi.org/10.1002/sd.3196
Citations
5
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
1.60

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Critical drivers and barriers of digital twin adoption in water infrastructure: An environmental, social, governance, and financial perspective

Yubo Guo, Igor Martek, Xiaowei Luo, Chuan Chen
5 citations
Sustainable Development
Smart Cities and Technologies
1.60
article

Critical drivers and barriers of digital twin adoption in water infrastructure: An environmental, social, governance, and financial perspective

Yubo Guo, Igor Martek, Xiaowei Luo, Chuan Chen
article en
5 citations

Abstract

Abstract Digital twin (DT) is increasingly recognized as a valuable instrument for improving water infrastructure efficiency. However, current research has yet to identify drivers and barriers impacting the use of DT in water infrastructure practices. Preliminary work has been undertaken regarding drivers and barriers of DT in the manufacturing, processing, and construction industries, but not in water. Therefore, this study engages two questions. First, what are the drivers and barriers to DT adoption in water infrastructure projects? Second, which of these drivers and barriers are critical? A literature review targeting an Environmental, Social, Governance, and Financial (ESG‐Financial) framework was undertaken to identify candidate drivers and barriers. Text mining not only supplements but also validates and refines the list of driving and barrier factors obtained from literature reviews. Next, a fuzzy decision‐making trial and evaluation laboratory (DEMATEL) method was utilized to find those most critical. Subsequently, a questionnaire survey provided cross‐validation of the fuzzy DEMATEL results. Finally, 11 critical drivers and 8 critical barriers were identified. Based on these findings, some policy implications are proposed, including developing the potential of DT in water environment monitoring, strengthening collaboration, improving DT model reuse, promoting DT value capture, enhancing publicity, and increasing DT education investment.

Sustainable DevelopmentVol. 33(2)
Deakin University (AU), Southwest Petroleum University (CN), City University of Hong Kong (HK), Sichuan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Smart Cities and Technologies
1.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.