sustainability-logo

Journal Browser

Journal Browser

Green Transportation and Collaborative Logistics Management Driven by Artificial Intelligence

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4636

Editors


E-Mail Website
Guest Editor
School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
Interests: logistics; transportation; intelligent algorithms
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Interests: transportation planning and management; transport network optimization models and algorithms; urban multi-modal transportation systems; transport network carrying capacity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In view of current global environmental challenges, the introduction of measures for climate change control is highly encouraged. Governments and local companies have recently concentrated on green transportation and logistics management driven by artificial intelligence, since it constitutes one of the fastest growing carbon dioxide emission sources. The convenience of online shopping platforms and the continuous growth of the world’s commodity consumption rate are essential driving forces for green transportation and logistics management. The diversification and personalization of customer demands constitute emerging challenges that affect the design of intelligent logistics transportation systems. Multi-echelon and complex logistics networks regularly impose hard-to-predict and variable challenges to the resource configuration and planning of green supply chain and logistics operations. Artificial intelligence technology and collaborative logistics network design have been advocated as proactive and complementary strategies and can contribute to reducing cross-regional transportation, CO2 emissions, energy consumption, and transportation capacity configuration, and improving the efficient utilization of green transportation and logistics network resources.

In this context, we encourage the innovative study of green transportation and logistics management driven by artificial intelligence (e.g., green logistics network design, green supply chain design, green and intelligent transportation, recycling, and intelligent logistics). We particularly welcome studies that pay attention to operation model innovation, intelligent algorithm design, and collaborative mechanism innovation related to green transportation and logistics management. Additionally, we also encourage interdisciplinary studies, especially those related to AI, generative artificial intelligence, ChatGPT, DeepSeek, Internet of Things, big data, cloud computing, and blockchain for resource sharing, workload balance, synchronization degree, location-routing panning, intelligent logistics network modeling, and optimization of the complex green supply chain and logistics networks.

In this context, the objective of this Special Issue is to explore and advance the latest achievements in green transportation and logistics management driven by artificial intelligence. We invite researchers and experts worldwide to submit high-quality innovative research papers and critical review articles on topics including but not limited to those below.

    (1) Green transportation;
    (2) Green logistics network optimization;
    (3) Eco-logistics;
    (4) Sustainable operation management;
    (5) Logistics schemes and performance evaluation;
    (6) Green recycling logistics;
    (7) Resource-sharing modes and strategies;
    (8) Collaborative mechanisms (i.e., how to facilitate collaboration among transport entities) with AI in logistics network operations;
    (9) Integrated intelligent logistics network design and optimization;
    (10) Logistics network design with intelligent transportation and workload balance;
    (11) Multiperiod resource configuration in the time–space logistics network;
    (12) Operations management and optimization in intelligent logistics systems;
    (13) Location-routing planning in intelligent transportation network design;
    (14) Vehicle routing and scheduling with dynamic customer demands;
    (15) Application of intelligent algorithms for solving logistics network modeling;
    (16) Application of emerging technologies in intelligent logistics network design.

Prof. Dr. Yong Wang
Dr. Muqing Du
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-anonymized 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

  • green transportation
  • logistics network design
  • resource sharing
  • operations management
  • recycling logistics
  • vehicle routing problem
  • artificial intelligence
  • intelligent algorithm

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

42 pages, 5887 KB  
Article
Green Infrastructure Investment and Urban Industrial Chain Resilience: Evidence from Chinese Prefecture-Level Cities
by Shuangyang Zhai, Yilin Wang, Ji Wang and Yuanhe Du
Sustainability 2026, 18(16), 8507; https://doi.org/10.3390/su18168507 - 19 Aug 2026
Abstract
Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is [...] Read more.
Against the background of global production-network restructuring, low-carbon transition, and rising external uncertainty, this study examines the effect of green infrastructure investment on urban industrial chain resilience. Using panel data for 285 Chinese prefecture-level cities from 2012 to 2024, industrial chain resilience is measured from the dimensions of industrial diversification and urban innovation capacity. Double machine learning is employed for baseline estimation, supplemented by mediation analysis, threshold regression, spatial econometric analysis, and a series of robustness tests. The results show that green infrastructure investment significantly enhances industrial chain resilience, and the finding remains robust to alternative model specifications, cross-fitting settings, generalized propensity score weighting, continuous-treatment entropy balancing, winsorization, and the exclusion of pandemic-period observations. Resource allocation efficiency plays a partial mediating role in this relationship. The threshold analysis identifies a significant nonlinear effect associated with energy consumption intensity, with the positive effect of green infrastructure investment being stronger below the estimated threshold and weakening above it. Spatial analysis further shows significant spatial dependence in both green infrastructure investment and industrial chain resilience, together with positive spillover effects on neighboring cities. These findings highlight the importance of improving green infrastructure investment efficiency, strengthening factor allocation, and promoting regional coordination in enhancing urban industrial chain resilience. Full article
Show Figures

Figure 1

29 pages, 3014 KB  
Article
Green Finance, Infrastructure Upgrading, and Urban Supply Chain Resilience: Evidence from China’s Green Finance Reform and Innovation Pilot Zones
by Yilin Wang, Xujing Dai and Xueyan Li
Sustainability 2026, 18(15), 7895; https://doi.org/10.3390/su18157895 - 4 Aug 2026
Viewed by 323
Abstract
The simultaneous pressures of low-carbon transition and repeated supply chain disruptions have made urban supply chain resilience an important concern for economic security and sustainable development. This study examines whether China’s Green Finance Reform and Innovation Pilot Zones (GFRIZ) policy, launched in 2017, [...] Read more.
The simultaneous pressures of low-carbon transition and repeated supply chain disruptions have made urban supply chain resilience an important concern for economic security and sustainable development. This study examines whether China’s Green Finance Reform and Innovation Pilot Zones (GFRIZ) policy, launched in 2017, improved the resilience of urban supply chains. Using panel data for 285 prefecture-level cities from 2012 to 2024, we combine a partially linear Double Machine Learning framework with spatial econometric analysis. To address concerns about construct validity, urban supply chain resilience is reconstructed as a composite index covering four dimensions: resistance, innovation, coordination, and recovery. These dimensions are measured by industrial diversification, invention patent authorization, industrial product sales rate, and supply chain fluidity, respectively. The results show that the GFRIZ policy significantly enhances urban supply chain resilience. This conclusion remains robust after alternative machine learning estimators, different cross-fitting folds, interactive fixed effects, winsorization, exclusion of the COVID-19 year, and placebo tests. Mechanism analysis suggests that infrastructure upgrading remains an important transmission channel, especially through digital, green, and transport infrastructure. The effect is stronger in eastern and central cities, large cities, transport hubs, and old industrial bases. Spatial analysis further shows that although urban supply chain resilience displays negative spatial autocorrelation, the GFRIZ policy generates positive spillover effects on neighboring cities through capital flows, industrial linkages, and knowledge diffusion. The findings provide evidence that green finance reform can strengthen urban economic resilience beyond its environmental objectives. Full article
Show Figures

Figure 1

30 pages, 2960 KB  
Article
Dynamic Pricing for Wireless Charging Lane Management Based on Deep Reinforcement Learning
by Fan Liu, Zhen Tan and Hing Kai Chan
Sustainability 2025, 17(21), 9831; https://doi.org/10.3390/su17219831 - 4 Nov 2025
Cited by 1 | Viewed by 1303
Abstract
We consider a dynamic pricing problem in a double-lane system consisting of one general purpose lane and one wireless charging lane (WCL). The electricity price is dynamically adjusted to affect the lane-choice behaviors of incoming electric vehicles (EVs), thereby regulating the traffic assignment [...] Read more.
We consider a dynamic pricing problem in a double-lane system consisting of one general purpose lane and one wireless charging lane (WCL). The electricity price is dynamically adjusted to affect the lane-choice behaviors of incoming electric vehicles (EVs), thereby regulating the traffic assignment between the two lanes with both traffic operation efficiency and charging service efficiency considered in the control objective. We first establish an agent-based dynamic double-lane traffic system model, whereby each EV acts as an agent with distinct behavioral and operational characteristics. Then, a deep Q-learning algorithm is proposed to derive the optimal pricing decisions. A regression tree (CART) algorithm is also designed for benchmarking. The simulation results reveal that the deep Q-learning algorithm demonstrates superior capability in optimizing dynamic pricing strategies compared to CART by more effectively leveraging system dynamics and future traffic demand information, and both outperform the static pricing strategy. This study serves as a pioneering work to explore dynamic pricing issues for WCLs. Full article
Show Figures

Figure 1

30 pages, 2371 KB  
Article
Optimization of Joint Distribution Routes for Automotive Parts Considering Multi-Manufacturer Collaboration
by Lingsan Dong, Jian Wang and Xiaowei Hu
Sustainability 2025, 17(14), 6615; https://doi.org/10.3390/su17146615 - 19 Jul 2025
Cited by 4 | Viewed by 2288
Abstract
The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production [...] Read more.
The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article
Show Figures

Figure 1

Back to TopTop