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Sustainable Operations and Green Supply Chain
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Sustainable Operations and Green Supply Chain

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 4108

Special Issue Editor

Prof. Dr. Fouad Jawab

E-Mail Website
Guest Editor
High School of Technology, Sidi Mohammed Ben Abdellah University, Fez 30000, Morocco
Interests: supply chain; industrial engineering; healthcare and hospital logistics; urban freight transport; modelling; transport and mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The general orientation of this Special Issue is to explore in depth the different facets of the transition towards environmentally friendly, socially just and equitable, and economically viable value chains.

In terms of scope, this Special Issue seeks to address the challenges of sustainability in operations, covering a broad spectrum of sectors (manufacturing, agri-food, logistics, etc.) at all levels from supply to reverse logistics, including production and distribution. It aims at exploring the multiple dimensions of sustainability at different scales, local, regional and global.

This Special Issue also aims at focusing on the assessment of environmental and social impacts, by analyzing the methodologies, indicators and tools used. It also aims at examining the levers of action to improve the environmental and social performance of companies, such as certifications (ISO 14001, Ecolabel, etc.), collaboration between chain actors, and the influence of public policies (regulation, taxation, standards, etc.).

Its objective is in line with the cutting-edge work published in the journal Sustainability.

On the theoretical level, this Special Issue aims at taking stock of the most recent research and proposing new conceptual frameworks as well as innovative methodological approaches to analyze and evaluate the multiple facets of sustainability within the supply chain.

On the practical level, this Special Issue seeks to highlight the best practices of economic, political, and social actors in sustainable operations, by presenting concrete tools and case studies from companies in different sectors. It also aims at exploring possible synergies between the different actors in the chain to succeed in this transformation and to present the latest trends and future challenges of the transition to sustainable supply chains.

Prof. Dr. Fouad Jawab
Guest Editor

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

  • sustainability
  • supply chain
  • operations
  • performance
  • collaboration
  • environmental impact
  • social impact
  • theory
  • best practices

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Published Papers (4 papers)

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Research

32 pages, 3312 KB  
Article
Green Investment and Emission Reduction in Supply Chains Under Dual-Carbon Regulation: A Dynamic Game Perspective on Coordination Mechanisms and Policy Insights
by Dandan Wu, Kun Li and Yang Cheng
Sustainability 2025, 17(19), 8951; https://doi.org/10.3390/su17198951 - 9 Oct 2025
Viewed by 467
Abstract
This study examines green investment and emission reduction strategies in a two-tier supply chain under dual-carbon regulation that combines a carbon tax with a cap-and-trade mechanism. A multi-stage dynamic game model is developed, in which the manufacturer reduces emissions through recycling efforts and [...] Read more.
This study examines green investment and emission reduction strategies in a two-tier supply chain under dual-carbon regulation that combines a carbon tax with a cap-and-trade mechanism. A multi-stage dynamic game model is developed, in which the manufacturer reduces emissions through recycling efforts and investments in green technology. We compare optimal decisions under centralized, decentralized, and coordinated structures, and propose an enhanced bilateral cost-sharing contract to improve collaboration. Numerical experiments validate the theoretical results, and sensitivity analyses provide further insights. The results show that while both carbon tax and permit trading increase emission reduction, the carbon tax may lower manufacturer profit, underscoring the need for coordinated policy design. Benchmarking proves more effective than grandfathering in stimulating green investment, particularly under high carbon prices and strong consumer environmental preferences. The proposed contract alleviates free riding, enhances overall supply chain profitability, and improves emission reduction performance. Policy implications highlight the importance of prioritizing benchmark allocation, promoting consumer environmental awareness, and encouraging firms to integrate carbon asset management with technological innovation. This research provides both theoretical and practical insights for designing effective carbon policies and collaborative mechanisms in green supply chains. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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23 pages, 1162 KB  
Article
Can Green Supply Chain Management Improve Supply Chain Resilience? A Quasi-Natural Experiment from China
by Jiajing Li and Chengcheng Zhu
Sustainability 2025, 17(16), 7481; https://doi.org/10.3390/su17167481 - 19 Aug 2025
Viewed by 1493
Abstract
The supply chain is a critical tool for enterprises to withstand risks and ensure sustainable development. Integrating green and environmentally friendly practices into the supply chain has become an increasingly prominent trend. This study examines the impact of green supply chain management (GSCM) [...] Read more.
The supply chain is a critical tool for enterprises to withstand risks and ensure sustainable development. Integrating green and environmentally friendly practices into the supply chain has become an increasingly prominent trend. This study examines the impact of green supply chain management (GSCM) on supply chain resilience, using the green supply chain pilot projects implemented in China as a quasi-natural experiment, employing a multi-period difference-in-difference (DID) model. Based on panel data from manufacturing enterprises listed on the A-share market in China from 2014 to 2022, the findings reveal three key insights. First, GSCM significantly improves the resilience of enterprise supply chains. Second, GSCM has both signaling and cost effects, as it can reduce corporate financing costs and enhance market value, lower market transaction costs, and improve productivity. These are potential channels through which GSCM exerts a positive influence. Third, the positive impact of GSCM on supply chain resilience is more pronounced in enterprises with third-party environmental certifications and higher institutional shareholder ratios. Additionally, this study also extends to demonstrate that GSCM directly and positively influences corporate environmental performance. These findings provide policy recommendations for enhancing green supply chain development and offer managerial insights to help enterprises proactively embrace green transformation. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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41 pages, 883 KB  
Article
Dependent-Chance Goal Programming for Sustainable Supply Chain Design: A Reinforcement Learning-Enhanced Salp Swarm Approach
by Yassine Boutmir, Rachid Bannari, Achraf Touil, Mouhsene Fri and Othmane Benmoussa
Sustainability 2025, 17(13), 6079; https://doi.org/10.3390/su17136079 - 2 Jul 2025
Viewed by 555
Abstract
The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration [...] Read more.
The Sustainable Supply Chain Network Design Problem (SSCNDP) is to determine the optimal network configuration and resource allocation that achieve the trade-off among economic, environmental, social, and resilience objectives. The Sustainable Supply Chain Network Design Problem (SSCNDP) involves determining the optimal network configuration and resource allocation that allows trade-off among economic, environmental, social, and resilience objectives. This paper addresses the SSCNDP under hybrid uncertainty, which combines objective randomness got from historical data, and subjective beliefs induced by expert judgment. Building on chance theory, we formulate a dependent-chance goal programming model that specifies target probability levels for achieving sustainability objectives and minimizes deviations from these targets using a lexicographic approach. To solve this complex optimization problem, we develop a hybrid intelligent algorithm that combines uncertain random simulation with Reinforcement Learning-enhanced Salp Swarm Optimization (RL-SSO). The proposed RL-SSO algorithm is benchmarked against standard metaheuristics—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and standard SSO, across diverse problem instances. Results show that our method consistently outperforms these techniques in both solution quality and computational efficiency. The paper concludes with managerial insights and discusses limitations and future research directions. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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34 pages, 1710 KB  
Article
Logistics Sprawl and Urban Congestion Dynamics Toward Sustainability: A Logistic Regression and Random-Forest-Based Model
by Manal El Yadari, Fouad Jawab, Imane Moufad and Jabir Arif
Sustainability 2025, 17(13), 5929; https://doi.org/10.3390/su17135929 - 27 Jun 2025
Viewed by 1064
Abstract
Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several [...] Read more.
Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article
(This article belongs to the Special Issue Sustainable Operations and Green Supply Chain)
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