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Sustainable Cold Chain Packaging: Passive Solutions and EPS Alternatives for Thermal Integrity

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1066

Special Issue Editors

Dr. Stefano Landini

E-Mail Website
Guest Editor
School of Engineering, Mathematics and Physics, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
Interests: electrical and thermal energy systems and storage integration; renewable energy technologies; heat transfer; thermal management; phase change cooling; energy/exergy/economic analysis and optimisation of energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Anindita Roy

E-Mail Website
Guest Editor
Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India
Interests: modelling and simulation of renewable energy systems; small wind turbines; thermal and electric energy storage; renewable hydrogen production; life degradation of lithium-ion batteries; photovoltaics; energy engineering; distributed generation
Dr. Gordhan Das Valasai

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67450, Pakistan
Interests: renewable energy systems; energy modelling and optimisation; decarbonisation and sustainability; energy policy and planning; rural electrification and social impact; hydrogen energy modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ensuring the thermal integrity of perishable goods during transport is vital for food supply chains, as it impacts food security, public health, and waste reduction. Expanded polystyrene (EPS) currently dominates cold chain packaging for its affordability and basic insulation properties, yet it has considerable drawbacks, such as contributing up to 80% of marine litter, occupying large landfill volumes, and adding to microplastics due to its non-biodegradable, single-use, and fossil fuel-based nature.

EPS’s insulating capacity rarely maintains the required 2–8 °C temperature range for more than 24–36 hours under variable ambient conditions. Conventional mitigation, ice packs, dry ice, and rapid shipping create operational complexity, energy demand, and risk of cold chain failure. While incremental attempts to improve EPS using coatings or composites defer rather than solve fundamental environmental and technical problems, the cold chain’s carbon footprint, already significant, continues to rise, challenging net-zero ambitions. Global bans and regulatory action are starting to drive replacement with more sustainable materials.

There is an urgent need for passive cold storage packaging solutions that do not rely on energy-intensive, active cooling strategies. Innovations focused on insulation and thermal management materials, design for recyclability or biodegradability, and the reduction of resource consumption throughout the cold chain are especially important for real-world impact.

This Special Issue invites contributions addressing the technical, environmental, and system-level aspects of developing and implementing passive energy storage techniques in cold chain packaging solutions. The focus is on alternatives to EPS and other single-use plastics that reduce reliance on powered refrigeration, aligning with the goals of sustainable materials, process efficiency, and net-zero cold chain operations.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Novel insulation and phase change materials (PCMs) for passive temperature regulation;
  • Integration of bio-based and recyclable materials in cold chain logistics;
  • Biomimetic and bio-inspired approaches to passive thermal regulation;
  • Life cycle and techno-economic analysis of passive cold chain solutions;
  • Policy, standards, and industrial implementation pathways for EPS-free cold chains;
  • Smart and data-driven passive packaging for food logistics.

We encourage submissions that advance the field of passive cold chain packaging, helping deliver sustainable, scalable solutions that reduce dependence on active cooling and minimize environmental footprint. For submission details and the peer review process, please refer to the journal’s editorial guidelines.

We look forward to receiving your contributions.

Dr. Stefano Landini
Dr. Anindita Roy
Dr. Gordhan Das Valasai
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

  • passive thermal packaging
  • EPS alternatives
  • cold chain sustainability
  • phase change materials
  • circular economy
  • biodegradable materials
  • sustainable logistics
  • renewable-based insulation
  • passive thermal packaging of battery packs

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Published Papers (1 paper)

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24 pages, 3245 KB  
Article
Experimental Data-Driven Machine Learning Analysis for Prediction of PCM Charging and Discharging Behavior in Portable Cold Storage Systems
by Raju R. Yenare, Chandrakant Sonawane, Anindita Roy and Stefano Landini
Sustainability 2026, 18(3), 1467; https://doi.org/10.3390/su18031467 - 2 Feb 2026
Viewed by 626
Abstract
The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, [...] Read more.
The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R2, MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R2 values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R2) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article
(This article belongs to the Special Issue Sustainable Cold Chain Packaging: Passive Solutions and EPS Alternatives for Thermal Integrity)
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