Sustainable Transportation Systems: Integrating Network, Cultural, Quality and Decision-Making Perspectives

This editorial review presents five scientific articles published in the Special Issue “Sustainable Transportation: Logistics and Route Network Aspects”, which explores the areas of transport and logistics from different—but interrelated—perspectives.

The main feature that unites all five articles examined is a clear transition from fragmented, isolated transport system analysis to multilayered, networked and structural approaches, where the main object of assessment is not only a separate element, but the entire systemic context: nodes, flows, connections, hierarchies and their changes over time. In this way, these studies form a modern paradigm of transport analysis, in which the resilience, dynamism and sustainability of systems are examined through the intersection of infrastructural, organizational and methodological networks. This paradigmatic approach is especially clearly revealed through the topological analysis of transport networks, which becomes not only a description of the geographical or route structure, but also a fundamental method for assessing sustainability. In Park et al.’s work on the Asia-Europe container shipping network, and O’Kelly and Park’s on air transport route topologies, the network structure is treated as a direct indicator of sustainability. In both maritime and air transport, methodological justification is provided on how the level of centralization, the intensity of network intermediation and structural resilience affect the ability to maintain systemic functioning in crisis conditions—this becomes particularly important in relation to SDG 9 (“Industry, Innovation and Infrastructure”) and SDG 13 (“Fighting Climate Change”).

Park et al. used the Social Network Analysis (SNA) methodology to quantify the functional position of seaports on container transport routes, with a particular focus on Asian and European transit hubs during the pandemic. The centrality indices of Shanghai, Singapore and Rotterdam ports changed dynamically, depending on supply chain pressures, congestion or cargo reallocation between alternative ports. Of particular importance here is the betweenness centrality indicator, which determines how many times a node is on the shortest freight path trajectory between other nodes. Singapore’s high betweenness index indicates its role as a “systemic stabilizer” along the entire route—an example of how the SNA methodology becomes a tool for sustainability forecasting and infrastructure planning.

A similar methodological framework is used by O’Kelly & Park, but in their study the network structure is linked to the concept of resilience. The comparison of hub-and-spoke and point-to-point networks is presented not only as a theoretical taxonomy, but also as an empirically measurable risk index, allowing us to assess which type of networks are more vulnerable during global disruptions. One-hub structures, such as Emirates or KLM, are characterized by extremely high operational efficiency in peacetime conditions, but are very sensitive to the closure of central nodes. Meanwhile, decentralized point-to-point (P2P) models (e.g., Ryanair, Southwest) show greater flexibility, faster recovery and less systemic rupture.

This is essentially consistent with the concept of sustainability transformation, which is also promoted by the UN SDG system: it is not enough to be “green”, it is important to be adaptive and structurally strong.

In both articles, network analysis is associated with the impact of sustainability on different time horizons. In the study of maritime transport, it is seen how temporary changes in routes (e.g., refusal to call at ports due to traffic jams or quarantines) change the topology of the entire network, generating new flow axes. In air transport, on the contrary, the shape of the network determines the system’s ability to survive: the more the system depends on a small number of central nodes, the faster it loses functionality.

Sustainability analysis in the transport sector is still dominated by technical, engineering and economic indicators, but this approach is inevitably limited. Cultural factors, social relations and organizational behavior have long been considered “soft” aspects, poorly suited to systematic modeling. However, the study by Kuranović and co-authors refutes this narrative—here we present an empirically based and mathematically formalized model that demonstrates that social capital is not only quantitatively measurable, but also significant for the intensity of freight movement and logistics efficiency.

This article is unique in that guanxi—traditionally associated with Chinese business culture—is conceptualized as an analytical tool in the process of warehouse management, and not just as a cultural phenomenon. The study treats guanxi as a system of social relationships based on long-term trust, mutual commitment and adherence to unwritten norms. The authors prove that this system has a real impact on the flow of goods in warehouses, especially in cases where procedural inconsistencies or human errors have to be addressed. Kuranovič et al. applied a mixed methodology: they formulated a mathematical modeling of the intensity of cargo flows using classical logistics variables (e.g., cargo volume, storage time, pallet mass, loading density), and additionally integrated an expert assessment of the application of guanxi at specific stages of warehousing. Although at first glance these two levels of analysis (physical logistics and social relations) seem different, the study shows their interaction: when there are strong guanxi ties between employees and partners, the waiting time for goods is reduced, the speed of order processing is increased, the rate of returns is reduced, and operational adaptation under uncertainty is increased.

This analysis allows us to formulate two important theoretical insights, relevant to the assessment of the sustainability of transport systems:

Moreover, the novelty of this work lies in the fact that the cultural component is treated not as a “soft” sociological dimension, but as a quantitatively included parameter of the model. The intensity of cargo flows, the load of storage areas, the internal density of goods movement—all these factors are mathematically described, but at the same time their dependence on the quality of guanxi is assessed, based on an expert survey. This enables social capital to be assessed in the same way as, say, the dimensions of pallets or the area of a warehouse—as a component belonging to and managed by the logistics system.

Such a methodological example of synthesis shows that culture can be modeled, and not just described—this is a radical step towards expanding the concept of sustainability to the level of social systems. In other words, guanxi here becomes the “internal ecology” of logistics: if traditional ecology takes care of the natural environment, then guanxi describes the organizational environment, which affects efficiency, distribution of responsibilities, speed of decisions and the ability to act in a complex way. In summary, the study by Kuranovič et al. adds a third dimension to the analysis of sustainable transport—the social architecture of sustainability, which is usually not included in classical quantitative models. In this way, the study not only expands the spectrum of logistics indicators, but also opens up new possibilities for modeling organizational behavior as an important component of sustainability, thus connecting to the broader logic of the UN SDG system, in which social integration, partnerships, trust and cultural sensitivity are treated as equally important as environmental protection or technology.

However, it should be remembered that a sustainable transport system does not exist without the efficient and integrated functioning of logistics nodes—and it is precisely warehousing that is that functional, but often underestimated, logistics segment in which supply accuracy, delivery reliability and customer satisfaction are formed. The article by Batarlienė and Jarašūnienė introduces an important structural breakthrough: it treats warehousing not as a “static resource”, but as a dynamic system managed by quality management principles, which has a huge impact on the sustainability of the entire supply chain.

In this study, the main analytical focus becomes the logic of quality management (QM), based on the principles of Lean and process standardization. One of the essential contributions of the study to the sustainability discourse is that the problem of warehousing quality is associated with the lack of digitalization and automation—not only in the technical sense, but also in the management sense. The Lean logic that the authors apply through the identification of process losses (waste) becomes not only a tool for efficiency, but also a model of sustainability: each eliminated process loss is not only less time or labor, but also a smaller amount of energy, transportation, packaging materials, and carbon footprint. In other words, process quality becomes the equivalent of ecological sustainability.

Moreover, the authors propose a process quality improvement model based on consistent assessment of work stages, integration of information flows and a continuous feedback system. In this model, a warehouse management system (WMS) occupies a special place, which is connected to transport planning tools. Such integration can reduce the number of returns, increase the accuracy of collection, improve inventory balance management and accelerate the exchange of information between the warehouse, carrier and end customer.

Digitalization is not treated superficially here—the authors highlight that it is not enough to have software: employee competence development, organizational involvement, process standardization and management commitment are required. This leads to an important theoretical conclusion: sustainable technological transformation is not only technical—it is structural, institutional and cultural.

After all, it is in warehouses that systematic losses, errors and “invisible” emission sources are most often generated—packaging, re-sorting, non-optimized movement, etc.

This study fits perfectly into the SDG system, especially the following goals:

In addition, it is important to note the practical originality of this article—it is not a conceptual or theoretical model: it is a model based on massive empirical data, and the proposed interventions are practically applicable.

Transport management constantly balances the trade-off between efficiency and resilience, between economic efficiency and systemic reliability. The more global the supply chain, the more influential not only physical factors (cost, distance, time), but also institutional, infrastructural and technological parameters are. The decision-making model proposed by Kabashkin (2023), based on the hierarchy of transport needs and Petri nets, extends classical logistics optimization to a systemic, dynamic and sustainable architecture.

The core of the model is a five-layer hierarchy, developed according to the analogy of Maslow’s psychological needs: each decision level (geographic, economic, institutional-political, infrastructural, technological) acts as a filter that an alternative route must “pass”. Only those routes that meet the requirements of all levels are considered suitable. Such a concept is not only methodologically attractive, but also very close to the concept of systemic sustainability.

In order to process this complex set of interactions, the author uses the formalism of Petri nets. It plays an important role here not as an abstract mathematical scheme, but as a modeling of the realities of transport flows, politics and strategic planning. For example, if institutional conditions (customs, political stability, security) are unfavorable—the route becomes stuck at this level, regardless of whether it is geographically or economically optimal.

The innovativeness of the model also lies in the application methodology: each alternative route passes not only through evaluation filters, but also through dynamic, time-varying transition times (delays) depending on the intensity of the criteria (e.g., the higher the price—the longer the “passage” through the economic layer). In this way, the model acquires the cognitive ability to simulate the behavior of the system in real conditions—not only to find the “best” solution, but also to understand why certain solutions do not work under systemic pressure.

It is important to emphasize that this form of analysis goes beyond the classical MCDM (Multi-Criteria Decision Making) framework. Typically, these models are based on AHP, TOPSIS or other scoring systems, which—although useful—often can not predict the behavior of the system under dynamic conditions. Petri nets allow us to go further: not only evaluation criteria are integrated into the model, but also process sequences, dependencies, intermediate stations, and flow competition. This converts the decision model from an evaluation table into a functional, structurally based transport decision simulation system.

Such a decision model is closely related to the content of the previously discussed articles. It can be applied to the following:

In summary, it can be stated that the decision model proposed by Kabashkin reflects the conceptualization of sustainability not as an “outcome”, but as a process-based, time-evolving decision-making structure that is able to process diverse information, recognize conflicts between goals and generate optimal, but not unique, solutions. This model is not a formula, but an adaptive system that corresponds to the logic of a complex world and fits perfectly into the vision of sustainable transport systems.

After analyzing five methodologically different but strategically close studies, it becomes obvious that their totality creates a new, systemic layer of knowledge that allows us to rethink the very concept of transport sustainability. Each study contributes its semantic basis to the overall transformation architecture: some form a structural framework (network analysis), others introduce social cement (guanxi), still others implement management mechanics (Lean, quality), and still others provide decision-making “intelligence” (Petri net models). However, this entire structure becomes significant only when it is integrated into a unified systemic logical structure of sustainability, characterized by mutual synthesis:

This is a comprehensive, multi-criteria, evolutionary decision model that establishes the paradigm of process sustainability: a decision is not a “choice”, but a process flow through a labyrinth of conditions and constraints.

This set of five studies forms a systemic core of transport sustainability analysis, where infrastructural, social, technological and management components operate as a unified, integrated structure.

The overall contribution of these studies is based on three interrelated conceptual foundations:

The frequency of warehouse employee training or order accuracy are two examples of micro-level decisions that can have macroeconomic and ecological effects, according to a study on lean management and quality improvement (Batarlienė & Jarašūnienė). This illustrates that local efficiency is a necessity for global sustainability, and warehousing is becoming one of the most important sustainability modules in the entire supply chain.