FAEP - Sustainable Strategies

The China-Europe Railway Express (CRE) and maritime transport are the twin pillars of the "Belt and Road Initiative" (BRI) and China-Europe logistics. However, they face a cost-carbon emission paradox. This study aims to find the balance point between the environmental and economic benefits of these two transportation methods using a machine learning-driven framework. It takes China-Europe container logistics as the research object and develops a hybrid model integrating XGBoost-LSTM prediction, NSGA-III multi-objective optimization, and reinforcement learning (RL) for dynamic decision-making. Data on the costs and carbon emissions of the CRE and maritime transport from 2018 to 2023 were collected and preprocessed with Generative Adversarial Networks (GANs) to address missing values. The model quantifies the carbon-cost trade-off, identifies Pareto-optimal transport portfolios, and simulates policy impacts under scenarios like carbon tax adjustments. The results provide actionable insights for optimizing China-Europe logistics toward a greener and more cost-effective direction. Practical policy suggestions include adaptive subsidies and carbon pricing mechanisms.

This study presents a new Mixed Integer Nonlinear Programming (MINLP) model assessing shipowners optimal strategies in response to carbon pricing, balancing operational adjustments (such as speed and fleet deployment) with technical measures (like energy-saving technologies and alternative fuels). Unlike previous research that mostly treats operational and technical strategies separately, our approach integrates these strategies within a cost-minimizing framework. We apply the model to a liner shipping company operating six Asia-Europe routes We simulate scenarios ranging from baseline operations to advanced decarbonization measures. Our results show that when carbon costs increase, shipowners start to make larger investments in energy-saving technologies and switch to alternative fuels like LNG and hydrogen, producing zero emissions at higher price points. We observe that both technical and operational measures play a crucial role in sustaining trade and limiting the need for fleet expansion, while cargo flow is redirected along the most carbon-efficient routes. This study offers insights into how the marine industry may decarbonize in a cost-effective way by analysing the connection between technological innovation and operational efficiency.

Inland waterway transport (IWT) is increasingly recognized as a cleaner, more efficient alternative toroad transport for freight movement. However, the successful adoption of zero-emission fuels-particularly hydrogen and battery power-depending on the strategic location and capacity of bunkeringand charging stations. This extended abstract presents a multi-stage framework that combinessimulation and mixed-integer optimization to identify where and how these stations should bedeployed. First, a simulation model estimates the fuel consumption of vessels under varied waterwayconditions, vessel dimensions, and hydrodynamic influences. Next, an optimization module, modelledwithin the supply chain, aims to minimize capital and operating expenses while ensuring sufficientfuel availability. Strategically placing multi-fuel stations in high-demand locations reducesinfrastructure redundancy and ensures flexible operations. This study underlines the critical role ofwell-planned bunkering infrastructures and highlights the potential for future expansions in zero-emissionvessel networks.

Reducing product carbon footprints has gained prominence in global discussions, with over 110 countries pledging to achieve net-zero emissions by 2050. China aims for a 60% to 65% reduction in carbon intensity by 2030 relative to 2005 levels, prompting companies to prioritize carbon footprint mapping and mitigation. The complexity of product life cycles complicates the management of direct and indirect carbon emissions, particularly Scope 3 emissions, which include all indirect emissions across the product value chain-from upstream procurement to downstream delivery. Current industry practices predominantly address direct emissions, highlighting a significant gap in frameworks for Scope 3 analysis and reduction, especially for products with intricate components like automotive and electronics. This study introduces a novel Scope 3 carbon neutrality emission model utilizing a recurrent neural network algorithm to minimize indirect emissions during component transportation within manufacturing supply chains. The model incorporates various decision factors, including shipment volume, slot size, fuel mix, routing network, transportation mode, and reliability. Through advanced simulation techniques, it aims to substantially reduce indirect emissions in Hong Kong, the Greater Bay Area, and China. Furthermore, the findings will be integrated into educational modules to enhance students' comprehension of sustainable supply chains and advanced simulation methodologies.

Norway aims to reduce its greenhouse gas emissions by at least 50 to 55 percent relative to 1990 levels by 2030, and to become a low-emission society by 2050. It consequently seeks a leading role in adoption of green vessel technologies. Currently, 8.4 of Norway's greenhouse gas emissions stem from domestic maritime transports, and the fishing industry is among the most significant contributors. This paper proposes an optimization framework for identifying cost-effective trajectories for fishing fleet renewal subject to targets for greenhouse gas emission reductions. A novel combination of convex regression and mixed-integer programming is proposed to identify a cost-effective mix of technical and operational measures for reducing greenhouse gas emissions from the heterogenous fleet. Scenarios for emission reduction towards 2050 are analyzed, resulting in abatement cost estimates in the range of 200 ECU/ton of greenhouse gas emissions removed. Cost minimization suggests a limited phase-in of alternative fuels in the intermediate term, while hydrogen-derivatives are identified as cost efficient in energy intensive vessel groups when approaching the low-emission society in 2050. Access to drop-in fuels hampers the adoption of alternative fuels and associated bunkering infrastructures.