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Data-Driven Approaches and Decision Support Tools for Sustainable and Resilient Infrastructure Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 2675

Special Issue Editors

Dr. Weiwei Mo

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Interests: sustainable and resilient infrastructure; infrastructure–human–resource interdependency; water–energy nexus; complex systems modeling; life cycle assessment; system dynamics modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Fei Han

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH 03824, USA
Interests: machine learning; artificial intelligence; urban planning; system modeling and optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite contributions to a forthcoming Special Issue focused on “Data-Driven Approaches and Decision Support Tools for Sustainable and Resilient Infrastructure Systems”. As infrastructure systems face growing pressures from climate change, aging assets, and evolving societal needs, the integration of data analytics and computational tools is becoming increasingly critical for enabling adaptive, informed, and future-ready decision-making.

This Special Issue will emphasize systems thinking and interdisciplinary modeling approaches in the development and application of data analytics and decision support tools for sustainable and resilient infrastructure systems. Topics of interest include systems-based predictive analytics, digital twins, scenario analysis, risk and resilience modelling, AI integration, and participatory decision-making under uncertainty. The collection aims to bridge disciplinary boundaries and connect theoretical advances with practical implementation. By foregrounding systems-oriented, data-driven frameworks, this Special Issue will highlight how interdisciplinary tools and methods can guide infrastructure design and management toward more adaptive, equitable, and sustainable outcomes in the face of evolving environmental, social, and technological challenges. 

We welcome interdisciplinary research, case studies, and methodological advancements that push the boundaries of how data is used to guide infrastructure decisions.

Dr. Weiwei Mo
Dr. Fei Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systems thinking
  • sustainable and resilient infrastructure systems
  • decision support systems
  • interdisciplinary approaches
  • data analytics, scenario analysis
  • digital twinning
  • artificial intelligence
  • machine learning
  • uncertainty and risk assessment
  • participatory modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

26 pages, 1009 KB  
Article
Quantifying GHG Emissions of Korean Domestic Tourism: Spend-Based Multiregional EEIO Approach to Category 6 of Scope 3
by Dasom Jeong, ChangKeun Park, Yongbin Lee, Soomin Park and JiYoung Park
Sustainability 2025, 17(22), 10174; https://doi.org/10.3390/su172210174 - 13 Nov 2025
Cited by 1 | Viewed by 863
Abstract
Tourism is a fast-growing sector that generates a significant greenhouse gas (GHG) footprint, yet subnational data needed to measure the sector remain scarce. Quantifying tourism-related emissions is essential for effective climate policy and alignment with international targets. This study contributes to quantifying tourism [...] Read more.
Tourism is a fast-growing sector that generates a significant greenhouse gas (GHG) footprint, yet subnational data needed to measure the sector remain scarce. Quantifying tourism-related emissions is essential for effective climate policy and alignment with international targets. This study contributes to quantifying tourism sector GHG emissions using the 2023 Korean National Travel Survey data and a spend-based environmentally extended input–output (EEIO) model. Expenditure data were mapped onto the 33-sector multiregional EEIO framework, estimating a total of 2623 tCO2eq emissions by region, expenditure type, and industry sector in 2023, where about 73% of the total was attributed to tourism-related sectors with the sample data, 24,282. The results illustrate how tourism emissions are shaped especially by transportation systems and regional context. Provinces that surround metropolitan cities in the mainland, for example, Gyeonggi and Gangwon Provinces near Seoul and Incheon, and Gyeongnam Province neighboring Busan and Ulsan, record higher emissions due to large travel volumes from these metropolitan cities and energy-intensive transportation services. Jeju Island stands out as an outlier, with disproportionately high emissions relative to its size, driven by reliance on aviation, which significantly raises its per-visitor footprint. Sectoral analysis identified transportation services, agriculture, electricity, and gas as key sectors. By providing detailed provincial-level data, this study offers a first empirical foundation to corporate Category 6 of Scope 3 reporting and supports central and local governments in designing region-specific climate strategies associated with tourism-related sectors. Full article
(This article belongs to the Special Issue Data-Driven Approaches and Decision Support Tools for Sustainable and Resilient Infrastructure Systems)
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24 pages, 5518 KB  
Article
PropNet-R: A Custom CNN Architecture for Quantitative Estimation of Propane Gas Concentration Based on Thermal Images for Sustainable Safety Monitoring
by Luis Alberto Holgado-Apaza, Jaime Cesar Prieto-Luna, Edgar E. Carpio-Vargas, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, José Miguel Barrón-Adame, José Alfredo Aguirre-Puente, Dalmiro Ramos Enciso, Danger David Castellon-Apaza and Danny Jesus Saman-Pacamia
Sustainability 2025, 17(21), 9801; https://doi.org/10.3390/su17219801 - 3 Nov 2025
Cited by 1 | Viewed by 1296
Abstract
Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the [...] Read more.
Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R2 = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R2 = 0.397), VGG19 (MSE = 0.447, R2 = 0.280), and ResNet50 (MSE = 0.474, R2 = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems. Full article
(This article belongs to the Special Issue Data-Driven Approaches and Decision Support Tools for Sustainable and Resilient Infrastructure Systems)
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