http://repositorio.lnec.pt:8080/jspui/handle/123456789/58 2026-03-06T10:12:53Z 2026-03-06T10:12:53Z Arsénio, E. Aparício, J. Henriques, R. Dias, G. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018869 2025-11-27T12:23:55Z 2025-11-03T00:00:00Z

Assessing the co-evolution of intermodal freight transport research and patenting technology trends for advancing green and intelligent logistics Arsénio, E.; Aparício, J.; Henriques, R.; Dias, G. Green and intelligent freight logistics play a crucial role in driving sustainable development and are increasingly recognised as innovations within Logistics 5.0. This research examines the dynamics of knowledge transfer between academia and industry, focusing on intermodal transport and sustainability. By analyzing academic publications from Scopus and patents from PATENTSCOPE (2014–2024), this research employs advanced methodologies, including transformer-based keyword extraction and multilayer network analysis, to investigate indicators of knowledge transfer. Findings reveal distinct phases of technological innovation, including acceleration driven by AI, IoT, and blockchain technologies, followed by stabilization and consolidation in logistical operations. A patent surge after 2018 highlights industry-led advancements, contrasting with academia's focus on theoretical sustainability frameworks. However, cross-layer analyses highlight the declining industry adoption of academic findings, revealing gaps in the effective transfer of knowledge. The integration of mature innovations into logistical practices reflects the emergence of a consolidation phase. The findings underline the criticality of aligning academic discoveries with industrial applications, fostering mutual collaboration to drive the evolution of green logistics. By uncovering the dynamics underpinning sustainable innovation, the research contributes to the discourse on the twin transition towards greener and more intelligent freight logistics, offering pathways to strengthen academia-industry collaborations.

2025-11-03T00:00:00Z Dais, S. Stamelou, A. Sobrino, N. Vassallo, J. Dias, G. Arsénio, E. Mulhern, E. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018804 2025-11-27T12:21:00Z 2025-03-25T00:00:00Z

Sustainability maturity model Dais, S.; Stamelou, A.; Sobrino, N.; Vassallo, J.; Dias, G.; Arsénio, E.; Mulhern, E. ADMIRAL seeks to develop and pilot AI-driven solutions for managing logistics supply chains to reduce transport and logistics emissions and to increase transparency, resilience, and stakeholder cooperation. This deliverable reports the results of the Task 2.4 - Development of factors and KPIs to measure sustainability - carried out in the project’s Work Package 2 - Sustainable Development of logistics & transport. The primary aim of Deliverable 2.4 is to propose a sustainability maturity model by identifying and proposing reference Key Performance Indicators (KPIs) to measure sustainability, adapting the STAR model to estimate the overall sustainability rating of logistics solutions, and determining the probability of sustainability noncompliance risks in various activities. In addition, Deliverable 2.4 analyses the requirements and proposes the specifications for the validation of a green port certification. To achieve its aims, the report elaborates a multifaceted approach by mixing methodological tools and analyses, such as desk research, expert validation (workshops, online consultation, questionnaires), the STAR model methodology for evaluating the sustainability of ADMIRAL logistics solutions and the Bayesian Network analysis for the sustainability risk noncompliance of the AM. It also deploys an in-depth analysis of the regulatory framework, to correlate selected KPIs with EU regulations. This ensures that the ADMIRAL project complies with policies that enhance efficiency, safety, and environmental impact.

2025-03-25T00:00:00Z Sobrino, N. Vassallo, J. Arsénio, E. Cerqueira, S. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018803 2025-11-27T12:20:55Z 2025-06-18T00:00:00Z

Exploring the potential of the multimodal low-carbon freight corridor Port of Sines - Madrid. Sobrino, N.; Vassallo, J.; Arsénio, E.; Cerqueira, S. Freight transport contributes significantly to greenhouse gas (GHG) emissions in Europe, primarily due to the reliance on fossil fuels and road transport. With increasingly interconnected supply chains and rising cargo volumes, direct (Scope 1) and indirect (Scopes 2 and 3) GHG emissions are expected to increase unless proactive and effective measures are implemented. European transport policy has emphasized a shift to eco-friendly transport modes, such as rail and inland waterways, to reduce the dominance of road transport and consequently mitigate impacts on climate change. Within the framework of the ADMIRAL Horizon Europe project, this study conducts a detailed analysis of the multimodal freight corridor between the Port of Sines (Portugal) and Madrid (Spain), part of the Atlantic corridor promoted by the European TEN-T initiative. The study characterizes the existing and future rail and road infrastructure of the Sines-Madrid corridor, and analyses the cargo moved between Sines and Madrid by transport mode, estimating the share of the modal freight that the multimodal corridor could potentially capture and evaluating the reduction of GHG emissions. Additionally, feedback from key stakeholders was collected in a workshop with the aim of exploring their interests in this multimodal solution. The results of the analysis indicate that investment in infrastructure and interoperability is crucial for the viability and success of the service, particularly with regard to the possible development of rail motorways. The traffic analysis suggests that approximately 10% to 30% of current cargo could be transferred to rail, leading to significant reductions in emissions. This work enhances understanding of the sustainability challenges and the extended traffic-related changes necessary to greening transport within the Sines-Madrid corridor and its hinterland.

2025-06-18T00:00:00Z Antunes, J. P. Arsénio, E. Henriques, R. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018802 2025-11-27T13:33:59Z 2025-06-18T00:00:00Z

Data-centric models for the sustainable development of the multimodal Sines-Madrid transport corridor. Antunes, J. P.; Arsénio, E.; Henriques, R. The multimodal freight transport planning on strategic transnational corridors often neglects critical sustainability criteria. This observation is corroborated by the generalized lack of optimization principles for the carbon-aware allocation of transport modes. This work introduces a prospective study of freight transport along the Sines-Madrid multimodal transport corridor, proposing data-centric models to guide its sustainable growth. The Sines-Madrid corridor represents a critical section in the Trans-European Transport Network Atlantic Rail Freight Corridor. The research explores road-rail transport scenarios until 2030 by: i) consolidating data provided by the Port of Sines and other institutional statistical sources, such as Eurostat, for modelling freight transport.; ii) developing uncertainty-aware time series models to analyze freight trends and forecast demand for road and rail modes in the corridor; and iii) conducting a sensitive analysis of various scenarios for freight transport, namely increasing rail-to-road split ratios to reduce carbon emissions, and their potential impacts. The acquired results from the proposed scenario-based modelling offer stakeholders insights to promote sustainable freight transport strategies. The 2030 horizon is selected to align with the European Union’s (EU) climate and transport goals. The explored scenarios account for the impact of road and rail modal split, as well as road fleet configurations, on emissions and costs. These findings, conducted in the context of the Advanced Multimodal Marketplace for Low Emission and Energy Transportation (ADMIRAL) project, provide a blueprint for similar corridors, guiding EU policy and investment toward sustainable transport solutions.

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