Development of a carbon neutrality model for optimising indirect emissions from components in a multilevel bill of materials across transportation operations in product life cycle

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 Abstract Status : Accepted Submission Type : Extended Abstractbe integrated into educational modules to enhance students' comprehension of sustainable supply chains and advanced simulation methodologies.