Current Issue : October-December Volume : 2026 Issue Number : 4 Articles : 5 Articles
Driven by the global carbon neutrality target and China’s dual-carbon strategic deployment, the transportation industry has become a key field for energy conservation and emission reduction. As a low-carbon, high-capacity, and stable transportation mode, railway undertakes more than 70% of domestic bulk cargo transportation including coal, iron ore and grain. However, traditional railway bulk cargo transportation organization still suffers from unreasonable train marshalling, low wagon loading rate, high empty running ratio and unscientific operation scheduling, resulting in redundant energy consumption and excessive carbon emissions, which restricts the green upgrading of railway freight systems. This paper takes the actual operation scenarios of domestic railway bulk cargo transportation as the research object, constructs a complete railway freight carbon emission accounting model based on IPCC national greenhouse gas inventory guidelines and railway industry actual operation parameters, and establishes a multi-objective transportation organization optimization model aiming at minimum carbon emission, minimum transportation cost and maximum operation efficiency. A hybrid genetic algorithm integrating particle swarm optimization is adopted to solve the nonlinear constrained optimization problem. All statistical data spanning 2020-2025 are sourced from China State Railway Group freight big data platform covering five core bulk trunk lines; raw data adopts daily statistical granularity with total valid sample volume of 192,600 groups, abnormal data from temporary line overhaul and emergency suspension is eliminated via 3σ outlier screening during preprocessing. Based on the authentic statistical operation data of China State Railway Group from 2020 to 2025, empirical verification and scheme comparison are carried out. The research results show that the optimized organization scheme significantly improves the overall operation level of bulk railway freight. After optimization, the total carbon emission of bulk cargotransportation is reduced by 12.5%, the comprehensive transportation cost is decreased by 9.5%, the average wagon loading rate is increased by 5.1 percentage points, and the empty driving rate is reduced to 9.2%. The proposed optimization strategy can effectively solve the low-carbon operation bottleneck of traditional railway bulk freight, provide practical technical support for railway green transportation organization scheduling, and offer a reference for lowcarbon transformation of comprehensive freight transportation systems....
Global supply chains account for approximately 60% of total carbon emissions worldwide, yet fragmented information across multiple actors and divergent interest objectives render systemic emission reductions unattainable through traditional management approaches. This paper delineates four pathways through which artificial intelligence (AI) drives the green and low-carbon transition of supply chains: intelligent demand sensing enhances supply-demand matching, curbing superfluous emissions at the source; intelligent logistics scheduling optimizes the trade-off among cost, delivery time, and energy consumption; chainwide carbon footprint traceability addresses Scope 3 emissions; and supplier collaboration and empowerment promote green and low-carbon transformation. It also identifies structural limitations inherent in these pathways, including fragmented data governance, the opacity of algorithmic decision-making rationales, organizational incentive misalignments, and environmental rebound risks arising from the computing power consumption of AI itself. Accordingly, the paper proposes optimization directions such as constructing a cross-organizational carbon data governance framework, developing explainable carbon decision intelligence models, designing collaborative carbon-reduction incentive mechanisms that balance equity and efficiency, and establishing a full-life-cycle carbon performance evaluation system. Embedding institutional design into technical processes enables AI to evolve from an efficiency tool into an institutional infrastructure for the green governance of supply chains....
Location selection of logistics distribution centers is a key decision-making issue in the field of logistics engineering and management, which directly affects logistics costs, distribution efficiency and service quality. Taking the regional logistics distribution center location of a chain retail enterprise as the research object, this paper applies the Analytic Hierarchy Process (AHP) to construct a multi-criteria decision-making model. By clarifying the location objectives and sorting out the influencing factors, an evaluation system including 4 first-level indicators (economic cost, geographical location, infrastructure, policy environment) and 13 second-level indicators is established. Weights are determined through pairwise comparison matrices aggregated by expert geometric mean, and finally three candidate location schemes are ranked and optimized. The research results show that economic cost and geographical location are the core influencing factors for logistics distribution center location, accounting for 35.2% and 28.7% of the weight respectively, and the selected optimal scheme has obvious advantages in comprehensive benefits. Sensitivity analysis verifies the robustness of the ranking result. This study provides a scientific and feasible decision-making method for enterprises’ logistics distribution center location, and also offers practical references for the application of AHP in the field of logistics management....
Global supply chains have become increasingly vulnerable to disruptions caused by pandemics, geopolitical conflicts, and economic instabilities. Emerging economies face unique structural challenges including inadequate infrastructure, regulatory uncertainty, and limited digital maturity. Digital technologies such as artificial intelligence (AI), blockchain, big data analytics (BDA), the Internet of Things (IoT), and cloud computing have been identified as potential enablers of supply chain resilience. However, much of the corresponding literature focuses on the developed economy context. This study is a systematic review of 22 peer-reviewed articles published between 2015 and 2025 to analyze the effect of digital technologies on supply chain resilience of emerging economies. Employing a method of content analysis from Scopus, Web of Science, Google Scholar and Emerald Insight, the.............
Background: Rapid adoption of artificial intelligence (AI) in logistics enhances operational efficiency and firm performance; however, empirical evidence on its capability‑driven impact remains limited, particularly in Saudi Arabia’s e‑commerce sector. This study’s purpose is to examine the influence of AI on logistics firm performance through the mediation role of supply chain consistency and logistics capabilities. Methods: A quantitative study was conducted and data were collected using a convenience sampling technique from 275 employees working in the Saudi Arabian logistics firms. Partial Least Squares Structural Equation Modeling was used to perform data analysis. Results: The study findings indicated that AI usage has significant and positive influence on supply chain consistency (β = 0.290) and logistics capabilities (β = 0.303). Furthermore, supply chain consistency (β = 0.115) and logistics capabilities (β = 0.171) play mediating role between AI usage and firm performance. The research model exhibits substantial predictive capability, explaining 74.6% (R2 = 0.746) of the variance in firm performance, while AI usage explains a smaller portion of the variance in supply chain consistency 8.4% (R2 = 0.084) and logistics capabilities 9.2% (R2 = 0.092). Conclusions: The findings demonstrate that AI‑based logistics operations provide extensive support to streamline operations and reduce costs....
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