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Disaster Risk Reduction and Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: 25 May 2026 | Viewed by 404

Special Issue Editors


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Guest Editor
Mechanical Engineering Department, University of Canterbury, Christchurch 8041, New Zealand
Interests: resilience to catastrophes; resilient food; global catastrophic infrastructure loss resilience

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Guest Editor
Department of Electrical & Computer Engineering, Western University, London, ON N6A 3K7, Canada
Interests: solar photovoltaics; appropriate technology; distributed recycling and additive manufacturing; open hardware, resilient food
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
Interests: demand-side management; demand response; energy policy; just transition; energy access; energy security

Special Issue Information

Dear Colleagues,

Disasters, ranging from local disruptions caused by earthquakes to global disruptions caused by pandemics and other global catastrophes, threaten the continued operation of global systems. Such disasters directly affect many of the Sustainable Development Goals, including in the areas of poverty, hunger, health, education, gender equality, clean water and sanitation, energy, economic growth, inequalities, and peace. Accordingly, long-term sustainability, and progress towards the Sustainable Development Goals requires the ability to prevent and adapt to such disasters.

This Special Issue aims to create a bridge between researchers in the communities of disaster risk reduction and sustainability, supporting a transdisciplinary dialogue to improve the security, resilience, and long-term sustainability of global systems.

Both original research papers and reviews are welcome in this Special Issue, and research themes may include, but are not limited to, the following topics:

  • Disaster prevention, including climate change mitigation, resilient food sources, climate resilient agriculture systems, and climate resilient water systems;
  • Disaster response and recovery, including post-disaster coordination and short- and long-term emergency planning;
  • Disaster adaptation, including urban resilience, sustainable infrastructure, climate change adaptation, and policies for resilience to global catastrophic risks;
  • Energy security, including resilience to fuel shocks and supply chain disruptions.

We look forward to receiving your contributions.

Dr. David Denkenberger
Prof. Dr. Joshua M. Pearce
Dr. Baxter Kamana-Williams
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • disaster prevention
  • disaster resilience
  • sustainability
  • energy security
  • global catastrophic risk

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Published Papers (1 paper)

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Research

23 pages, 5501 KB  
Article
Development of a Road Surface Conditions Prediction Model for Snow Removal Decision-Making
by Gyeonghoon Ma, Min-Cheol Park, Junchul Kim, Han Jin Oh and Jin-Hoon Jeong
Sustainability 2025, 17(19), 8794; https://doi.org/10.3390/su17198794 - 30 Sep 2025
Abstract
Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and [...] Read more.
Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks. Full article
(This article belongs to the Special Issue Disaster Risk Reduction and Sustainability)
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