The core problem of a multimodal transportation system is integrating various transportation modes into a cohesive, efficient, and user-friendly network. This study introduces a novel centralized load concentration approach for regions facing geographic challenges. The principal aim is improving multimodal transportation systems by mitigating CO₂ emissions and improving operational efficiency. This will significantly reduce high logistics costs and the environmental impact caused by greenhouse gas emissions, particularly in land transportation, aligning with the global sustainable development goals and offering a promising path towards a more sustainable future. The proposed method implicates direct cargo transportation from its origin to the export ports without passing through intermediate centers. The mathematical model determines the most efficient means of transportation for each route, considering variables such as distance, volume, and type of cargo. Research results indicate that multiple hubs may not be necessary in scenarios with high freight concentration, which could streamline transportation and logistics operations. The modal preferences vary depending on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options in specific circumstances, outperforming road transport. The proposed model shows reductions in logistics costs and CO₂ emissions compared to road-focused scenarios. This study provides an adaptable framework for optimizing multimodal transportation systems in regions with similar geographic and logistical attributes. It offers a versatile solution to various contexts and needs. Lastly, the strategic integration of multiple modes of transportation is fundamental to improving efficiency and sustainability.

This study aims to enhance multimodal transportation systems by mitigating CO2 emissions and enhancing operational efficiency. It introduces a novel centralized cargo concentration approach tailored for regions facing geographical challenges. The method proposes for direct transportation of cargo from its loading origin to export ports, bypassing intermediate hubs. The mathematical model determines the most efficient means of transport for each route, factoring in variables like distance, volume, and cargo type. Research results indicate that, in scenarios with a high concentration of cargo, multiple hubs may not be necessary, which could streamline transportation and logistics operations. Modal preferences vary based on regional dynamics and cargo characteristics, with rail and sea transport emerging as preferable options under specific circumstances, surpassing the efficacy of road transport. The proposed model showcases reductions in both logistics costs and CO2 emissions compared to road-centric scenarios. It underscores the strategic integration of diverse transportation modes as pivotal for enhancing efficiency and sustainability. This study furnishes a framework adaptable to optimizing multimodal transportation systems in regions sharing similar geographical and logistical attributes.

This research presents a comprehensive analysis of sustainable port operations in two countries, utilizing econometric models, including the stochastic frontier and Cobb–Douglas models, to examine critical elements such as the number of empty containers and vessel traffic. These factors, driven by globalization, significantly impact the environmental and social dynamics within port operations. By integrating these models, this study assesses efficiency and sustainability, addressing methodological gaps in existing research, particularly in the quantification and generalization of CO₂ emissions. Findings indicate that, despite differences in port management and operations, CO₂ levels can be estimated using operational variables without the need for complete CO₂ traceability throughout the logistics chain. This study concludes by emphasizing the importance of incorporating international environmental standards into port operations and recommends the use of econometric models as tools for assessing and managing port sustainability effectively.

Background: The Bullwhip and Ripple effects are systemic phenomena that disrupt supply chain performance. However, research often neglects their connection to resilience. This article presents a hybrid literature review examining how both effects are addressed about supply chain resilience, focusing on methodological and conceptual trends. Methods: The review combines thematic analysis of studies from Web of Science and ScienceDirect (2000–2023) with bibliometric trend modeling using Long Short-Term Memory neural networks to detect nonlinear patterns and disciplinary dynamics. Results: While 64.7% of the reviewed works explicitly link the Bullwhip Effect or Ripple Effect to resilience, only 11.7% of those focused on the Bullwhip Effect offer models with clear practical use. A structural break in 2019 marks a notable rise in research connecting these effects to resilience. Nonlinear modeling dominates (88.23%) through network theory and system dynamics. Social, Engineering and Business Sciences drive Bullwhip-related studies, while Economics, Computer Science, and Social Sciences lead Ripple-related research. Business, Energy, and Social Sciences strongly influence the integration of the Ripple Effect into supply chains. A modeling typology is proposed, and neural network techniques uncover key bibliometric patterns. Conclusions: The review highlights limited practical application and calls for more adaptive, integrative research approaches.

This study introduces a new way to predict Supply Chain Resilience (SCR) by combining resilience stages, logistical multi-effects (like Synchronization, Spillover, Bullwhip, Ripple, and Economic effects), and strategic decision-making scenarios. A hierarchical nonlinear state-space model assesses resilience velocity in panel data by integrating structural dynamics, memory effects, and latent exogenous shocks. A Bayesian hierarchical particle filter with partial pooling is used to find the parameters. This method looks at changes at both the unit level and the global level. The framework includes nonlinear relative-change dynamics, an autoregressive memory part, and a latent exogenous variable found through principal component analysis. It also uses methods for updating strong likelihoods and resampling in a way that adapts. Transfer Entropy shows how things are connected in a dynamic way. The model is used to look at the Mexican maritime port system from 2008 to 2023, which includes the COVID-19 pandemic and the global financial crisis. The results indicate that Synchronization serves as the primary resilience driver, Bullwhip functions as an early-warning signal, persistent negative Spillovers occur, and Ripple effects are context-dependent. We use evaluation metrics and visualizations to check how well the model works. Spanish-language metadata / Metadatos en españolTítulo en español:Revelando la dinámica estructural de la resiliencia de las cadenas de suministro: un análisis de panel de alta frecuencia con múltiples efectos Resumen:Este estudio introduce una nueva forma de predecir la resiliencia de las cadenas de suministro mediante la combinación de las etapas de resiliencia, múltiples efectos logísticos —como los efectos de sincronización, desbordamiento, látigo, propagación y económico— y escenarios estratégicos de toma de decisiones. Un modelo jerárquico no lineal de espacio de estados evalúa la velocidad de resiliencia en datos de panel mediante la integración de dinámicas estructurales, efectos de memoria y perturbaciones exógenas latentes. Para estimar los parámetros se utiliza un filtro bayesiano jerárquico de partículas con agrupamiento parcial. Este método analiza los cambios tanto en el nivel de cada unidad como en el nivel global. El marco incorpora dinámicas no lineales de cambio relativo, un componente autorregresivo de memoria y una variable exógena latente identificada mediante análisis de componentes principales. Asimismo, utiliza métodos robustos de actualización de la verosimilitud y procedimientos adaptativos de remuestreo. La entropía de transferencia permite identificar relaciones dinámicas entre las variables. El modelo se aplica al sistema portuario marítimo mexicano durante el periodo 2008–2023, que incluye la pandemia de COVID-19 y la crisis financiera mundial. Los resultados indican que la sincronización actúa como el principal impulsor de la resiliencia, el efecto látigo funciona como una señal de alerta temprana, persisten efectos negativos de desbordamiento y los efectos de propagación dependen del contexto. El desempeño del modelo se evalúa mediante métricas y visualizaciones. Palabras Claves:Resiliencia de las cadenas de suministro; modelos bayesianos jerárquicos; filtrado de partículas; dinámica de la resiliencia; datos de panel; perturbaciones exógenas; no linealidad estructural; entropía de transferencia. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i4.1317Dimensions.Open Alex.

The world has changed drastically in logistical and economic spheres as a result of the COVID-19 pandemic. This pandemic has caused a global crisis in supply-chain structures, creating regional, national and international impacts of unprecedented magnitude. Accordingly, this research develops a methodology to favour the logistics-resilience framework based on regional externalities and technical-efficiency analysis of the 51 US states, applying a Spatial Data Panel model and a Stochastic Frontier model in conjunction with graph theory (Ford–Fulkerson algorithm). The findings indicate that New York, West Virginia and North Dakota are vital external regions to support California’s logistics resilience. We demonstrate that a region with high technical efficiency does not necessarily constitute a key logistics spillover for a target region. This study represents one of the first attempts to optimise and redirect externalities from one region to another using spatial and logistical mechanisms.