The increasing cost and environmental impact of last-mile home delivery have intensified interest in alternative urban logistics configurations. Mobile warehouses (MWs), which act as temporary urban micro-depots enabling two-echelon delivery, offer potential reductions in last-mile travel distance and support the use of low-emission vehicles. However, their economic viability remains insufficiently understood. This study evaluates the cost efficiency of MW-based last-mile delivery systems under alternative vehicle technologies and facility location strategies. A unified analytical framework integrating facility location optimization, network-based routing, and cost-per-parcel modeling is developed. Two classical location models such as k-center and p-median, used to solve distance- and time-based objectives, are applied to determine optimal MW placements. The resulting configurations are evaluated for three secondary delivery vehicle technologies: electric cargo bikes, electric vans, and diesel vans. The analysis is conducted using real-world delivery demand data from two mid-sized Swedish cities. Results show that MW deployment reduces delivery distances and times but increases cost per parcel under low-demand conditions due to additional operational overhead. Electric vans achieve the lowest cost in low MW-density configurations, while electric cargo bikes become competitive only at higher MW densities and under efficiency-oriented location strategies. Sensitivity analysis reveals that demand density and MW utilization are the dominant drivers of cost competitiveness, whereas energy price variation has a limited effect. The findings indicate that MW-based delivery systems are economically viable primarily in high-density, spatially clustered demand environments, offering practical guidance for logistics operators and urban freight planners. The results support evidence-based decisions on when MW deployment, vehicle electrification, and multi-stop consolidation strategies are economically justified in urban logistics planning.
This research conducted two vignette-based experiments designed to examine the potential role of social norms to motivate engagement with car sharing service (CSS) adoption in Spain. The analysis answered two research questions: a) to what extent do descriptive and injunctive social norms promote the willingness to adopt CSS, and b) what is the influence of injunctive norms on the willingness to use of sustainable mobility in general (i.e. walking, cycling and public transportation), including or excluding CCS? The initial hypothesis was based on the assumption that social norms can have a positive effect on the likelihood of adopting CSS and sustainable mobility choices. Results from the first experiment do not support this hypothesis, as they indicate that the use of social norms does not significantly influence the willingness to adopt CSS, suggesting that private car users may be less responsive to brief normative cues. On the contrary, the results of the second experiment suggest that strategies to encourage the adoption of sustainable transport options and CSS should be integrated into a broader and more cohesive framework of transport alternatives. Regression results show that several socio-demographic characteristics, mobility patterns, and environmental attitudes influence the willingness to adopt sustainable transport options.
As global connectivity hubs, airports increasingly leverage service quality as a primary strategic differentiator. However, evaluating airport performance is inherently complex due to the multi-dimensional, uncertain, and subjective nature of service systems. This paper proposes a hybrid multi-criteria decision-making (MCDM) framework that addresses these complexities to provide more reliable airport service performance evaluations and actionable insights for decision-makers. The proposed framework effectively addresses expert hesitation and heterogeneous information, enabling more reliable decision-making under uncertainty by Sugeno-Weber Fermatean Fuzzy (SW-FF) environment. A dual-weighting approach is introduced, synthesizing subjective expert insights via Ranking Comparison (RANCOM) with objective, data-driven analysis through Logarithmic Decompositions of Criteria Importance (LODECI) method. Furthermore, the study develops a Double-Normalization (DN) based extension of the Alternative Prioritization and Assessment System (ALPAS) method to improve ranking stability and normalization strength. The framework is validated through an empirical assessment of twenty Skytrax 5-Star airports. Results identify Immigration Service, Terminal Facilities, and Transfer Services as the most critical performance drivers, with Tokyo Narita, Bahrain International, and Chubu Centrair emerging as the top-tier performers. Extensive sensitivity and comparative analyses confirm the framework's reliability, offering a powerful decision-support tool for airport operators and policymakers aiming to optimize competitive positioning in a volatile aviation market.
The rapid but uneven transition toward electromobility across Europe is fundamentally reshaping modern transport and energy policies. However, the lack of a unified implementation approach under the AFIR guidelines has outpaced the analytical tools available to assess national progress, with existing comparisons often relying on single-indicator metrics that distort relative market standing. To overcome these analytical limitations, this article develops a multidimensional index DEVCS for 33 European countries, utilising the latest 2025 data from the European Alternative Fuels Observatory (EAFO). The methodological framework integrates 5 core indicators called EV adoption, infrastructure availability, spatial density, charging capacity and transition depth, through objective entropy weighting and the TOPSIS technique. Subsequently, hierarchical clustering is applied to map the structural topology of national ecosystems. When charging density is normalised by administrative land area, as is conventional, the indicator largely captures uninhabited territory, and entropy weighting inflates it to nearly half the index. Correcting the denominator to built-up land rebalances the weights and changes the leaders. Iceland, Norway and Denmark head the corrected ranking, the Netherlands falls from 1st to 4th. A stable leading group of 7 countries holds across weighting and clustering methods. GDP per capita and urbanisation are significant positive correlates, whilst raw population density is not. Testing provision against AFIR proves revealing: 32 of 33 countries already meet the fleet-based target, and those that clear it most comfortably have the least electromobility, because the requirement scales with fleet size. In most of Europe the binding constraint is no longer infrastructure but demand.
Air transport constitutes a complex sector to address from a sustainability perspective because of its high technological requirements and the significant volume of emissions associated with its activity. Since the introduction of the EU Taxonomy, aviation has been recognised as a transition activity, reflecting its potential role in the progression towards more sustainable business models. This study examines whether EU Taxonomy eligibility and sustainability performance are associated with systematic market risk in the airline industry. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and 156 pooled firm-year observations from 39 listed airlines over 2021–2024, this study examines the direct and indirect associations among EU Taxonomy eligibility, measured through eligible CapEx, sustainability performance, and systematic market risk, proxied by market beta. In addition, the moderating role of ESG Controversies Score in the relationship between sustainability performance and systematic market risk is analysed, while controlling for firm size. The results show that greater eligible CapEx and stronger sustainability performance are associated with lower systematic market risk. Sustainability performance partially mediates the association between EU Taxonomy eligibility and market beta. In addition, the ESG Controversies Score is negatively associated with systematic market risk and significantly moderates the sustainability performance-risk relationship. The negative interaction indicates that sustainability performance is more strongly associated with lower market beta when firms have a more favourable controversy profile and lower exposure to negative ESG events.
The global rise in electric vehicle (EV) adoption has prompted policymakers to investigate user preferences and barriers to EV uptake. While EV usage in Türkiye has grown steadily, this trend remains below that of many developed countries. Understanding the factors behind this lag is essential for accelerating the transition to sustainable transportation. The study contributes to the literature by presenting one of the first exploratory applications of ARM to EV adoption intention in Türkiye, introducing a structured segmentation of participants to examine adoption, hesitation, and refusal pathways separately, and demonstrating that EV preferences are associated with factor combinations rather than isolated variables. To improve transparency in rule interpretation, a stability-based rule selection procedure is applied using multiple support-confidence scenarios and objective interestingness measures. The findings show that non-vehicle users who would prefer EVs in the future are mainly characterized by environmental awareness, perceived economic benefits, technological appeal, and certain demographic patterns. Participants who might prefer EVs display a conditional adoption profile shaped by incentives, charging infrastructure, charging duration, reliability, workplace charging, resale value, range, and price. In contrast, participants who would not prefer EVs show co-occurring concerns related to charging infrastructure, range adequacy, charging duration, and EV reliability.
Decarbonization commitments, net-zero targets, and increasingly stringent environmental regulations are reshaping shipowners' newbuilding decisions. In response to these changes, the strategic selection of Green Shipbuilding Yards has become an important business decision and a key means of supporting shipowners' long-term sustainable operations through environmentally compliant newbuilding projects. Previous studies have mainly focused on traditional selection criteria, while few have integrated green, technical, financial, and sustainable management considerations into a comprehensive evaluation framework. This study develops a framework and employs the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) method to reveal the causal relationships among interdependent evaluation dimensions. The results show that after-sales warranty and post-delivery support, contract compliance and timely delivery, and the construction process and artistry are the most important selection criteria. In contrast, green quality and credibility assurance are identified as an effect dimension. This study contributes to the literature by developing a comprehensive evaluation framework for Green Shipbuilding Yard selection and by demonstrating the applicability of Fuzzy DEMATEL to analyze causal interdependencies among evaluation dimensions. The proposed framework supports shipowners in selecting sustainability-oriented Green Shipbuilding Yards and helps Green Shipbuilding Yards identify priorities for capability improvement to enhance competitiveness.
Electric vehicles (EVs) play a crucial role in energy transition and addressing climate change. However, the current share of new EV sales still falls far short of the target. Based on panel data from 49 International Energy Agency (IEA) member countries over the period 2010–2022, this study treats the Global Electric Vehicle Pilot City Programme (EVPCP), as a quasi-natural experiment and employs a staggered intensity difference-in-differences (DID) model with a treatment intensity measured as the population share of pilot cities to examine its effectiveness. The findings are as follows. First, the EVPCP significantly boosts EV sales in participating countries. Second, the EVPCP promotes EV sales by facilitating policy support, charging infrastructure deployment, and technology R&D and cooperation, which in turn accelerates EV adoption. Third, the positive impact of the EVPCP varies across countries and vehicle types. In high-exposure countries, the programme contributes substantially to sales growth in privately consumed vehicle segments, including cars, trucks, and vans, whereas in low-exposure countries, the effect is concentrated on buses. Meanwhile, the policy effect is more pronounced in Northern countries and countries with lower population density. Additionally, the EVPCP can further optimize the energy consumption structure and generate a superimposed effect when combined with smart grid policies. This study not only provides evidence for the effectiveness of the EVPCP but also offers insights for accelerating the global promotion of EVs, thus holding policy implications.
The transformative potential of self-driving vehicles (SDVs) to reshape transportation systems is undeniable, yet their adoption in developing countries such as Ghana presents unique challenges. Existing literature predominantly focuses on developed nations, leaving gaps in understanding the infrastructural, regulatory, and socio-economic barriers in less-industrialized contexts. This study addresses these gaps by evaluating the risks associated with SDV implementation in Ghana and prioritizing implementation models to enhance SDV adoption. The study employed an integrated sine-entropy weighted assignment (SEWA) and Additive Ratio Assessments (ARAS) Multi-Criteria Decision-Making (MCDM) methods. Specifically, the SEWA method is used to explore risk factors, and the ARAS method is used to rank implementation models. We found that the three (3) topmost risk factors are (i) inadequate infrastructure for vehicle-to-road communication, (ii) regulatory and legal uncertainties, and (iii) limited public acceptance and ethical concerns. The findings also reveal a clear ranking of the models, with government-led initiatives emerging as the most effective, followed by Public-Private Partnerships (PPPs), private sector-driven, international development and aid-supported models, and community-based approaches. From a policy perspective, the study emphasizes the need for progressive regulatory frameworks, including pilot zones for SDV testing, to refine policies before national-scale deployment.
Airports are often benchmarked using common service performance indicators, yet airports of different sizes operate under different spatial, operational, and passenger-experience conditions. This study examines whether the effects of airport service components on overall passenger satisfaction differ between small and large airports, and whether these differential impacts align with observable performance gaps. Using standardized departing-passenger satisfaction survey data from four commercial airports in Thailand, the analysis compares 6527 passenger responses across two small and two large airports. A two-step analytical process is applied. First, differential impact analysis identifies service components whose effects on overall satisfaction differ significantly by airport-size group. Second, performance-gap analysis is integrated with the differential-impact results to classify size-sensitive improvement priorities. The findings show that movement- and process-related components, including walking ease, security waiting time, border control waiting time, and ease of security screening, have stronger satisfaction effects in large airports. In contrast, charging availability has a stronger satisfaction effect in small airports. Performance gaps do not fully align with satisfaction impacts, indicating that low service scores alone do not necessarily identify the most important improvement priorities. The study contributes a size-sensitive approach to airport service benchmarking and passenger-experience prioritization.
As the coordinated development of smart city infrastructure and intelligent connected vehicles (hereinafter referred to as the “Dual-Intelligence” pilot) becomes a national strategic priority, exploring the empowering effects of vehicle-road cloud integration on urban traffic governance is of great significance. Based on the quasi-natural experiment of the “Dual-Intelligence” pilot policy, this paper utilizes panel data of Chinese cities at the prefecture level and above from 2016 to 2023, employing the Difference-in-Differences (DID) method to empirically examine the impact of vehicle-road cloud integration on urban traffic congestion and its underlying mechanisms. The study finds that the implementation of the “Dual-Intelligence” pilot policy significantly reduced urban traffic congestion levels, a conclusion that remains robust after a series of tests, including the instrumental variable approach. Mechanism analysis indicates that the pilot policy alleviates congestion primarily through three pathways: first, optimizing traffic flow efficiency by enhancing road network throughput via real-time dispatching; second, improving the supply capacity and service readiness of public and shared mobility, thereby creating favorable conditions for a potential modal shift; and third, promoting the synergetic evolution of technology and safety, which improves driving safety while reducing non-recurring congestion caused by accidents. Heterogeneity analysis further reveals that the mitigating effect of vehicle-road cloud integration on congestion is more pronounced in cities with high degrees of terrain undulation and superior digital infrastructure. Further analysis shows that the policy induces a significant “substitution effect” on the demand side; specifically, while maintaining a dynamic balance in vehicle scale, it significantly drives the structural substitution of traditional fuel vehicles with new energy vehicles (NEVs) through technological adaptation and environmental incentives, thereby increasing NEV penetration. This conclusion not only provides localized evidence for China to deepen its “Dual-Intelligence” strategy but also offers a universally relevant “China Solution” for emerging economies worldwide to leapfrog the “traffic governance trap” and achieve the United Nations Sustainable Development Goals through the dual-drive of “technology and institutions” during digital transformation.
